diff --git a/opt_prime/examples/pd/STREAMING.md b/opt_prime/examples/pd/STREAMING.md
new file mode 100644
index 00000000..9ad565f5
--- /dev/null
+++ b/opt_prime/examples/pd/STREAMING.md
@@ -0,0 +1,81 @@
+# Experimental RNGD layer streaming
+
+`serve_batched.py --pd-prefill-stream-layers` enables validated partial KV reception
+with a Torch or ATOM decoder. The subprocess must advertise progress support.
+ATOM imports only into a reserved lane. B1 next-request prefetch may compute ahead,
+but its partial data waits until the current decode releases the destination.
+Multi-lane hybrid caches defer layer imports until final decode admission because
+idle lanes may still write their local cache. SDK write failures poison the owner;
+invalid source geometry fails only the request. Incomplete prompts never enter
+the reusable prefix index.
+
+## Code review map
+
+This path uses same-host shared memory for KV payloads and subprocess pipe messages
+for progress descriptors. The measured producer is the research factory engine
+listed below. It uses the existing vendor SDK; this change does not expose the
+closed native furiosa-llm engine's KV protocol or establish cross-host streaming.
+
+| Responsibility | Code | Review invariant |
+|---|---|---|
+| Source export | [prefill_server.py](../../opt_prime/accel/pd/prefill_server.py), [factories](streaming_factories.py) | Copy each layer/group to SHM before notification; preserve layer arithmetic and head. |
+| Transport and coverage | [pd_batching.py](../../opt_prime/accel/pd/pd_batching.py), [layer_progress.py](../../opt_prime/accel/pd/layer_progress.py) | Validate request IDs, nonoverlapping spans and complete coverage; serial writers keep published storage stable. |
+| Torch destination | [paged.py](../../opt_prime/accel/pd/paged.py), [PDBatcher](../../opt_prime/accel/pd/pd_batching.py) | Map dense layers to cache groups, fence destination copies before page reuse, admit only complete KV. |
+| ATOM destination | [batching.py](../../opt_prime/accel/platforms/atom/batching.py) | Reserve the lane before import; protect active B1/hybrid caches; never publish an incomplete prefix. |
+| Serving switch | [serve_batched.py](serve_batched.py) | Explicit opt-in and a compatible subprocess producer; whole transfer remains the default. |
+
+Per-layer source export is separate from destination notification timing. The
+three-mode GPU control retains per-layer source/SHM work while delaying progress
+notifications until source logits return. Much of the measured Qwen/KONI gain
+persists under that control; early notification adds a smaller benefit in the
+recorded workload. The control also retains per-layer destination import and
+allows cross-request overlap, so it cannot assign the remaining gain to source
+assembly alone. See the [control summary](http_matrix/evidence265/stream265-control-summary.json)
+and experiment record for samples and limits.
+
+The three factories in `streaming_factories.py` cover the four measured models:
+
+| Factory | Model | Existing producer |
+|---|---|---|
+| `streaming_factories:qwen` | Qwen3-0.6B, Qwen3-4B | `long_width_variant.make_prefill` |
+| `streaming_factories:gemma` | Gemma3-1B | `gemma_prefill.Prefill` |
+| `streaming_factories:koni` | KONI-Llama3.1-8B | `koni_prefill.Prefill` |
+
+Set the producer's `PYTHONPATH` to include this package, `examples/pd`, and the
+existing `serving_matrix` and `qwen3_rngd` example directories from aicomp commit
+`04dab7cb6961103f9404c99e9fcbcaadac14b098`, plus the compatible `rngd_pd` package.
+These are explicit research dependencies; the adapters do not bundle the compiler
+or model implementations. Exact measured source hashes are in evidence265.
+
+The producer arguments are:
+
+```text
+--engine factory --engine-factory streaming_factories:qwen --width 128 --chip 0
+```
+
+Pass those through `--pd-prefill-args`, choose the corresponding factory, and use
+`--pd-prefill-python` for the isolated Furiosa interpreter/registration wrapper.
+Use local safetensors model checkpoints. Factory width is one compiled width and
+must fit `--max-context`; the model-specific constructor also checks supported
+widths and geometry. KONI's validated partitioned producer requires width512 or
+larger in multiples of512.
+
+Keep the same factory and omit only `--pd-prefill-stream-layers` for the whole-KV
+control. That path calls the original producer's `prefill` method. Streaming
+preserves its layer arithmetic and final head but exports each finished layer or
+group before the next one runs. Published spans are copied to shared memory before
+notification and must remain immutable until the serial producer finishes the
+request. Decoder cache lengths and reusable prefix state are published only after
+all requested suffix spans arrive and destination copies complete.
+
+This is blocking per-group CPU export followed by incremental decoder import.
+It can allow decoder import during later source computation, but does not imply
+asynchronous RNGD download, device DMA overlap, or a serving speedup. Hardware
+source parity has been checked at widths128/512 and8192. Recorded GPU timing
+improvements apply only to the measured c2/fixed32, c4/fixed128 and c16/fixed128 workloads (c16 queues behind four execution slots). Four-model ATOM timing
+and same-route parity are also verified for B1/two queued2K/8K prompts, fixed32
+outputs, and prefetch off/on. Actual ATOM streaming faults pass all four models: cancellation/reuse after partial import, source death before admission, and prefetched source death preserving an active512-token decode. The retained16/512-token arrays match same-route references; see `http_matrix/experiments/2026-10-24-stream265-atom-faults.md`.
+Notification-timing controls show that total improvement is not solely the effect
+of early notification; source/host path changes and cross-request overlap remain. None establishes cross-backend
+numerical equivalence. See the experiment record for exact cases,
+failed attempts and the current verification scope.
diff --git a/opt_prime/examples/pd/http_matrix/.research/events.jsonl b/opt_prime/examples/pd/http_matrix/.research/events.jsonl
index 32c711bd..42c6a658 100644
--- a/opt_prime/examples/pd/http_matrix/.research/events.jsonl
+++ b/opt_prime/examples/pd/http_matrix/.research/events.jsonl
@@ -4,3 +4,28 @@
 {"id": "e4993823527144b5a14e6441b1cd5c01", "ts": "2026-10-23T21:29:56+09:00", "task": "Tokenizer EOS in batched serving (aicomp #229)", "result": "Published production EOS fix and real KONI HTTP evidence in draft PR230 (e5fa087). Final factory/streaming/sampling/batched suite22passed; hardware checker5/5 cases passed; changed files sensitive-clean,4unchanged baseline findings in this branch.", "next": "Verify same HTTP factory on NPU; continue full four-model long semantic and heterogeneous PD validation.", "state": "active", "done": false, "blocker": "", "metrics": {}, "params": {"backend": "gpu"}, "artifacts": [{"path": "analysis/serving-matrix-201/eos229-koni-http-final.json", "size": 5067, "sha256": "d5be9a4a52a6485f"}], "experiments": ["experiments/2026-10-23-serving-eos229.md"], "labels": ["bug"], "review": true, "todos": [], "note": "", "agent": "codex", "host": "xeon-max", "branch": "main", "commit": "351d39d-dirty"}
 {"id": "328b8349e9974a059ddc7e1a8a485e2c", "ts": "2026-10-23T21:32:23+09:00", "task": "Tokenizer EOS in batched serving (aicomp #229)", "result": "Real ATOM KONI HTTP also passes5/5 baseline/fixed/ignore/stream/reuse checks using same production factory: config-only length vs union stop. GPU+ATOM now10/10 expected outcomes; no SDK/artifact rebuild.", "next": "Update PR230 with NPU evidence; finish KONI medium-context RNGD handoff and broader PD work.", "state": "active", "done": false, "blocker": "", "metrics": {}, "params": {"backend": "atom", "kv_blocks": "1"}, "artifacts": [{"path": "analysis/serving-matrix-201/eos229-koni-atom-http.json", "size": 5199, "sha256": "4735255db379ff00"}], "experiments": ["experiments/2026-10-23-serving-eos229.md"], "labels": ["bug"], "review": true, "todos": [], "note": "", "agent": "codex", "host": "xeon-max", "branch": "main", "commit": "351d39d-dirty"}
 {"id": "2a2897e2be914684b04942a24bf4f220", "ts": "2026-10-24T03:15:52+09:00", "task": "Chunk-copy overlap (aicomp #47)", "result": "Four-model GPU copy-overlap A/B complete: 192 timed batches passed output parity. Long/c8 changes vary by block; Qwen4 B +17.39% is confounded by slower baseline prefill. Keep opt-in.", "next": "Publish reverse-block evidence; finish warm RNGD-to-ATOM prefetch comparison for KONI, Qwen3-4B, Qwen3-0.6B and Gemma3-1B", "state": "active", "done": false, "blocker": "", "metrics": {}, "params": {}, "artifacts": [{"path": "analysis/serving-matrix-201/chunk47-four-model-serving-ab-summary.json", "size": 10794, "sha256": "52442216970a0e25"}], "experiments": ["experiments/2026-10-24-chunk-copy47.md"], "labels": ["benchmark"], "review": true, "todos": [], "note": "", "agent": "codex", "host": "xeon-max", "branch": "main", "commit": "351d39d-dirty"}
+{"id": "1020452aea8445f080108c1ed7f6d643", "ts": "2026-10-24T04:35:37+09:00", "task": "Dense hybrid GPU KV import (aicomp #258)", "result": "Dense hybrid receiver fix:31focused and505full CPU tests passed,34skipped,253subtests,151.69s. Preparing isolated PR and fresh Gemma live rerun; hardware and numerical acceptance not yet claimed.", "next": "Run corrected Gemma RNGD/GPU matrix preserving original failure; complete lifecycle and numerical checks", "state": "active", "done": false, "blocker": "", "metrics": {}, "params": {}, "artifacts": [], "experiments": ["experiments/2026-10-24-dense-hybrid258.md"], "labels": ["bug"], "review": false, "todos": [], "note": "", "agent": "codex", "host": "xeon-max", "branch": "main", "commit": "351d39d-dirty"}
+{"id": "fa2dcb1997d24559b0ed5ed800ae94c1", "ts": "2026-10-24T05:50:42+09:00", "task": "Layer-progress RNGD transfer (aicomp #265)", "result": "Created issue265 and isolated worktree. Implemented source coverage validation and reserved partial hybrid-cache import; found and added a guard against overwriting admitted prefix. Worker/owner/source wiring and hardware verification not yet implemented.", "next": "Finish final regression then connect validated progress to subprocess futures and reserved GPU admission; keep live263ATOM queue unchanged", "state": "active", "done": false, "blocker": "", "metrics": {}, "params": {}, "artifacts": [], "experiments": ["experiments/2026-10-24-rngd-layer-stream265.md"], "labels": ["experiment"], "review": false, "todos": [], "note": "", "agent": "codex", "host": "xeon-max", "branch": "main", "commit": "351d39d-dirty"}
+{"id": "670f2e0e98354387addd1807770cdef6", "ts": "2026-10-24T05:55:03+09:00", "task": "Layer-progress RNGD transfer (aicomp #265)", "result": "Partial-import groundwork passes22focused and556full tests,15skipped,253subtests. Coverage checks reject overlap/incomplete spans; partial hybrid writes reject unreserved memory and admitted-prefix overwrite. No wire/owner integration or overlap claim yet.", "next": "Publish draft groundwork then connect opt-in subprocess progress to reserved GPU admission; live263ATOM queue continues separately", "state": "active", "done": false, "blocker": "", "metrics": {}, "params": {}, "artifacts": [], "experiments": ["experiments/2026-10-24-rngd-layer-stream265.md"], "labels": ["experiment"], "review": false, "todos": [], "note": "", "agent": "codex", "host": "xeon-max", "branch": "main", "commit": "351d39d-dirty"}
+{"id": "9635af86bf8a4e3488b13630c09c75b3", "ts": "2026-10-24T06:32:18+09:00", "task": "Within-request layer streaming (aicomp #265)", "result": "Opt-in SHM progress receiver and GPU owner implemented; request-ID alias and capacity failure regressions fixed. Focused CPU/CUDA18pass; full574pass/15skip/253subtests. Controlled producers only; no real RNGD streaming speedup claim.", "next": "Connect real RNGD source adapters and ATOM owner; measure same-route parity and compute-transfer overlap", "state": "active", "done": false, "blocker": "", "metrics": {}, "params": {"stream_layers": "opt-in"}, "artifacts": ["evidence265/stream265-final-regression.log"], "experiments": ["experiments/2026-10-24-rngd-layer-stream265.md"], "labels": ["experiment", "rngd"], "review": true, "todos": [], "note": "", "agent": "codex", "host": "xeon-max", "branch": "main", "commit": "351d39d-dirty"}
+{"id": "19634472c8254c7d89e8636c33d4497d", "ts": "2026-10-24T06:41:38+09:00", "task": "Within-request layer streaming (aicomp #265)", "result": "Production source SHM protocol, rngd-full per-group export and Torch CLI opt-in implemented. Full591pass before final head compatibility helper; final source13pass. Actual KONI RNGD source parity running; no speedup claim. Four-model acceptance ledger now includes all576 heldout responses without waiving strict failures.", "next": "Poll source parity session5290/PID1408878; finish hardware evidence, then connect long-context factories and ATOM owner", "state": "active", "done": false, "blocker": "", "metrics": {}, "params": {"engine": "rngd-full", "width": "128", "sdk": "2026.4.0"}, "artifacts": ["evidence265/stream265-source-final.log"], "experiments": ["experiments/2026-10-24-rngd-layer-stream265.md"], "labels": ["experiment", "rngd"], "review": true, "todos": [], "note": "", "agent": "codex", "host": "xeon-max", "branch": "main", "commit": "351d39d-dirty"}
+{"id": "bb0db22a79ed4a06ba77ec40328d8196", "ts": "2026-10-24T06:42:16+09:00", "task": "Within-request layer streaming (aicomp #265)", "result": "Source protocol/CLI tests pass; actual KONI legacy rngd-full attempt failed before inference: graph compiled74s, on-card head imports unavailable furiosa.native_llm_common. No KV/logit or performance result. Failure retained; use validated partitioned/host-head factories next.", "next": "Connect the four-model validated factory adapters to source streaming; repeat real source parity before decoder overlap benchmarks", "state": "active", "done": false, "blocker": "", "metrics": {}, "params": {"engine": "rngd-full", "width": "128", "sdk": "2026.4.0"}, "artifacts": ["evidence265/stream265-source-hardware.log"], "experiments": ["experiments/2026-10-24-rngd-layer-stream265.md"], "labels": ["experiment", "rngd"], "review": false, "todos": [], "note": "", "agent": "codex", "host": "xeon-max", "branch": "main", "commit": "351d39d-dirty"}
+{"id": "b0667e569a434d809a2402e0819ce0cc", "ts": "2026-10-24T06:58:54+09:00", "task": "Within-request layer streaming (aicomp #265)", "result": "All4 actual RNGD-GPU routes pass8/8 fixed8-token output pairs; source24/24 exact KV/logits. Stream imports56/72/52/64 spans vs0 whole. Tuple/list server integration bug reproduced and fixed; no speedup or quality-equivalence claim.", "next": "Finish full regression and publish; run alternating same-engine 2K/8K inputs and record early imports, TTFT and total latency; ATOM owner remains pending", "state": "active", "done": false, "blocker": "", "metrics": {}, "params": {"models": "4", "concurrency": "2", "output_tokens": "8", "widths": "128,512"}, "artifacts": ["evidence265/stream265-gpu-queue.json"], "experiments": ["experiments/2026-10-24-rngd-layer-stream265.md"], "labels": ["experiment", "rngd"], "review": true, "todos": [], "note": "", "agent": "codex", "host": "xeon-max", "branch": "main", "commit": "351d39d-dirty"}
+{"id": "30e2f47887874d45b40330d564620267", "ts": "2026-10-24T07:01:24+09:00", "task": "Within-request layer streaming (aicomp #265)", "result": "Four-model factory integration verified:24/24 exact real RNGD KV/logit cases,8/8 real GPU token pairs, full606passed/15skipped/253subtests. Tuple/list integration failure fixed and retained. Long2K/8K timing prepared, not yet measured; ATOM streaming pending.", "next": "Run same-engine alternating whole/stream long-context matrix and examine TTFT, end-to-end latency and early-import traces", "state": "active", "done": false, "blocker": "", "metrics": {}, "params": {"models": "4", "concurrency": "2", "source_widths": "128,512"}, "artifacts": ["evidence265/stream265-factory-full.log", "evidence265/stream265-gpu-queue.json"], "experiments": ["experiments/2026-10-24-rngd-layer-stream265.md"], "labels": ["experiment", "rngd"], "review": true, "todos": [], "note": "", "agent": "codex", "host": "xeon-max", "branch": "main", "commit": "351d39d-dirty"}
+{"id": "63bac08d360c462ea1702ca871c14305", "ts": "2026-10-24T07:10:12+09:00", "task": "Within-request layer streaming (aicomp #265)", "result": "All4 long RNGD-GPU runs complete: c2 inputs2K/8K output32,3 measured batches/mode. Whole→stream median seconds Q06 3.638→2.613(-28.2%), Q4 7.306→5.690(-22.1%), Gemma3.936→3.768(-4.3%), KONI7.599→5.856(-22.9%). All64 sequences incl warmups exact;732 imports completed before source Future. Host trace, not device-overlap proof.", "next": "Verify long byte-level KV and streaming fault isolation; connect ATOM ownership-safe partial import; retain opt-in pending wider loads", "state": "active", "done": false, "blocker": "", "metrics": {}, "params": {"models": "4", "concurrency": "2", "input_tokens": "2048,8192", "output_tokens": "32", "compiled_width": "8192", "repeats": "3"}, "artifacts": ["evidence265/stream265-long-summary.json"], "experiments": ["experiments/2026-10-24-rngd-layer-stream265.md"], "labels": ["benchmark", "rngd"], "review": true, "todos": [], "note": "", "agent": "codex", "host": "xeon-max", "branch": "main", "commit": "351d39d-dirty"}
+{"id": "636712c7c9774931afdfeb40f706ed4c", "ts": "2026-10-24T07:32:29+09:00", "task": "Within-request layer streaming (aicomp #265)", "result": "Four-model RNGD-ATOM:16 mode batches/32 sequences exact, early imports preserve B1 ownership; owned Pod deleted. Long source24/24 at2K/8K exact KV/logits. Full626passed/15skipped/253subtests; partial-prefix publication fixed.", "next": "Measure long ATOM streaming performance and actual streaming cancellation/producer failure; retain numerical-quality limits", "state": "active", "done": false, "blocker": "", "metrics": {}, "params": {"models": "Qwen3-0.6B,Qwen3-4B,Gemma3-1B,KONI-8B", "atom_source_width": "512"}, "artifacts": ["evidence265/stream265-atom-summary.json"], "experiments": ["experiments/2026-10-24-rngd-layer-stream265.md"], "labels": ["experiment", "rngd"], "review": true, "todos": [], "note": "", "agent": "codex", "host": "RNGD+ATOM host", "branch": "main", "commit": "351d39d-dirty"}
+{"id": "f36dafcfc72848ca90f5561030f49edd", "ts": "2026-10-24T07:49:06+09:00", "task": "Within-request layer streaming (aicomp #265)", "result": "All4 GPU streaming hardware faults passed: cancellation/reuse and source death preserve active512-token decode, kill at5/6/4/10 tokens; all child exits0 and local devices idle. Auditor rerun exact. Long ATOM matrix continues (Q06 complete; Q4 active).", "next": "Publish GPU fault evidence; finish long ATOM matrix and run ATOM hardware faults", "state": "active", "done": false, "blocker": "", "metrics": {}, "params": {"runtime": "33d6cb0", "source_width": "8192"}, "artifacts": ["evidence265/stream265-fault-summary.json"], "experiments": ["experiments/2026-10-24-rngd-layer-stream265.md"], "labels": ["experiment", "rngd"], "review": true, "todos": [], "note": "", "agent": "codex", "host": "GPU+RNGD host", "branch": "main", "commit": "351d39d-dirty"}
+{"id": "8c7b1d3b116049ad81b87cea3cf5b526", "ts": "2026-10-24T07:57:43+09:00", "task": "Within-request layer streaming (aicomp #265)", "result": "Long ATOM Qwen4 complete16batches/32sequences exact: median completion21.26% lower without prefetch and22.90% with prefetch; Q06 previously35.66/39.25%. Gemma now running. Added partial-prefetch fail/cancel regressions;39focused tests pass, production unchanged.", "next": "Complete Gemma/KONI long ATOM, run full auditor, then actual three-case ATOM faults", "state": "active", "done": false, "blocker": "", "metrics": {}, "params": {"runtime": "33d6cb0", "input_tokens": "2048,8192"}, "artifacts": ["evidence265/stream265-qwen4b-long-atom.json"], "experiments": ["experiments/2026-10-24-rngd-layer-stream265.md"], "labels": ["benchmark", "rngd"], "review": true, "todos": [], "note": "", "agent": "codex", "host": "RNGD+ATOM host", "branch": "main", "commit": "351d39d-dirty"}
+{"id": "c6fcebd4f4144d41a6e4cb2f9a61eb0b", "ts": "2026-10-24T08:16:30+09:00", "task": "Within-request layer streaming (aicomp #265)", "result": "Four-model GPU notification control complete48batches/96sequences exact; all exits0/local devices idle. Early vs deferred improvement4.64/2.99/0.40/1.72%; deferred vs whole22.74/19.62/2.84/21.25%. Most Qwen/KONI gain persists without within-request early notification; not proof of DMA overlap. Full auditor passes.", "next": "Publish full attribution results; finish KONI long ATOM64332 and run prepared ATOM faults", "state": "active", "done": false, "blocker": "", "metrics": {}, "params": {"runtime": "33d6cb0", "control": "deferred_notification"}, "artifacts": ["evidence265/stream265-control-summary.json"], "experiments": ["experiments/2026-10-24-rngd-layer-stream265.md"], "labels": ["benchmark", "rngd"], "review": true, "todos": [], "note": "", "agent": "codex", "host": "GPU+RNGD host", "branch": "main", "commit": "351d39d-dirty"}
+{"id": "2350450944714c339f9ec5a5fc02f95a", "ts": "2026-10-24T08:23:40+09:00", "task": "Within-request layer streaming (aicomp #265)", "result": "Long ATOM all4 complete64batches/128sequences exact, full auditor passes; completion reduction35.66/21.26/9.79/19.19% without prefetch,39.25/22.90/11.21/16.84% with prefetch. Timing Pod deletion verified and queue exited0. Allocated separate owned fault Pod and started three-case matrix53032.", "next": "Publish complete ATOM timing; finish actual ATOM partial cancel/death and active-prefetch isolation matrix53032", "state": "active", "done": false, "blocker": "", "metrics": {}, "params": {"runtime": "33d6cb0", "source_width": "8192"}, "artifacts": ["evidence265/stream265-long-atom-summary.json"], "experiments": ["experiments/2026-10-24-rngd-layer-stream265.md"], "labels": ["benchmark", "rngd"], "review": true, "todos": [], "note": "", "agent": "codex", "host": "RNGD+ATOM host", "branch": "main", "commit": "351d39d-dirty"}
+{"id": "8aaa500919a74b4e96d76dfa275d3405", "ts": "2026-10-24T08:41:44+09:00", "task": "Within-request layer streaming (aicomp #265)", "result": "GPU c4/fixed128 all4 complete32batches/128sequences exact and drained; exits0/devices idle. Whole-to-stream completion reductions20.72/16.44/2.26/19.34% (Q06/Q4/Gemma/KONI), full independent auditor passes. ATOM fault queue53032 remains active.", "next": "Publish c4 evidence/review map; complete remaining ATOM hardware faults and final scope audit", "state": "active", "done": false, "blocker": "", "metrics": {}, "params": {"concurrency": "4", "output_tokens": "128", "runtime": "33d6cb0"}, "artifacts": ["evidence265/stream265-c4-summary.json"], "experiments": ["experiments/2026-10-24-rngd-layer-stream265.md"], "labels": ["benchmark", "rngd"], "review": true, "todos": [], "note": "", "agent": "codex", "host": "GPU+RNGD host", "branch": "main", "commit": "351d39d-dirty"}
+{"id": "f077f3618b9a4b0ab0d6cd317e87b3b8", "ts": "2026-10-24T09:10:56+09:00", "task": "Within-request layer streaming (aicomp #265)", "result": "Prepared PR266 evidence for allfour actual ATOM streaming faults:12cases pass, retained16/512-token arrays independently verified from publication copy. Cleanup verified. Added reproducible fault record and updated streaming scope; production unchanged.", "next": "Publish fault evidence; continue GPUKONI and ATOMHTTPc16, then GPUHTTP after local cleanup", "state": "active", "done": false, "blocker": "", "metrics": {}, "params": {}, "artifacts": ["evidence265/stream265-atom-fault-summary.json"], "experiments": ["experiments/2026-10-24-stream265-atom-faults.md"], "labels": ["benchmark", "rngd"], "review": true, "todos": [], "note": "", "agent": "codex", "host": "RNGD+ATOM host", "branch": "perf/265-rngd-stream", "commit": "33d6cb0"}
+{"id": "69f886cf99bb4d24a17eef3e6d4302dc", "ts": "2026-10-24T09:13:24+09:00", "task": "Within-request layer streaming (aicomp #265)", "result": "Prepared c16 PR evidence: allfour32batches/512sequences exact; auditor reproduces reductions25.20/20.02/2.33/20.91% from publication copy; malformed-evidence self-check passes. Runtime unchanged; GPU/ATOMHTTP queues active.", "next": "Publish c16 evidence and monitor HTTP correctness, failures and cleanup", "state": "active", "done": false, "blocker": "", "metrics": {}, "params": {}, "artifacts": ["evidence265/stream265-c16-summary.json"], "experiments": ["experiments/2026-10-24-stream265-c16.md"], "labels": ["benchmark", "rngd"], "review": true, "todos": [], "note": "", "agent": "codex", "host": "GPU+RNGD host", "branch": "perf/265-rngd-stream", "commit": "33d6cb0"}
+{"id": "aae08bd8e3ef40c39645cf7375f63d13", "ts": "2026-10-24T10:01:42+09:00", "task": "RNGD layer streaming (aicomp #265)", "result": "Qwen06/Qwen4 GPU HTTP native/whole/stream six modes independently audited; each264 responses across8conditions. Whole/stream references exact. Qwen4 observed throughput+22.1-27.2% vswhole; native parity differences remain.", "next": "Finish Gemma/KONI GPU and backlog128 ATOM matrix; isolate fixed-width cost after devices free", "state": "active", "done": false, "blocker": "", "metrics": {}, "params": {"concurrency": "16", "width": "8192"}, "artifacts": [{"path": "analysis/serving-matrix-201/http265-qwen4b-gpu-observations.json", "size": 3782, "sha256": "9f218fe842631dd0"}], "experiments": ["experiments/2026-10-24-stream265-qwen-http.md"], "labels": ["benchmark"], "review": true, "todos": [], "note": "", "agent": "codex", "host": "xeon-max", "branch": "main", "commit": "351d39d-dirty"}
+{"id": "da1ed733433f4f25892928b452a4ca6e", "ts": "2026-10-24T10:20:22+09:00", "task": "RNGD layer streaming (aicomp #265)", "result": "Prepared reviewable Gemma HTTP evidence:three raw modes independently audited,whole/stream8/8 exact;native strict4/8 and throughEOS8/8 retained. Single-batch+1.51-3.17% throughput not a reliable speedup claim. GPUKONI/ATOMQwen4 continue.", "next": "Publish Gemma evidence; finish running four-model matrix and measure width cost after device release", "state": "active", "done": false, "blocker": "", "metrics": {}, "params": {}, "artifacts": [{"path": "analysis/serving-matrix-201/http265-gemma-gpu-observations.json", "size": 3806, "sha256": "8ca8103e474abe94"}], "experiments": ["experiments/2026-10-24-stream265-gemma-http.md"], "labels": ["benchmark"], "review": true, "todos": [], "note": "", "agent": "codex", "host": "xeon-max", "branch": "main", "commit": "351d39d-dirty"}
+{"id": "724d1e78a2b742c9a9edf52d23c02a8a", "ts": "2026-10-24T10:54:08+09:00", "task": "RNGD layer streaming (aicomp #265)", "result": "GPU HTTP queue41030 exit0;all12modes independently audited,96conditions/1536tokenchecks/1536SSEchecks. Whole/stream8/8exact permodel. KONI observed+20.94-33.93%throughput;native numerical failures retained. Widthqueue beganQwen06w512 afteridle check.", "next": "Continue remainingATOMmatrix and width268 sourceprobes;publish completedGPU evidence without waiving numerical/quality failures", "state": "active", "done": false, "blocker": "", "metrics": {}, "params": {}, "artifacts": [{"path": "analysis/serving-matrix-201/http265-four-model-gpu-summary.json", "size": 2932, "sha256": "6856c50fff58ae2e"}], "experiments": ["experiments/2026-10-24-stream265-gpu-http-complete.md"], "labels": ["benchmark"], "review": true, "todos": [], "note": "", "agent": "codex", "host": "xeon-max", "branch": "main", "commit": "351d39d-dirty"}
+{"ts": "2026-10-24T03:53:34.726931+00:00", "task": "RNGD layer streaming (#265)", "result": "Three-model ATOM HTTP whole/stream evidence audited; KONI native timeout retained, longer-wait diagnostic pending.", "next": "Complete KONI ATOM coverage and repeat performance measurements", "state": "active", "done": false, "experiments": ["experiments/2026-10-24-stream265-atom-http-progress.md"]}
+{"ts": "2026-10-24T04:21:47.012026+00:00", "task": "RNGD layer streaming (#265)", "result": "KONI native600s diagnostic completes264 responses, original180s failure retained;8K/128 max completion263.08s. Whole/stream pending.", "next": "Complete KONI whole/stream HTTP runs and compare latency", "state": "active", "done": false, "experiments": ["experiments/2026-10-24-stream265-atom-http-progress.md"]}
+{"ts": "2026-10-24T05:13:10.885088+00:00", "task": "aicomp #265 / #267", "result": "KONI whole diagnostic264 responses pass;8K/128 native7.7847->whole9.3113tok/s; native/whole token parity6/8. Stream pending, different physical ATOM allocation.", "state": "active", "done": false, "review": true, "experiments": ["experiments/2026-10-24-stream265-atom-http-progress.md"], "runtime_commit": "33d6cb08ec806f693dbecfd7d630331a3bd1bb2f"}
+{"ts": "2026-10-24T05:56:15.922056+00:00", "task": "aicomp #265 / #267", "result": "KONI all3 routes264 responses each audited; whole/stream8/8 exact.8K128 throughput7.7847/9.3113/10.8298tok/s. Native parity6/8; single batch and different physical card limits retained.", "state": "active", "done": false, "review": true, "experiments": ["experiments/2026-10-24-stream265-atom-http-progress.md"], "runtime_commit": "33d6cb08ec806f693dbecfd7d630331a3bd1bb2f"}
+{"ts": "2026-10-30T04:24:12.257264+00:00", "task": "aicomp #201 / #265", "result": "ATOM short/long final-HTTP sweep:12 arms96conditions528responses audited;whole/stream16/16 references exact. Native/PD Qwen0.6/Qwen4=4/4,Gemma2/4(other2afterEOS),KONI2/4(preEOS). Eleven selfchecks pass;coordinator/cleanup0,fresh owned-Pod absence verified.", "state": "active", "done": false, "review": true, "experiments": ["experiments/2026-10-30-atom-edge201-final.md"], "runtime_scope": "historical stream265 B1 engine,final HTTP SHA256e3449979b88d03edb6d5bf0d402a708e5d98054fdf203e22b480f3d7e6fd759a"}
diff --git a/opt_prime/examples/pd/http_matrix/STATUS.md b/opt_prime/examples/pd/http_matrix/STATUS.md
index be6a7807..d2880622 100644
--- a/opt_prime/examples/pd/http_matrix/STATUS.md
+++ b/opt_prime/examples/pd/http_matrix/STATUS.md
@@ -47,3 +47,102 @@ Qwen3-0.6B input8192 paired prefill: median997.98 ->955.64 ms (-4.24%), six runs
 GPU HTTP blockA96batches all parity;long/c8 chunk-copy throughput changes: Qwen0.6-3.1%,Qwen4+2.5%,KONI+2.4%,Gemma-1.5%. Reverse-order blockB pending. Export-only0.1–4GB curve: chunked slower, but incremental source staging bounded at~67.1MB rather than4GB. Keep opt-in; no general serving speedup claim.
 
 Reverse-order blockB completed: 192 total timed batches passed parity. Long/c8 A/B changes: Qwen0.6 -3.12%/+5.80%, Qwen4 +2.49%/+17.39%, KONI +2.38%/-1.53%, Gemma -1.54%/-0.28%. Qwen4 B baseline prefill slowed while transfer stayed constant, confounding the larger gain. Keep opt-in; no repeatable general throughput improvement. See evidence47/chunk47-four-model-serving-ab-summary.json.
+#258 dense hybrid KV: actual Gemma failure reproduced; mapping/group validation corrected.31focused and505full CPU tests pass; live rerun pending.
+
+#265 groundwork: coverage validation and reserved partial hybrid-cache import implemented;22focused and556full tests pass (15skip,253subtests). Admitted-prefix overwrite regression fixed. Not connected to worker/owner/source paths yet; no hardware overlap claim.
+
+## Layer-progress receiver and GPU owner (#265)
+
+Opt-in subprocess progress validation and incremental GPU imports implemented; partial state remains private until complete. CPU/CUDA focused18pass; full574pass,15skip,253subtests. Invalid boolean ID and capacity error regressions fixed. Controlled producers only: real RNGD source adapters, ATOM owner, and hardware overlap/performance remain pending. See layer-stream265 record/evidence265.
+
+## Producer connection (#265)
+
+Production SHM source protocol and rngd-full per-group export connected. Torch CLI opt-in added; unsupported owners rejected. Full591pass before final legacy-head helper; final source13pass. Actual KONI source parity running; no numerical or speedup claim yet. Four-model long-context factories and ATOM ownership integration remain pending.
+
+Hardware attempt ended before inference: legacy FullPrefillEngine on-card head requires missing furiosa.native_llm_common. Group graph compiled74s; no KV/logit parity result. Continue with the already validated four-model factory adapters and KONI partitioned host-head path.
+
+## Four-model streaming factories (#265)
+
+Validated producers connected:24/24 actual RNGD source cases exact K/V/logits;8/8 real RNGD-GPU fixed8-token pairs exact, all four models. Width128/512 only. Factory tuple/list integration bug reproduced and corrected;27focused tests pass. Long-context repeated timing, hardware fault isolation under streaming and ATOM owner remain pending. Same-route parity does not waive previous cross-backend content counterexamples.
+
+Final factory integration full regression:606passed,15skipped,253subtests in166.21s. Short-width source/GPU hardware checks complete; long-context matrix is the next measurement.
+
+## Long-context streaming measurements (#265)
+
+Runtime171e8c4: c2 mixed2K/8K input, fixed32output,3batches/mode after warmups. Median completion reduction28.2% Q06,22.1% Q4,4.3% Gemma,22.9% KONI. All64sequences incl warmups exact;732 GPU span imports complete before source Future. Published auditor reproduces raw-record summary. Host trace only; no universal speedup or semantic-equivalence claim. Long KV byte parity, hardware fault tests and ATOM partial ownership remain pending.
+## B1 next-request prefill (#253), 2026-10-24
+
+Opt-in remote_prefetch / --pd-prefetch-next implemented for single-lane ATOM remote-only PD. Keep one source result reserved through owner import; cancelled writers drain before reuse. Prefix/host cache admission remains late, so early prefill transfers the full prompt.35focused tests and full511passed34skipped253subtests; real four-model hardware pipeline/performance validation pending. No speedup claim. See experiments/2026-10-24-atom-prefetch253.md.
+
+## ATOM streaming and long source parity (#265)
+
+Long source24/24 conditions at2K/8K have exact KV/logits. ATOM partial import and B1 prefetch ownership implemented; failed/partial prompts no longer publish prefixes. Four models × four modes:16batches/32sequences same-route exact, all drain; owned Pod deleted and absence verified. Full626passed/15skipped/253subtests. Prerequisite #255 included. Long ATOM timing and streaming hardware faults remain pending; no ATOM speedup claim.
+
+## Streaming hardware fault checks (#265)
+
+Runtime33d6cb0: allfour GPU cancellation/reuse and partial-producer-death checks passed. Active512-token decode remains exact to same-route no-fault reference; kill at5/6/4/10 tokens. All child exits0 and local devices idle. Auditor reproduces summary. First harness-only AttributeError and interrupted follow-up retained. Long ATOM Q06 complete16batches/32sequences exact, median completion reduction35.66% without prefetch/39.25% with prefetch; remaining models running. ATOM hardware faults remain pending.
+
+Qwen4 long ATOM completed16batches/32sequences exact: median completion reduction21.26% prefetchoff/22.90% on,3samples/mode. Gemma/KONI pending. Added two partial-prefetch failure/cancellation regressions; related39passed in5.00s. Production unchanged; previous full626-test run predates these two added tests.
+
+## Notification-timing control (#265)
+
+Allfour GPU control48batches/96sequences exact. Early-vs-deferred completion reduction4.64/2.99/0.40/1.72%; deferred-vs-whole22.74/19.62/2.84/21.25%. Most Qwen/KONI gain persists without within-request early notification; deferred still changes source assembly/destination import and permits cross-request overlap, so no pure source-assembly or DMA-overlap attribution. Three samples/mode; Gemma ranges overlap. Auditor reproduces summary. Gemma long ATOM complete16batches/32sequences exact,9.79/11.21% reduction without/with prefetch. KONI long ATOM and ATOM faults pending.
+
+## Complete long ATOM timing (#265)
+
+Allfour64batches/128sequences same-route exact; full auditor passes. Whole-vs-stream completion reductions35.66/21.26/9.79/19.19% without prefetch and39.25/22.90/11.21/16.84% with prefetch, Q06/Q4/Gemma/KONI. Three measured batches/mode, synthetic2K/8K fixed32output, B1/two queued requests. Timing Pod absence independently verified after termination; queue exited0. Separate owned ATOM hardware fault matrix now running; no fault result yet.
+
+## Higher-load GPU streaming (#265)
+
+C4/fixed128output, mixed2K/8K/2K/8K: allfour32batches/128sequences exact, exits0 and local devices idle. Whole-vs-stream median completion reductions20.72/16.44/2.26/19.34% (Q06/Q4/Gemma/KONI),3measured batches/mode. Auditor verifies outputs, ordering, arrivals/spans and reproduces summaries. STREAMING.md now maps source/transport/destination/CLI code and explicitly scopes same-host research-factory support. ATOM Q06 faults passed; other models still running.
+
+## Complete actual ATOM streaming faults (#265)
+
+Allfour models pass all3 actual cases (12 total), independently comparing retained16-token replacement and512-token survivor arrays. Producer death at survivor13/6/10/5tokens; owners drain without poisoning, incomplete prefixes remain private. All child/copy exits0; queue terminal0 and owned Pod absence verified. See experiments/2026-10-24-stream265-atom-faults.md and evidence265/stream265-atom-fault-summary.json. Native numerical differences and hybrid-prefix support remain separate limitations.
+
+## Complete c16 queued streaming matrix (#265)
+
+Four models complete32batches/512sequences exact, c16 queued behind4slots, mixed2K/8K fixed128output. Whole-to-stream median completion reductions25.20/20.02/2.33/20.91% (Q06/Q4/Gemma/KONI),3samples/mode. Full independent auditor and its malformed-evidence self-check pass; queue0/devices idle verified. See experiments/2026-10-24-stream265-c16.md. HTTPc16 runs are separate and still in progress; numerical boundaries remain.
+
+## Qwen HTTP c16 streaming (#265)
+
+Qwen0.6/Qwen4 native/whole/stream six modes independently pass eight conditions each. Whole/stream references exact. Qwen4 observed throughput22.1–27.2% above whole in single batches; native/PD numerical differences remain. Short-input PD remains slower, fixedwidth8192 cost is not yet isolated. Gemma/KONI and ATOM matrix pending. See experiments/2026-10-24-stream265-qwen-http.md and evidence265.
+
+## Gemma HTTP c16 follow-up (#265)
+
+Allthree modes independently pass8conditions each;whole/stream exact. Native strict4/8 differences occur afterEOS;throughEOS8/8 on these fixtures only. Observed+1.51–3.17% throughput is small and needs repeat measurements. Earlier Gemma content/numerical counterexamples remain. KONI GPU and remainingATOM cases active. See experiments/2026-10-24-stream265-gemma-http.md.
+
+## GPU HTTP c16 matrix completed (#265)
+
+Allfour models x3modes completed:96conditions,1536token checks,1536SSE text checks. Whole/stream exact8/8 permodel. Native/whole exact Qwen06=8/8,Qwen4=4/8,Gemma=4/8(EOStrim8/8),KONI=5/8;earlier numerical/quality failures retained. KONI observed throughput+20.94–33.93%,single batches only. RemainingATOMmatrix andwidth268 probes active. See experiments/2026-10-24-stream265-gpu-http-complete.md.
+
+## ATOM HTTP progress (#265/#267)
+
+Three models pass whole/stream controls, eight conditions each. Qwen4 and Gemma native results also audited; Gemma remains slower than native. KONI native180s timeout retained and600s diagnostic pending. Listener and width variants are not production changes. See experiments/2026-10-24-stream265-atom-http-progress.md.
+
+
+## KONI native long-wait diagnostic
+
+Separate600s native run passes264 responses across8 conditions, with original180s failure retained. At8K/128/c16 max firstSSE253.93s and max completion263.08s; no OOM/listen drops. Whole/stream remain running. See stream265-atom-http-progress record and evidence265/http267d-koni-native-complete-summary.json.
+
+KONI whole HTTP diagnostic:264 responses pass,8K/128 throughput7.7847(native) ->9.3113(whole)tok/s; short inputs slower. Native/whole strict and EOS parity6/8. Stream is pending on a fresh physical-card allocation; no new runtime change. See stream265 ATOM HTTP progress record.
+
+KONI stream HTTP complete: all3 routes264 responses each audited; whole/stream8/8 exact, native/whole6/8.8K/128 native7.7847,whole9.3113,stream10.8298tok/s; stream maxE2E189.105s. All4 model B1/c16 ATOM whole/stream controls complete; native equivalence and B4 remain open.
+
+## Intermediate-length summary correction (2026-10-30)
+
+Fixed an evidence-summary key collision: aggregate `output_tokens` previously overwrote request length. Published native/default/PD GPU summaries now include `output_tokens_per_request` separately. Corrected the native GPU2K/128c4 table, which had selected32tokens/request; raw results and correctness conclusions are unchanged. PD GPU and existing ATOM tables were checked and already selected the intended raw conditions. Portable ATOM raw auditor/selfcheck added under evidence265:10checks, including preserving request/aggregate lengths, duplicate-arm rejection and corrupt/missing raw results. Current ATOM final-HTTP matrix remains in progress; these scripts do not certify uncompleted configurations.
+
+## Intermediate ATOM and short/long GPU coverage (2026-10-30)
+
+Final-HTTP ATOM native/whole/stream comparison completes all four models at512/2048 input,32/128 output,c1/c4:96 conditions,528 responses. All within-route checks pass and whole/stream references match4/4 per model. Native/PD references match2/4 for each Qwen and4/4 for Gemma/KONI; Qwen pre-EOS differences and prior quality limits remain. One ATOM lane, caches off, research artifacts; c4 is queued clients. Owned Pod cleanup and absence verified. See experiments/2026-10-30-atom-mid201-final.md.
+
+Native GPU short/long sweep completes four models at128/8192 input,32/128 output,c1/c4:32 conditions,176 responses, all16 reference arrays equal historical c16 controls. Four child exits and coordinator exit0; GPU compute-process query empty after completion. Portable raw auditor and8 selfchecks included. See experiments/2026-10-30-native-gpu-edge201.md. ATOM short/long c1/c4, heterogeneous GPU short/long, cache-on and supported export work remain separate; the full four-model goal is not complete.
+
+## RNGD-to-GPU short/long controls complete (2026-10-30)
+
+Four models, whole/stream, input128/8192, output32/128, c1/c4:64 conditions352 responses independently audited. Whole/stream references match4/4 per model; native matches Qwen0.6B4/4,Qwen4B2/4,Gemma4/4,KONI2/4. Qwen4/KONI pre-EOS differences remain. At8K/128/c4, stream throughput is75.41/36.29/56.54/39.71tok/s respectively, versus native60.84/40.92/58.83/40.46. Single batches and an added RNGD prevent stable/equal-resource claims. All exits0 and local GPU/RNGD idle checks pass. Portable auditor and10 selfchecks included. Remote ATOM short/long sweep remains active. See experiments/2026-10-30-pd-gpu-edge201.md.
+
+## ATOM short/long controls complete (2026-10-30)
+
+All four models complete native/whole/stream at128/8192 input,32/128 output,c1/c4:96conditions528responses. Whole/stream16/16 reference pairs match. Native/PD equality is Qwen0.6/Qwen4=4/4,Gemma2/4(other2equal through EOS),KONI2/4(pre-EOS differences retained). Single-batch8K/128/c4 stream throughput44.24/14.47/43.87/10.44tok/s; one ATOM lane,extra RNGD for PD. Eleven independent auditor selfchecks pass;all exits0 and owned Pod absence verified. Cache and broader goal work remain active. See experiments/2026-10-30-atom-edge201-final.md.
diff --git a/opt_prime/examples/pd/http_matrix/evidence253/atom253-cli-red.log b/opt_prime/examples/pd/http_matrix/evidence253/atom253-cli-red.log
new file mode 100644
index 00000000..318622e9
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence253/atom253-cli-red.log
@@ -0,0 +1,38 @@
+F
+=================================== FAILURES ===================================
+__ test_next_prefill_rejects_unsupported_route_before_load[torch-1-True-True] __
+
+monkeypatch = <_pytest.monkeypatch.MonkeyPatch object at 0x75d4cb85d2d0>
+backend = 'torch', slots = 1, remote = True, worker = True
+
+    @pytest.mark.parametrize('backend,slots,remote,worker', [('torch',1,True,True),('atom',2,True,True),
+                                                            ('atom',1,False,True),('atom',1,True,False)])
+    def test_next_prefill_rejects_unsupported_route_before_load(monkeypatch,backend,slots,remote,worker):
+        path=Path(__file__).parents[1]/'examples/pd/serve_batched.py'
+        spec=importlib.util.spec_from_file_location('serve_prefetch_test',path)
+        factory=importlib.util.module_from_spec(spec);spec.loader.exec_module(factory)
+        args=SimpleNamespace(model='must-not-load',backend=backend,slots=slots,pd_prefetch_next=True,
+                             pd_remote_only=remote,pd_prefill_python='python' if worker else None,pd_prefill_device=None)
+        def unexpected_load(*args,**kwargs):
+            pytest.fail('unsupported prefetch route reached model loading')
+        monkeypatch.setattr(factory.AutoTokenizer,'from_pretrained',unexpected_load)
+        with pytest.raises(ValueError,match='single-lane'):
+>           factory.build(args)
+
+opt_prime/tests/test_serving_prefill_cache.py:89:
+_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
+opt_prime/examples/pd/serve_batched.py:69: in build
+    tok = AutoTokenizer.from_pretrained(a.model)
+_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
+
+args = ('must-not-load',), kwargs = {}
+
+    def unexpected_load(*args,**kwargs):
+>       pytest.fail('unsupported prefetch route reached model loading')
+E       Failed: unsupported prefetch route reached model loading
+
+opt_prime/tests/test_serving_prefill_cache.py:86: Failed
+=========================== short test summary info ============================
+FAILED opt_prime/tests/test_serving_prefill_cache.py::test_next_prefill_rejects_unsupported_route_before_load[torch-1-True-True]
+!!!!!!!!!!!!!!!!!!!!!!!!!! stopping after 1 failures !!!!!!!!!!!!!!!!!!!!!!!!!!!
+1 failed, 4 deselected in 2.58s
diff --git a/opt_prime/examples/pd/http_matrix/evidence253/atom253-focused.log b/opt_prime/examples/pd/http_matrix/evidence253/atom253-focused.log
new file mode 100644
index 00000000..c85ad178
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence253/atom253-focused.log
@@ -0,0 +1,2 @@
+...................................                                      [100%]
+35 passed in 4.39s
diff --git a/opt_prime/examples/pd/http_matrix/evidence253/atom253-full.log b/opt_prime/examples/pd/http_matrix/evidence253/atom253-full.log
new file mode 100644
index 00000000..a5356909
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence253/atom253-full.log
@@ -0,0 +1,21 @@
+.sssssss.....................ssssssssss........................................................... [ 18%]
+s.............................................................s.s....... [ 31%]
+..........ss.....................ss....s................................ [ 44%]
+........................................................................ [ 57%]
+..........s.s...........................................s............... [ 71%]
+.....................................................................s.s...............................................................................s. [ 99%]
+...                                                                      [100%]
+=============================== warnings summary ===============================
+tests/test_kvstore.py::test_second_engine_reuses_chunks_from_the_store[lmcache]
+  /home/gspark/venvs/pt213/lib/python3.10/site-packages/torch/nn/attention/flex_attention.py:2481: UserWarning: flex_attention called without torch.compile() - this will use an unfused implementation that materializes the full scores matrix instead of generating a fused kernel.
+
+  SOLUTION: Use torch.compile(flex_attention)(...)
+
+  If you want to debug your score_mod/mask_mod, you can set:
+  torch.nn.attention.flex_attention._FLEX_ATTENTION_DISABLE_COMPILE_DEBUG = True
+
+  This will allow you to use print statements or breakpoints. Note: This doesn't work with the backwards pass and may produce incorrect results.
+    _warn_once(
+
+-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html
+511 passed, 34 skipped, 1 warning, 253 subtests passed in 150.28s (0:02:30)
diff --git a/opt_prime/examples/pd/http_matrix/evidence253/atom253-pipeline-hypothesis.json b/opt_prime/examples/pd/http_matrix/evidence253/atom253-pipeline-hypothesis.json
new file mode 100644
index 00000000..6bee03e0
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence253/atom253-pipeline-hypothesis.json
@@ -0,0 +1,26 @@
+{
+  "warning": "Heuristic only: first SSE includes queueing and KV import, not a pure producer stage. Assumes perfect overlap of first-SSE and remaining decode, ignores import contention and shared resources. Not a throughput prediction or hardware result.",
+  "rows": [
+    {
+      "model": "qwen06b",
+      "first_sse_s": 2.6405895240604877,
+      "e2e_s": 2.9849001180846244,
+      "post_first_sse_s": 0.34431059402413666,
+      "optimistic_pipeline_ratio": 1.1303915625230094
+    },
+    {
+      "model": "qwen4b",
+      "first_sse_s": 4.540284454007633,
+      "e2e_s": 5.850550717907026,
+      "post_first_sse_s": 1.3102662638993934,
+      "optimistic_pipeline_ratio": 1.288586822515678
+    },
+    {
+      "model": "koni",
+      "first_sse_s": 4.553356555989012,
+      "e2e_s": 6.776184441987425,
+      "post_first_sse_s": 2.222827885998413,
+      "optimistic_pipeline_ratio": 1.4881734734950058
+    }
+  ]
+}
diff --git a/opt_prime/examples/pd/http_matrix/evidence253/atom253-red.log b/opt_prime/examples/pd/http_matrix/evidence253/atom253-red.log
new file mode 100644
index 00000000..34a6aa29
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence253/atom253-red.log
@@ -0,0 +1,38 @@
+F
+=================================== FAILURES ===================================
+_ test_b1_prefetch_overlaps_decode_and_holds_source_until_import[False-success] _
+
+fake_rebel = {'model': <test_atom_batching.FakeBlocks object at 0x77747516d4e0>}
+outcome = 'success', cache = False
+
+    @pytest.mark.parametrize('outcome', ['success', 'cancel', 'error'])
+    @pytest.mark.parametrize('cache', [False, True])
+    def test_b1_prefetch_overlaps_decode_and_holds_source_until_import(fake_rebel, outcome, cache):
+        """A reserved prefill must neither wait for the decode lane nor reuse a still-written source slot."""
+        import copy
+        from concurrent.futures import Future
+        from opt_prime.accel.pd.pd_batching import TorchPrefillEngine
+
+        class ControlledProducer:
+            def __init__(self):
+                self.calls = []
+
+            def submit(self, request, send_from=0, slot=None):
+                assert send_from == slot == 0
+                future = Future()
+                self.calls.append((request, future))
+                return future
+
+        model = _llama()
+        fake = _blocks_model(fake_rebel, model, batch=1, blocks=3 if cache else 1)
+        producer = ControlledProducer()
+        engine = TorchPrefillEngine(copy.deepcopy(model), max_context=128, chunk=16, page_size=16)
+>       b = AtomBatcher(fake, prefix_cache=cache, host_kv_gib=1.0 if cache else 0,
+                        prefill_worker=producer, local_prefill=False, remote_prefetch=True)
+E       TypeError: AtomBatcher.__init__() got an unexpected keyword argument 'remote_prefetch'
+
+opt_prime/tests/test_atom_batching.py:461: TypeError
+=========================== short test summary info ============================
+FAILED opt_prime/tests/test_atom_batching.py::test_b1_prefetch_overlaps_decode_and_holds_source_until_import[False-success]
+!!!!!!!!!!!!!!!!!!!!!!!!!! stopping after 1 failures !!!!!!!!!!!!!!!!!!!!!!!!!!!
+1 failed, 21 deselected in 2.74s
diff --git a/opt_prime/examples/pd/http_matrix/evidence253/events.jsonl b/opt_prime/examples/pd/http_matrix/evidence253/events.jsonl
new file mode 100644
index 00000000..c632df6c
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence253/events.jsonl
@@ -0,0 +1,2 @@
+{"id": "ebfa7b30c08f4fe0af8effefe012382b", "ts": "2026-10-24T02:30:43+09:00", "task": "B1 ATOM prefetch (aicomp #253)", "result": "Prepared6regressions in own worktree;not run yet, no production changes. Source design uses one retained shared-slot reservation after current KV import, with cancellation writer lifetime preserved.", "next": "After quiet GPU measurement window, run RED and implement bounded next-request prefill; real four-model validation", "state": "active", "done": false, "blocker": "", "metrics": {}, "params": {}, "artifacts": [{"path": "analysis/serving-matrix-201/atom253-pipeline-hypothesis.json", "size": 912, "sha256": "cdfbe1aa8d44b972"}], "experiments": ["experiments/2026-10-24-atom-prefetch253.md"], "labels": ["analysis"], "review": false, "todos": [], "note": "", "agent": "codex", "host": "xeon-max", "branch": "main", "commit": "351d39d-dirty"}
+{"id": "e4869f8d099b4b33aafcafe632019406", "ts": "2026-10-24T02:46:11+09:00", "task": "B1 ATOM prefetch (aicomp #253)", "result": "Implemented opt-in next-request prefill with retained single-slot ownership;35 focused tests pass. Full suite511 passed34 skipped253 subtests in150.28s; cancellation/orphan drain and cache/failure reuse covered. Hardware validation pending.", "next": "Publish draft, then paired real RNGD-ATOM four-model pipeline tests with worker kept warm", "state": "active", "done": false, "blocker": "", "metrics": {}, "params": {}, "artifacts": [{"path": "analysis/serving-matrix-201/atom253-full.log", "size": 1619, "sha256": "31a4984744eead95"}], "experiments": ["experiments/2026-10-24-atom-prefetch253.md"], "labels": ["experiment"], "review": true, "todos": [], "note": "", "agent": "codex", "host": "xeon-max", "branch": "main", "commit": "351d39d-dirty"}
diff --git a/opt_prime/examples/pd/http_matrix/evidence258/dense258-focused-b.log b/opt_prime/examples/pd/http_matrix/evidence258/dense258-focused-b.log
new file mode 100644
index 00000000..7864cb60
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence258/dense258-focused-b.log
@@ -0,0 +1,15 @@
+...............................                                          [100%]
+=============================== warnings summary ===============================
+tests/test_pd_dense_hybrid.py::test_dense_gemma_pd_matches_independent_hf_beyond_window
+  /home/gspark/venvs/pt213/lib/python3.10/site-packages/torch/nn/attention/flex_attention.py:2481: UserWarning: flex_attention called without torch.compile() - this will use an unfused implementation that materializes the full scores matrix instead of generating a fused kernel.
+
+  SOLUTION: Use torch.compile(flex_attention)(...)
+
+  If you want to debug your score_mod/mask_mod, you can set:
+  torch.nn.attention.flex_attention._FLEX_ATTENTION_DISABLE_COMPILE_DEBUG = True
+
+  This will allow you to use print statements or breakpoints. Note: This doesn't work with the backwards pass and may produce incorrect results.
+    _warn_once(
+
+-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html
+31 passed, 1 warning in 29.86s
diff --git a/opt_prime/examples/pd/http_matrix/evidence258/dense258-focused.log b/opt_prime/examples/pd/http_matrix/evidence258/dense258-focused.log
new file mode 100644
index 00000000..755579d0
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence258/dense258-focused.log
@@ -0,0 +1,109 @@
+............FF................                                           [100%]
+=================================== FAILURES ===================================
+_________________ test_recurrent_pd_hands_over_state[qwen3_5] __________________
+
+make = <function _qwen35 at 0x73d66db4f880>
+
+    @RECURRENT
+    def test_recurrent_pd_hands_over_state(make):
+        model = make()
+        engine = TorchPrefillEngine(copy.deepcopy(model), max_context=128, chunk=8, page_size=8, prefix_cache=False)
+        b = PDBatcher(model, PrefillWorker(engine), slots=2, max_context=128, page_size=8)
+        prompts = _prompts(4, 6, 30, seed=4)
+        fs = [b.submit(PDRequest(f"p{i}", p, 12)) for i, p in enumerate(prompts)]
+>       b.run_until_idle()
+
+tests/test_pd_hybrid.py:157:
+_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
+opt_prime/accel/pd/batching.py:207: in run_until_idle
+    self.step()
+opt_prime/accel/pd/pd_batching.py:468: in step
+    self._admit_prefilled(block=not self.decoding)
+opt_prime/accel/pd/pd_batching.py:497: in _admit_prefilled
+    self.kv.write_span(r.slot, out.start, out.keys, out.values)
+_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
+
+self = <opt_prime.accel.pd.paged.PagedKV object at 0x73d626af4b80>, slot = 0
+start = 0
+keys = tensor([[[[-0.0660,  2.0859,  0.4345, -1.4313,  0.4459, -0.0251, -0.4985,
+           -1.7052, -1.7932,  0.2561,  1.213...26, -0.4552,
+           -0.7853,  0.2788, -2.0608, -0.8472, -0.4423, -0.3608,  1.5063,
+           -1.4844,  0.3283]]]])
+values = tensor([[[[-2.4548e-01, -2.1482e-01,  7.9515e-03,  2.1531e-01, -9.8814e-03,
+           -1.9493e-02, -1.3410e-01,  1.33...3, -3.6229e-02,
+           -1.5983e-01,  8.6865e-02,  1.8228e-01,  1.4877e-02, -7.5556e-02,
+            2.9426e-01]]]])
+
+    def write_span(self, slot, start, keys, values):
+        """keys/values [layers, kv_heads, n, head_dim] for positions start .. start+n of `slot` (a list of those, one
+        per KV group, for hybrid models), each group in one indexed copy (the PD admission of KV that another engine
+        prefilled)"""
+        if isinstance(keys, torch.Tensor) and isinstance(values, torch.Tensor):
+            layers = max(self.layer_group) + 1
+            if keys.ndim != 4 or values.shape != keys.shape or keys.shape[0] != layers:
+>               raise ValueError("dense KV span must contain every model layer with matching K/V shapes")
+E               ValueError: dense KV span must contain every model layer with matching K/V shapes
+
+opt_prime/accel/pd/paged.py:454: ValueError
+_______________ test_recurrent_pd_hands_over_state[qwen3_5_moe] ________________
+
+make = <function _qwen35_moe at 0x73d66db4f910>
+
+    @RECURRENT
+    def test_recurrent_pd_hands_over_state(make):
+        model = make()
+        engine = TorchPrefillEngine(copy.deepcopy(model), max_context=128, chunk=8, page_size=8, prefix_cache=False)
+        b = PDBatcher(model, PrefillWorker(engine), slots=2, max_context=128, page_size=8)
+        prompts = _prompts(4, 6, 30, seed=4)
+        fs = [b.submit(PDRequest(f"p{i}", p, 12)) for i, p in enumerate(prompts)]
+>       b.run_until_idle()
+
+tests/test_pd_hybrid.py:157:
+_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
+opt_prime/accel/pd/batching.py:207: in run_until_idle
+    self.step()
+opt_prime/accel/pd/pd_batching.py:468: in step
+    self._admit_prefilled(block=not self.decoding)
+opt_prime/accel/pd/pd_batching.py:497: in _admit_prefilled
+    self.kv.write_span(r.slot, out.start, out.keys, out.values)
+_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
+
+self = <opt_prime.accel.pd.paged.PagedKV object at 0x73d6253e5f60>, slot = 0
+start = 0
+keys = tensor([[[[-1.3119e+00, -4.6383e-02,  4.9614e-01, -9.4488e-01, -4.3832e-01,
+            2.6910e-01,  6.3046e-01, -1.59...1,  1.3446e+00,
+            4.5665e-01,  1.3538e-01,  3.4665e-01, -1.7604e-01, -2.0072e-02,
+           -1.5641e+00]]]])
+values = tensor([[[[-3.2685e-01,  2.6668e-01,  4.5333e-03,  7.0952e-02,  1.8279e-01,
+            2.7009e-01,  1.4598e-01,  2.43...2, -9.8851e-02,
+           -5.3184e-02, -4.8559e-02,  9.7567e-02,  8.8385e-02,  8.4103e-02,
+           -3.2886e-02]]]])
+
+    def write_span(self, slot, start, keys, values):
+        """keys/values [layers, kv_heads, n, head_dim] for positions start .. start+n of `slot` (a list of those, one
+        per KV group, for hybrid models), each group in one indexed copy (the PD admission of KV that another engine
+        prefilled)"""
+        if isinstance(keys, torch.Tensor) and isinstance(values, torch.Tensor):
+            layers = max(self.layer_group) + 1
+            if keys.ndim != 4 or values.shape != keys.shape or keys.shape[0] != layers:
+>               raise ValueError("dense KV span must contain every model layer with matching K/V shapes")
+E               ValueError: dense KV span must contain every model layer with matching K/V shapes
+
+opt_prime/accel/pd/paged.py:454: ValueError
+=============================== warnings summary ===============================
+tests/test_pd_hybrid.py::test_batched_beyond_window_matches_hf[gemma3]
+  /home/gspark/venvs/pt213/lib/python3.10/site-packages/torch/nn/attention/flex_attention.py:2481: UserWarning: flex_attention called without torch.compile() - this will use an unfused implementation that materializes the full scores matrix instead of generating a fused kernel.
+
+  SOLUTION: Use torch.compile(flex_attention)(...)
+
+  If you want to debug your score_mod/mask_mod, you can set:
+  torch.nn.attention.flex_attention._FLEX_ATTENTION_DISABLE_COMPILE_DEBUG = True
+
+  This will allow you to use print statements or breakpoints. Note: This doesn't work with the backwards pass and may produce incorrect results.
+    _warn_once(
+
+-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html
+=========================== short test summary info ============================
+FAILED tests/test_pd_hybrid.py::test_recurrent_pd_hands_over_state[qwen3_5]
+FAILED tests/test_pd_hybrid.py::test_recurrent_pd_hands_over_state[qwen3_5_moe]
+2 failed, 28 passed, 1 warning in 26.53s
diff --git a/opt_prime/examples/pd/http_matrix/evidence258/dense258-full.log b/opt_prime/examples/pd/http_matrix/evidence258/dense258-full.log
new file mode 100644
index 00000000..8dacb4f1
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence258/dense258-full.log
@@ -0,0 +1,21 @@
+.sssssss.....................ssssssssss.....................................................s..... [ 18%]
+........................................................s.s............. [ 31%]
+....ss.....................ss....s...................................... [ 45%]
+........................................................................ [ 58%]
+....s.s...........................................s..................... [ 72%]
+................................................................... [ 84%]
+s.s...........................................................................s....    [100%]
+=============================== warnings summary ===============================
+tests/test_kvstore.py::test_second_engine_reuses_chunks_from_the_store[lmcache]
+  /home/gspark/venvs/pt213/lib/python3.10/site-packages/torch/nn/attention/flex_attention.py:2481: UserWarning: flex_attention called without torch.compile() - this will use an unfused implementation that materializes the full scores matrix instead of generating a fused kernel.
+
+  SOLUTION: Use torch.compile(flex_attention)(...)
+
+  If you want to debug your score_mod/mask_mod, you can set:
+  torch.nn.attention.flex_attention._FLEX_ATTENTION_DISABLE_COMPILE_DEBUG = True
+
+  This will allow you to use print statements or breakpoints. Note: This doesn't work with the backwards pass and may produce incorrect results.
+    _warn_once(
+
+-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html
+505 passed, 34 skipped, 1 warning, 253 subtests passed in 151.69s (0:02:31)
diff --git a/opt_prime/examples/pd/http_matrix/evidence258/dense258-red.log b/opt_prime/examples/pd/http_matrix/evidence258/dense258-red.log
new file mode 100644
index 00000000..adaedfa1
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence258/dense258-red.log
@@ -0,0 +1,132 @@
+FFF                                                                      [100%]
+=================================== FAILURES ===================================
+______________ test_dense_span_maps_model_layers_at_nonzero_start ______________
+
+    def test_dense_span_maps_model_layers_at_nonzero_start():
+        cache = pool()
+        keys = torch.arange(30).reshape(3, 1, 5, 2).float()
+>       cache.write_span(0, 3, keys, keys + 100)
+
+tests/test_pd_dense_hybrid.py:19:
+_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
+
+self = <opt_prime.accel.pd.paged.PagedKV object at 0x713eaa041540>, slot = 0
+start = 3
+keys = [tensor([[[[ 0.,  1.],
+          [ 2.,  3.],
+          [ 4.,  5.],
+          [ 6.,  7.],
+          [ 8.,  9.]]],
+
+
+   ...]],
+
+
+        [[[20., 21.],
+          [22., 23.],
+          [24., 25.],
+          [26., 27.],
+          [28., 29.]]]])]
+values = [tensor([[[[100., 101.],
+          [102., 103.],
+          [104., 105.],
+          [106., 107.],
+          [108., 109....    [[[120., 121.],
+          [122., 123.],
+          [124., 125.],
+          [126., 127.],
+          [128., 129.]]]])]
+
+    def write_span(self, slot, start, keys, values):
+        """keys/values [layers, kv_heads, n, head_dim] for positions start .. start+n of `slot` (a list of those, one
+        per KV group, for hybrid models), each group in one indexed copy (the PD admission of KV that another engine
+        prefilled)"""
+        keys = keys if isinstance(keys, (list, tuple)) else [keys]
+        values = values if isinstance(values, (list, tuple)) else [values]
+        idx = self.span_index(slot, start, start + keys[0].shape[2])
+        for pk, pv, k, v in zip(self.gk, self.gv, keys, values):
+>           pk[:, :, idx] = k.to(self.device, pk.dtype, non_blocking=True)
+E           RuntimeError: shape mismatch: value tensor of shape [3, 1, 5, 2] cannot be broadcast to indexing result of shape [2, 1, 5, 2]
+
+opt_prime/accel/pd/paged.py:455: RuntimeError
+______ test_invalid_grouped_span_rejected_before_any_write[missing_group] ______
+
+bad = 'missing_group'
+
+    @pytest.mark.parametrize('bad', ['missing_group', 'bad_later_shape'])
+    def test_invalid_grouped_span_rejected_before_any_write(bad):
+        cache = pool()
+        keys = [torch.ones(2, 1, 5, 2), torch.ones(1, 1, 5, 2)]
+        values = [x + 1 for x in keys]
+        if bad == 'missing_group':
+            keys, values = keys[:1], values[:1]
+        else:
+            values[1] = torch.ones(1, 1, 4, 2)
+>       with pytest.raises(ValueError):
+E       Failed: DID NOT RAISE ValueError
+
+tests/test_pd_dense_hybrid.py:37: Failed
+_____ test_invalid_grouped_span_rejected_before_any_write[bad_later_shape] _____
+
+bad = 'bad_later_shape'
+
+    @pytest.mark.parametrize('bad', ['missing_group', 'bad_later_shape'])
+    def test_invalid_grouped_span_rejected_before_any_write(bad):
+        cache = pool()
+        keys = [torch.ones(2, 1, 5, 2), torch.ones(1, 1, 5, 2)]
+        values = [x + 1 for x in keys]
+        if bad == 'missing_group':
+            keys, values = keys[:1], values[:1]
+        else:
+            values[1] = torch.ones(1, 1, 4, 2)
+        with pytest.raises(ValueError):
+>           cache.write_span(0, 0, keys, values)
+
+tests/test_pd_dense_hybrid.py:38:
+_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
+
+self = <opt_prime.accel.pd.paged.PagedKV object at 0x713eaa0800a0>, slot = 0
+start = 0
+keys = [tensor([[[[1., 1.],
+          [1., 1.],
+          [1., 1.],
+          [1., 1.],
+          [1., 1.]]],
+
+
+        [[[1....  [1., 1.]]]]), tensor([[[[1., 1.],
+          [1., 1.],
+          [1., 1.],
+          [1., 1.],
+          [1., 1.]]]])]
+values = [tensor([[[[2., 2.],
+          [2., 2.],
+          [2., 2.],
+          [2., 2.],
+          [2., 2.]]],
+
+
+        [[[2....  [2., 2.],
+          [2., 2.]]]]), tensor([[[[1., 1.],
+          [1., 1.],
+          [1., 1.],
+          [1., 1.]]]])]
+
+    def write_span(self, slot, start, keys, values):
+        """keys/values [layers, kv_heads, n, head_dim] for positions start .. start+n of `slot` (a list of those, one
+        per KV group, for hybrid models), each group in one indexed copy (the PD admission of KV that another engine
+        prefilled)"""
+        keys = keys if isinstance(keys, (list, tuple)) else [keys]
+        values = values if isinstance(values, (list, tuple)) else [values]
+        idx = self.span_index(slot, start, start + keys[0].shape[2])
+        for pk, pv, k, v in zip(self.gk, self.gv, keys, values):
+            pk[:, :, idx] = k.to(self.device, pk.dtype, non_blocking=True)
+>           pv[:, :, idx] = v.to(self.device, pv.dtype, non_blocking=True)
+E           RuntimeError: shape mismatch: value tensor of shape [4, 2] cannot be broadcast to indexing result of shape [1, 1, 5, 2]
+
+opt_prime/accel/pd/paged.py:456: RuntimeError
+=========================== short test summary info ============================
+FAILED tests/test_pd_dense_hybrid.py::test_dense_span_maps_model_layers_at_nonzero_start
+FAILED tests/test_pd_dense_hybrid.py::test_invalid_grouped_span_rejected_before_any_write[missing_group]
+FAILED tests/test_pd_dense_hybrid.py::test_invalid_grouped_span_rejected_before_any_write[bad_later_shape]
+3 failed in 2.48s
diff --git a/opt_prime/examples/pd/http_matrix/evidence258/local201-gemma-matrix-a.json b/opt_prime/examples/pd/http_matrix/evidence258/local201-gemma-matrix-a.json
new file mode 100644
index 00000000..6deb6f56
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence258/local201-gemma-matrix-a.json
@@ -0,0 +1,8397 @@
+{
+  "passed": false,
+  "model": "Gemma3-1B",
+  "mode": "pd",
+  "dtype": "bfloat16",
+  "torch": "2.13.0+cu130",
+  "transformers": "5.15.0",
+  "replicas": 1,
+  "cpu_threads_per_server": 4,
+  "prompts": {
+    "128": [
+      2,
+      2094,
+      563,
+      1695,
+      4403,
+      236761,
+      669,
+      2604,
+      3673,
+      6097,
+      36157,
+      236761,
+      669,
+      3730,
+      3673,
+      6097,
+      6249,
+      236761,
+      107,
+      2094,
+      563,
+      1695,
+      4403,
+      236761,
+      669,
+      2604,
+      3673,
+      6097,
+      36157,
+      236761,
+      669,
+      3730,
+      3673,
+      6097,
+      6249,
+      236761,
+      107,
+      2094,
+      563,
+      1695,
+      4403,
+      236761,
+      669,
+      2604,
+      3673,
+      6097,
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diff --git a/opt_prime/examples/pd/http_matrix/evidence258/local201-gemma-matrix-a.server0.log b/opt_prime/examples/pd/http_matrix/evidence258/local201-gemma-matrix-a.server0.log
new file mode 100644
index 00000000..9d22f638
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence258/local201-gemma-matrix-a.server0.log
@@ -0,0 +1,24 @@
+
+Loading weights:   0%|          | 0/340 [00:00<?, ?it/s]
+Loading weights: 100%|██████████| 340/340 [00:00<00:00, 8560.83it/s]
+Traceback (most recent call last):
+  File "<string>", line 4, in <module>
+  File "/usr/lib/python3.10/runpy.py", line 289, in run_path
+    return _run_module_code(code, init_globals, run_name,
+  File "/usr/lib/python3.10/runpy.py", line 96, in _run_module_code
+    _run_code(code, mod_globals, init_globals,
+  File "/usr/lib/python3.10/runpy.py", line 86, in _run_code
+    exec(code, run_globals)
+  File "/home/gspark/workspace/aicomp-atom-prefetch-253/opt_prime/examples/pd/serve_batched.py", line 152, in <module>
+    owner.run()                                   # owner loop on the main thread
+  File "/home/gspark/workspace/aicomp-atom-prefetch-253/opt_prime/opt_prime/accel/serve/batched.py", line 217, in run
+    b.step()
+  File "/home/gspark/workspace/aicomp-atom-prefetch-253/opt_prime/opt_prime/accel/pd/pd_batching.py", line 468, in step
+    self._admit_prefilled(block=not self.decoding)
+  File "/home/gspark/workspace/aicomp-atom-prefetch-253/opt_prime/opt_prime/accel/pd/pd_batching.py", line 497, in _admit_prefilled
+    self.kv.write_span(r.slot, out.start, out.keys, out.values)
+  File "/home/gspark/workspace/aicomp-atom-prefetch-253/opt_prime/opt_prime/accel/pd/paged.py", line 455, in write_span
+    pk[:, :, idx] = k.to(self.device, pk.dtype, non_blocking=True)
+RuntimeError: shape mismatch: value tensor of shape [26, 1, 128, 256] cannot be broadcast to indexing result of shape [22, 1, 128, 256]
+/usr/lib/python3.10/multiprocessing/resource_tracker.py:224: UserWarning: resource_tracker: There appear to be 1 leaked shared_memory objects to clean up at shutdown
+  warnings.warn('resource_tracker: There appear to be %d '
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/atom-edge201-final-summary.json b/opt_prime/examples/pd/http_matrix/evidence265/atom-edge201-final-summary.json
new file mode 100644
index 00000000..25f36002
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/atom-edge201-final-summary.json
@@ -0,0 +1,1669 @@
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+          "all_text_equal": true
+        },
+        {
+          "input_tokens": 512,
+          "output_tokens_per_request": 32,
+          "concurrency": 4,
+          "requests": 4,
+          "output_tokens": 128,
+          "wall_s": 20.567025867057964,
+          "output_tok_s": 6.223554189476493,
+          "median_first_sse_s": 10.92611628700979,
+          "median_tpot_est_s": 0.06727996551912399,
+          "median_e2e_s": 13.011727488017641,
+          "all_text_equal": true
+        },
+        {
+          "input_tokens": 512,
+          "output_tokens_per_request": 128,
+          "concurrency": 1,
+          "requests": 1,
+          "output_tokens": 128,
+          "wall_s": 11.5852806980256,
+          "output_tok_s": 11.048502262169114,
+          "median_first_sse_s": 3.0284611789975315,
+          "median_tpot_est_s": 0.06737519739430368,
+          "median_e2e_s": 11.5851112480741,
+          "all_text_equal": true
+        },
+        {
+          "input_tokens": 512,
+          "output_tokens_per_request": 128,
+          "concurrency": 4,
+          "requests": 4,
+          "output_tokens": 512,
+          "wall_s": 46.390732183121145,
+          "output_tok_s": 11.036687198187543,
+          "median_first_sse_s": 20.51317862002179,
+          "median_tpot_est_s": 0.06724255512593183,
+          "median_e2e_s": 29.054801348480396,
+          "all_text_equal": true
+        },
+        {
+          "input_tokens": 2048,
+          "output_tokens_per_request": 32,
+          "concurrency": 1,
+          "requests": 1,
+          "output_tokens": 32,
+          "wall_s": 5.288444323930889,
+          "output_tok_s": 6.05092878735546,
+          "median_first_sse_s": 3.1799436199944466,
+          "median_tpot_est_s": 0.06801005105878558,
+          "median_e2e_s": 5.288255202816799,
+          "all_text_equal": true
+        },
+        {
+          "input_tokens": 2048,
+          "output_tokens_per_request": 32,
+          "concurrency": 4,
+          "requests": 4,
+          "output_tokens": 128,
+          "wall_s": 20.99751231400296,
+          "output_tok_s": 6.095960230233488,
+          "median_first_sse_s": 11.01778218743857,
+          "median_tpot_est_s": 0.06800803582319209,
+          "median_e2e_s": 13.122557741589844,
+          "all_text_equal": true
+        },
+        {
+          "input_tokens": 2048,
+          "output_tokens_per_request": 128,
+          "concurrency": 1,
+          "requests": 1,
+          "output_tokens": 128,
+          "wall_s": 11.857091940008104,
+          "output_tok_s": 10.795227079930402,
+          "median_first_sse_s": 3.204868826782331,
+          "median_tpot_est_s": 0.06812688143461884,
+          "median_e2e_s": 11.856982768978924,
+          "all_text_equal": true
+        },
+        {
+          "input_tokens": 2048,
+          "output_tokens_per_request": 128,
+          "concurrency": 4,
+          "requests": 4,
+          "output_tokens": 512,
+          "wall_s": 47.14957190398127,
+          "output_tok_s": 10.859059357795934,
+          "median_first_sse_s": 20.88058990763966,
+          "median_tpot_est_s": 0.06818704466485806,
+          "median_e2e_s": 29.546962124528363,
+          "all_text_equal": true
+        }
+      ]
+    }
+  },
+  "limits": "Historical stream265 runtime with final PR266 HTTP override, one ATOM decode lane; c4 queued clients, cache off, no current-main/cacheon/physicalB4 certification.",
+  "whole_stream_reference_tokens_equal": {
+    "qwen06b": {
+      "512:32": true,
+      "512:128": true,
+      "2048:32": true,
+      "2048:128": true
+    },
+    "qwen4b": {
+      "512:32": true,
+      "512:128": true,
+      "2048:32": true,
+      "2048:128": true
+    },
+    "gemma": {
+      "512:32": true,
+      "512:128": true,
+      "2048:32": true,
+      "2048:128": true
+    },
+    "koni": {
+      "512:32": true,
+      "512:128": true,
+      "2048:32": true,
+      "2048:128": true
+    }
+  },
+  "native_differences": {
+    "qwen06b": {
+      "eos_ids": [
+        151643,
+        151645
+      ],
+      "references": {
+        "512:32": {
+          "equal": false,
+          "first_difference_index": 1,
+          "native_eos_index": null,
+          "whole_eos_index": null,
+          "equal_through_same_eos": false,
+          "native_token": 594,
+          "whole_token": 374
+        },
+        "512:128": {
+          "equal": false,
+          "first_difference_index": 1,
+          "native_eos_index": null,
+          "whole_eos_index": null,
+          "equal_through_same_eos": false,
+          "native_token": 594,
+          "whole_token": 374
+        },
+        "2048:32": {
+          "equal": true,
+          "first_difference_index": null,
+          "native_eos_index": null,
+          "whole_eos_index": null,
+          "equal_through_same_eos": false,
+          "native_token": null,
+          "whole_token": null
+        },
+        "2048:128": {
+          "equal": true,
+          "first_difference_index": null,
+          "native_eos_index": null,
+          "whole_eos_index": null,
+          "equal_through_same_eos": false,
+          "native_token": null,
+          "whole_token": null
+        }
+      }
+    },
+    "qwen4b": {
+      "eos_ids": [
+        151643,
+        151645
+      ],
+      "references": {
+        "512:32": {
+          "equal": true,
+          "first_difference_index": null,
+          "native_eos_index": null,
+          "whole_eos_index": null,
+          "equal_through_same_eos": false,
+          "native_token": null,
+          "whole_token": null
+        },
+        "512:128": {
+          "equal": false,
+          "first_difference_index": 50,
+          "native_eos_index": null,
+          "whole_eos_index": null,
+          "equal_through_same_eos": false,
+          "native_token": 4829,
+          "whole_token": 2299
+        },
+        "2048:32": {
+          "equal": true,
+          "first_difference_index": null,
+          "native_eos_index": null,
+          "whole_eos_index": null,
+          "equal_through_same_eos": false,
+          "native_token": null,
+          "whole_token": null
+        },
+        "2048:128": {
+          "equal": false,
+          "first_difference_index": 61,
+          "native_eos_index": null,
+          "whole_eos_index": null,
+          "equal_through_same_eos": false,
+          "native_token": 1366,
+          "whole_token": 5230
+        }
+      }
+    },
+    "gemma": {
+      "eos_ids": [
+        1,
+        106
+      ],
+      "references": {
+        "512:32": {
+          "equal": true,
+          "first_difference_index": null,
+          "native_eos_index": 2,
+          "whole_eos_index": 2,
+          "equal_through_same_eos": true,
+          "native_token": null,
+          "whole_token": null
+        },
+        "512:128": {
+          "equal": true,
+          "first_difference_index": null,
+          "native_eos_index": 2,
+          "whole_eos_index": 2,
+          "equal_through_same_eos": true,
+          "native_token": null,
+          "whole_token": null
+        },
+        "2048:32": {
+          "equal": true,
+          "first_difference_index": null,
+          "native_eos_index": 2,
+          "whole_eos_index": 2,
+          "equal_through_same_eos": true,
+          "native_token": null,
+          "whole_token": null
+        },
+        "2048:128": {
+          "equal": true,
+          "first_difference_index": null,
+          "native_eos_index": 2,
+          "whole_eos_index": 2,
+          "equal_through_same_eos": true,
+          "native_token": null,
+          "whole_token": null
+        }
+      }
+    },
+    "koni": {
+      "eos_ids": [
+        128001,
+        128009
+      ],
+      "references": {
+        "512:32": {
+          "equal": true,
+          "first_difference_index": null,
+          "native_eos_index": null,
+          "whole_eos_index": null,
+          "equal_through_same_eos": false,
+          "native_token": null,
+          "whole_token": null
+        },
+        "512:128": {
+          "equal": true,
+          "first_difference_index": null,
+          "native_eos_index": null,
+          "whole_eos_index": null,
+          "equal_through_same_eos": false,
+          "native_token": null,
+          "whole_token": null
+        },
+        "2048:32": {
+          "equal": true,
+          "first_difference_index": null,
+          "native_eos_index": null,
+          "whole_eos_index": null,
+          "equal_through_same_eos": false,
+          "native_token": null,
+          "whole_token": null
+        },
+        "2048:128": {
+          "equal": true,
+          "first_difference_index": null,
+          "native_eos_index": null,
+          "whole_eos_index": null,
+          "equal_through_same_eos": false,
+          "native_token": null,
+          "whole_token": null
+        }
+      }
+    }
+  },
+  "state": "complete",
+  "completed_arms": 12
+}
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/atom-mid201-single-client-latency.json b/opt_prime/examples/pd/http_matrix/evidence265/atom-mid201-single-client-latency.json
new file mode 100644
index 00000000..b5b820d8
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/atom-mid201-single-client-latency.json
@@ -0,0 +1,85 @@
+{
+  "scope": "Single client, one timing batch; first SSE and estimated TPOT are not device kernel times.",
+  "models": {
+    "qwen06b": {
+      "native": {
+        "median_first_sse_s": 0.1879055928438902,
+        "median_tpot_est_s": 0.007809569536686296,
+        "median_e2e_s": 1.1797209240030497,
+        "output_tok_s": 108.48487358883817
+      },
+      "whole": {
+        "median_first_sse_s": 1.828077691141516,
+        "median_tpot_est_s": 0.007671130628611042,
+        "median_e2e_s": 2.8023112809751183,
+        "output_tok_s": 45.67517826810287
+      },
+      "stream": {
+        "median_first_sse_s": 1.011592352995649,
+        "median_tpot_est_s": 0.007834264731738629,
+        "median_e2e_s": 2.006543973926455,
+        "output_tok_s": 63.78602864998999
+      }
+    },
+    "qwen4b": {
+      "native": {
+        "median_first_sse_s": 1.0221819749567658,
+        "median_tpot_est_s": 0.03876677221310596,
+        "median_e2e_s": 5.945562046021223,
+        "output_tok_s": 21.528127850160352
+      },
+      "whole": {
+        "median_first_sse_s": 3.411107431864366,
+        "median_tpot_est_s": 0.03858469991292071,
+        "median_e2e_s": 8.311364320805296,
+        "output_tok_s": 15.400250446373338
+      },
+      "stream": {
+        "median_first_sse_s": 2.533690683078021,
+        "median_tpot_est_s": 0.038604193873058155,
+        "median_e2e_s": 7.436423304956406,
+        "output_tok_s": 17.21219579134921
+      }
+    },
+    "gemma": {
+      "native": {
+        "median_first_sse_s": 0.19625086686573923,
+        "median_tpot_est_s": 0.010694988110418044,
+        "median_e2e_s": 1.5545143568888307,
+        "output_tok_s": 82.33355325003127
+      },
+      "whole": {
+        "median_first_sse_s": 1.6123445159755647,
+        "median_tpot_est_s": 0.01075632953429668,
+        "median_e2e_s": 2.978398366831243,
+        "output_tok_s": 42.973727587950414
+      },
+      "stream": {
+        "median_first_sse_s": 1.5110139569733292,
+        "median_tpot_est_s": 0.010462222070851195,
+        "median_e2e_s": 2.839716159971431,
+        "output_tok_s": 45.07239711661869
+      }
+    },
+    "koni": {
+      "native": {
+        "median_first_sse_s": 1.6276817580219358,
+        "median_tpot_est_s": 0.06816972989046316,
+        "median_e2e_s": 10.285237454110757,
+        "output_tok_s": 12.444834751504189
+      },
+      "whole": {
+        "median_first_sse_s": 3.5296944479923695,
+        "median_tpot_est_s": 0.06797795922781422,
+        "median_e2e_s": 12.162895269924775,
+        "output_tok_s": 10.523703461849538
+      },
+      "stream": {
+        "median_first_sse_s": 3.204868826782331,
+        "median_tpot_est_s": 0.06812688143461884,
+        "median_e2e_s": 11.856982768978924,
+        "output_tok_s": 10.795227079930402
+      }
+    }
+  }
+}
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/audit_atom_edge201.py b/opt_prime/examples/pd/http_matrix/evidence265/audit_atom_edge201.py
new file mode 100644
index 00000000..b00f0be6
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/audit_atom_edge201.py
@@ -0,0 +1,61 @@
+import argparse,hashlib,json,math,pathlib,statistics
+parser=argparse.ArgumentParser(description="Audit saved ATOM native/whole/stream short/long measurements")
+parser.add_argument("artifacts",type=pathlib.Path)
+args=parser.parse_args()
+b=args.artifacts.resolve();q=json.loads((b/'edge201-atom1030-queue.json').read_text())
+completed=[x for x in q['runs'] if x.get('exit_code')==0 and x.get('passed') and x.get('parity_passed')]
+planned={(model,mode) for model in ['qwen06b','qwen4b','gemma','koni'] for mode in ['native','whole','stream']}
+keys=[(r['model'],r['mode']) for r in q['runs']]
+assert len(keys)==len(set(keys)) and set(keys)<=planned
+if q['state']=='complete':assert len(completed)==12 and set(keys)==planned
+expected={(n,o,c) for n in [128,8192] for o in [32,128] for c in [1,4]}
+report={'runtime_scope':q['runtime_scope'],'queue_sha256':hashlib.sha256((b/'edge201-atom1030-queue.json').read_bytes()).hexdigest(),'sources':q['source_hashes'],'models':{},'limits':'Historical stream265 runtime with final PR266 HTTP override, one ATOM decode lane; c4 queued clients, cache off, no current-main/cacheon/physicalB4 certification.'}
+for run in completed:
+ model=run['model'];mode=run['mode'];p=b/f'edge201-atom-final-{model}-{mode}.json';d=json.loads(p.read_text());oldpath=b/f'stream265-{model}-http-atom-c16-native.json';old=json.loads(oldpath.read_text()) if oldpath.exists() else None
+ previous_path=b/f'stream265-{model}-http-atom-c16-{mode}.json'
+ previous_pd=json.loads(previous_path.read_text()) if previous_path.exists() else None
+ assert d['passed'] and d['parity_passed'] and d['mode']==('native' if mode=='native' else 'pd')
+ assert d['physical_gpus']==0 and d['physical_rngd']==int(mode!='native') and d['physical_atom']==1 and d['slots_per_decoder']==1 and d['context']==9216
+ assert d['producer_cache'] is False and d['decoder_cache'] is False
+ assert len(d['warmups'])==4 and len(d['correctness_batches'])==len(d['batches'])==8
+ assert {tuple(map(int,r['id'].split('-')[-2:])) for r in d['warmups']}=={(n,o) for n in [128,8192] for o in [32,128]}
+ assert set(d['references'])=={f'{n}:{o}' for n in [128,8192] for o in [32,128]}
+ assert {(x['input_tokens'],x['output_tokens'],x['concurrency']) for x in d['correctness_batches']}==expected
+ assert {(x['input_tokens'],x['output_tokens_per_request'],x['concurrency']) for x in d['batches']}==expected
+ for n,ids in d['prompts'].items():assert len(ids)==int(n) and (old is None or old['prompts'][n]==ids)
+ for warm in d['warmups']:
+  n,o=map(int,warm['id'].split('-')[-2:]);ref=d['references'][f'{n}:{o}']
+  assert warm['tokens_equal'] and warm['tokens']==ref['tokens'] and len(warm['tokens'])==o and warm['text']==ref['text']
+ for batch in d['correctness_batches']:
+  n,o,c=batch['input_tokens'],batch['output_tokens'],batch['concurrency'];ref=d['references'][f'{n}:{o}'];assert len(batch['rows'])==c
+  assert all(r['tokens_equal'] and r['tokens']==ref['tokens'] and len(r['tokens'])==o for r in batch['rows'])
+ for batch in d['batches']:
+  n,o,c=batch['input_tokens'],batch['output_tokens_per_request'],batch['concurrency'];ref=d['references'][f'{n}:{o}'];rows=batch['rows'];s=batch['summary'];assert len(rows)==c
+  assert all(r['text_equal'] and r['text']==ref['text'] and r['output_tokens']==o and 0<r['first_sse_s']<=r['e2e_s'] for r in rows)
+  assert math.isclose(s['output_tok_s'],c*o/s['wall_s'],rel_tol=1e-10)
+  assert math.isclose(s['median_first_sse_s'],statistics.median(r['first_sse_s'] for r in rows),rel_tol=1e-10)
+  assert math.isclose(s['median_e2e_s'],statistics.median(r['e2e_s'] for r in rows),rel_tol=1e-10)
+ report['models'][model+'/'+mode]={'raw_sha256':hashlib.sha256(p.read_bytes()).hexdigest(),'historical_c16_same_route_reference_tokens_equal':({k:v['tokens']==previous_pd['references'][k]['tokens'] for k,v in d['references'].items()} if previous_pd and previous_pd.get('passed') and all(k in previous_pd.get('references',{}) for k in d['references']) else None),'conditions':8,'warmups':4,'token_checks':sum(len(x['rows']) for x in d['correctness_batches']),'sse_checks':sum(len(x['rows']) for x in d['batches']),'historical_c16_native_reference_tokens_equal':({k:v['tokens']==old['references'][k]['tokens'] for k,v in d['references'].items()} if old and old.get('passed') and all(k in old.get('references',{}) for k in d['references']) else None),'measurements':[{'input_tokens':x['input_tokens'],'output_tokens_per_request':x['output_tokens_per_request'],'concurrency':x['concurrency'],**x['summary']} for x in d['batches']]}
+report['whole_stream_reference_tokens_equal']={}
+for model in ['qwen06b','qwen4b','gemma','koni']:
+ if not all(any(r['model']==model and r['mode']==mode for r in completed) for mode in ['native','whole','stream']):continue
+ whole=json.loads((b/f'edge201-atom-final-{model}-whole.json').read_text());stream=json.loads((b/f'edge201-atom-final-{model}-stream.json').read_text());native=json.loads((b/f'edge201-atom-final-{model}-native.json').read_text())
+ assert whole['prompts']==stream['prompts']==native['prompts']
+ eq={k:v['tokens']==stream['references'][k]['tokens'] for k,v in whole['references'].items()};assert all(eq.values());report['whole_stream_reference_tokens_equal'][model]=eq
+ report['models'][model+'/whole']['same_run_native_reference_tokens_equal']={k:v['tokens']==native['references'][k]['tokens'] for k,v in whole['references'].items()}
+report['native_differences']={}
+metadata=json.loads((b/'mid201-atom-final-artifact-metadata.json').read_text())
+for model in ['qwen06b','qwen4b','gemma','koni']:
+ if not all(any(r['model']==model and r['mode']==mode for r in completed) for mode in ['native','whole']):continue
+ native=json.loads((b/f'edge201-atom-final-{model}-native.json').read_text());whole=json.loads((b/f'edge201-atom-final-{model}-whole.json').read_text())
+ eos=metadata[model]['generation_config']['eos_token_id'];eos=set(eos if isinstance(eos,list) else [eos])
+ if model=='koni':eos.update(x['id'] for x in json.loads((b/'mid201-atom-final-koni-eos.json').read_text()))
+ details={}
+ for key,ref in native['references'].items():
+  a=ref['tokens'];z=whole['references'][key]['tokens'];idx=next((i for i,(x,y) in enumerate(zip(a,z)) if x!=y),None)
+  ea=next((i for i,x in enumerate(a) if x in eos),None);ez=next((i for i,x in enumerate(z) if x in eos),None)
+  details[key]={'equal':a==z,'first_difference_index':idx,'native_eos_index':ea,'whole_eos_index':ez,'equal_through_same_eos':ea is not None and ea==ez and a[:ea+1]==z[:ez+1],'native_token':a[idx] if idx is not None else None,'whole_token':z[idx] if idx is not None else None}
+ report['native_differences'][model]={'eos_ids':sorted(eos),'references':details}
+report['state']=q['state'];report['completed_arms']=len(completed)
+(b/'edge201-atom-final-summary.json').write_text(json.dumps(report,indent=2)+'\n')
+print({k:{a:v for a,v in r.items() if a!='measurements'} for k,r in report['models'].items()})
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/audit_atom_mid201.py b/opt_prime/examples/pd/http_matrix/evidence265/audit_atom_mid201.py
new file mode 100644
index 00000000..e9ace109
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/audit_atom_mid201.py
@@ -0,0 +1,59 @@
+import argparse,hashlib,json,math,pathlib,statistics
+parser=argparse.ArgumentParser(description="Audit saved ATOM native/whole/stream intermediate-length measurements")
+parser.add_argument("artifacts",type=pathlib.Path)
+args=parser.parse_args()
+b=args.artifacts.resolve();q=json.loads((b/'mid201-atom-final1030-queue.json').read_text())
+completed=[x for x in q['runs'] if x.get('exit_code')==0 and x.get('passed') and x.get('parity_passed')]
+planned={(model,mode) for model in ['qwen06b','qwen4b','gemma','koni'] for mode in ['native','whole','stream']}
+keys=[(r['model'],r['mode']) for r in q['runs']]
+assert len(keys)==len(set(keys)) and set(keys)<=planned
+if q['state']=='complete':assert len(completed)==12 and set(keys)==planned
+expected={(n,o,c) for n in [512,2048] for o in [32,128] for c in [1,4]}
+report={'runtime_scope':q['runtime_scope'],'queue_sha256':hashlib.sha256((b/'mid201-atom-final1030-queue.json').read_bytes()).hexdigest(),'sources':q['source_hashes'],'models':{},'limits':'Historical stream265 runtime with final PR266 HTTP override, one ATOM decode lane; c4 queued clients, cache off, no current-main/cacheon/physicalB4 certification.'}
+for run in completed:
+ model=run['model'];mode=run['mode'];p=b/f'mid201-atom-final-{model}-{mode}.json';d=json.loads(p.read_text());oldpath=b/f'stream265-{model}-http-atom-c16-native.json';old=json.loads(oldpath.read_text()) if oldpath.exists() else None
+ previous_path=b/f'stream265-{model}-http-atom-c16-{mode}.json'
+ previous_pd=json.loads(previous_path.read_text()) if previous_path.exists() else None
+ assert d['passed'] and d['parity_passed'] and d['mode']==('native' if mode=='native' else 'pd')
+ assert d['physical_gpus']==0 and d['physical_rngd']==int(mode!='native') and d['physical_atom']==1 and d['slots_per_decoder']==1 and d['context']==9216
+ assert d['producer_cache'] is False and d['decoder_cache'] is False
+ assert len(d['warmups'])==4 and len(d['correctness_batches'])==len(d['batches'])==8
+ assert {(x['input_tokens'],x['output_tokens'],x['concurrency']) for x in d['correctness_batches']}==expected
+ assert {(x['input_tokens'],x['output_tokens_per_request'],x['concurrency']) for x in d['batches']}==expected
+ for n,ids in d['prompts'].items():assert len(ids)==int(n) and (old is None or old['prompts'][n]==ids)
+ for warm in d['warmups']:
+  n,o=map(int,warm['id'].split('-')[-2:]);ref=d['references'][f'{n}:{o}']
+  assert warm['tokens_equal'] and warm['tokens']==ref['tokens'] and len(warm['tokens'])==o and warm['text']==ref['text']
+ for batch in d['correctness_batches']:
+  n,o,c=batch['input_tokens'],batch['output_tokens'],batch['concurrency'];ref=d['references'][f'{n}:{o}'];assert len(batch['rows'])==c
+  assert all(r['tokens_equal'] and r['tokens']==ref['tokens'] and len(r['tokens'])==o for r in batch['rows'])
+ for batch in d['batches']:
+  n,o,c=batch['input_tokens'],batch['output_tokens_per_request'],batch['concurrency'];ref=d['references'][f'{n}:{o}'];rows=batch['rows'];s=batch['summary'];assert len(rows)==c
+  assert all(r['text_equal'] and r['text']==ref['text'] and r['output_tokens']==o and 0<r['first_sse_s']<=r['e2e_s'] for r in rows)
+  assert math.isclose(s['output_tok_s'],c*o/s['wall_s'],rel_tol=1e-10)
+  assert math.isclose(s['median_first_sse_s'],statistics.median(r['first_sse_s'] for r in rows),rel_tol=1e-10)
+  assert math.isclose(s['median_e2e_s'],statistics.median(r['e2e_s'] for r in rows),rel_tol=1e-10)
+ report['models'][model+'/'+mode]={'raw_sha256':hashlib.sha256(p.read_bytes()).hexdigest(),'historical_c16_same_route_reference_tokens_equal':({k:v['tokens']==previous_pd['references'][k]['tokens'] for k,v in d['references'].items()} if previous_pd and previous_pd.get('passed') and all(k in previous_pd.get('references',{}) for k in d['references']) else None),'conditions':8,'warmups':4,'token_checks':sum(len(x['rows']) for x in d['correctness_batches']),'sse_checks':sum(len(x['rows']) for x in d['batches']),'historical_c16_native_reference_tokens_equal':({k:v['tokens']==old['references'][k]['tokens'] for k,v in d['references'].items()} if old and old.get('passed') and all(k in old.get('references',{}) for k in d['references']) else None),'measurements':[{'input_tokens':x['input_tokens'],'output_tokens_per_request':x['output_tokens_per_request'],'concurrency':x['concurrency'],**x['summary']} for x in d['batches']]}
+report['whole_stream_reference_tokens_equal']={}
+for model in ['qwen06b','qwen4b','gemma','koni']:
+ if not all(any(r['model']==model and r['mode']==mode for r in completed) for mode in ['native','whole','stream']):continue
+ whole=json.loads((b/f'mid201-atom-final-{model}-whole.json').read_text());stream=json.loads((b/f'mid201-atom-final-{model}-stream.json').read_text());native=json.loads((b/f'mid201-atom-final-{model}-native.json').read_text())
+ assert whole['prompts']==stream['prompts']==native['prompts']
+ eq={k:v['tokens']==stream['references'][k]['tokens'] for k,v in whole['references'].items()};assert all(eq.values());report['whole_stream_reference_tokens_equal'][model]=eq
+ report['models'][model+'/whole']['same_run_native_reference_tokens_equal']={k:v['tokens']==native['references'][k]['tokens'] for k,v in whole['references'].items()}
+report['native_differences']={}
+metadata=json.loads((b/'mid201-atom-final-artifact-metadata.json').read_text())
+for model in ['qwen06b','qwen4b','gemma','koni']:
+ if not all(any(r['model']==model and r['mode']==mode for r in completed) for mode in ['native','whole']):continue
+ native=json.loads((b/f'mid201-atom-final-{model}-native.json').read_text());whole=json.loads((b/f'mid201-atom-final-{model}-whole.json').read_text())
+ eos=metadata[model]['generation_config']['eos_token_id'];eos=set(eos if isinstance(eos,list) else [eos])
+ if model=='koni':eos.update(x['id'] for x in json.loads((b/'mid201-atom-final-koni-eos.json').read_text()))
+ details={}
+ for key,ref in native['references'].items():
+  a=ref['tokens'];z=whole['references'][key]['tokens'];idx=next((i for i,(x,y) in enumerate(zip(a,z)) if x!=y),None)
+  ea=next((i for i,x in enumerate(a) if x in eos),None);ez=next((i for i,x in enumerate(z) if x in eos),None)
+  details[key]={'equal':a==z,'first_difference_index':idx,'native_eos_index':ea,'whole_eos_index':ez,'equal_through_same_eos':ea is not None and ea==ez and a[:ea+1]==z[:ez+1],'native_token':a[idx] if idx is not None else None,'whole_token':z[idx] if idx is not None else None}
+ report['native_differences'][model]={'eos_ids':sorted(eos),'references':details}
+report['state']=q['state'];report['completed_arms']=len(completed)
+(b/'mid201-atom-final-summary.json').write_text(json.dumps(report,indent=2)+'\n')
+print({k:{a:v for a,v in r.items() if a!='measurements'} for k,r in report['models'].items()})
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/audit_gpu_edge201.py b/opt_prime/examples/pd/http_matrix/evidence265/audit_gpu_edge201.py
new file mode 100644
index 00000000..7c5de1ba
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/audit_gpu_edge201.py
@@ -0,0 +1,40 @@
+import argparse,hashlib,json,math,pathlib,statistics
+parser=argparse.ArgumentParser(description="Audit native GPU short/long HTTP measurements")
+parser.add_argument("artifacts",type=pathlib.Path)
+args=parser.parse_args()
+b=args.artifacts.resolve();q=json.loads((b/'edge201-gpu-queue.json').read_text())
+assert q['state'] in ['running','complete','failed']
+completed=[r for r in q['runs'] if r.get('exit_code')==0]
+if q['state']=='complete':assert len(completed)==4
+planned={'qwen06b','qwen4b','gemma','koni'}
+keys=[r['model'] for r in q['runs']]
+assert len(keys)==len(set(keys)) and set(keys)<=planned
+if q['state']=='complete':assert set(keys)==planned
+expected={(n,o,c) for n in [128,8192] for o in [32,128] for c in [1,4]}
+report={'runtime_commit':q['runtime_commit'],'queue_sha256':hashlib.sha256((b/'edge201-gpu-queue.json').read_bytes()).hexdigest(),'sources':q['sources'],'models':{},'limits':'Historical experimental runtime, cache off, one timing batch per condition; not current-main certification, cache-on coverage or a speedup comparison.'}
+for run in completed:
+ model=run['model'];p=b/f'edge201-{model}-gpu-native.json';d=json.loads(p.read_text());old=json.loads((b/f'stream265-{model}-http-c16-native.json').read_text())
+ assert d['passed'] and d['parity_passed'] and d['mode']=='native'
+ assert d['physical_gpus']==1 and d['physical_rngd']==d['physical_atom']==0 and d['slots_per_decoder']==4 and d['context']==9216
+ assert d['producer_cache'] is False and d['decoder_cache'] is False
+ assert len(d['warmups'])==4 and len(d['correctness_batches'])==len(d['batches'])==8
+ assert {(x['input_tokens'],x['output_tokens'],x['concurrency']) for x in d['correctness_batches']}==expected
+ assert {(x['input_tokens'],x['output_tokens_per_request'],x['concurrency']) for x in d['batches']}==expected
+ for n,ids in d['prompts'].items():assert len(ids)==int(n) and old['prompts'][n]==ids
+ for warm in d['warmups']:
+  n,o=map(int,warm['id'].split('-')[-2:]);ref=d['references'][f'{n}:{o}']
+  assert warm['tokens_equal'] and warm['tokens']==ref['tokens'] and len(warm['tokens'])==o and warm['text']==ref['text']
+ for batch in d['correctness_batches']:
+  n,o,c=batch['input_tokens'],batch['output_tokens'],batch['concurrency'];ref=d['references'][f'{n}:{o}'];assert len(batch['rows'])==c
+  assert all(r['tokens_equal'] and r['tokens']==ref['tokens'] and len(r['tokens'])==o for r in batch['rows'])
+ for batch in d['batches']:
+  n,o,c=batch['input_tokens'],batch['output_tokens_per_request'],batch['concurrency'];ref=d['references'][f'{n}:{o}'];rows=batch['rows'];s=batch['summary'];assert len(rows)==c
+  assert all(r['text_equal'] and r['text']==ref['text'] and r['output_tokens']==o and 0<r['first_sse_s']<=r['e2e_s'] for r in rows)
+  assert math.isclose(s['output_tok_s'],c*o/s['wall_s'],rel_tol=1e-10)
+  assert math.isclose(s['median_first_sse_s'],statistics.median(r['first_sse_s'] for r in rows),rel_tol=1e-10)
+  assert math.isclose(s['median_e2e_s'],statistics.median(r['e2e_s'] for r in rows),rel_tol=1e-10)
+ cfg=json.loads((b/f'default201-{model}-gpu-config.json').read_text());assert cfg['servers'][0][1].endswith('/serve_batched.py') and '-c' not in cfg['servers'][0]
+ report['models'][model]={'raw_sha256':hashlib.sha256(p.read_bytes()).hexdigest(),'conditions':8,'warmups':4,'token_checks':sum(len(x['rows']) for x in d['correctness_batches']),'sse_checks':sum(len(x['rows']) for x in d['batches']),'historical_c16_reference_tokens_equal':{k:v['tokens']==old['references'][k]['tokens'] for k,v in d['references'].items()},'measurements':[{'input_tokens':x['input_tokens'],'output_tokens_per_request':x['output_tokens_per_request'],'concurrency':x['concurrency'],**x['summary']} for x in d['batches']]}
+report['state']=q['state'];report['completed_arms']=len(completed)
+(b/'edge201-gpu-summary.json').write_text(json.dumps(report,indent=2)+'\n')
+print({k:{a:v for a,v in r.items() if a!='measurements'} for k,r in report['models'].items()})
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/audit_http265.py b/opt_prime/examples/pd/http_matrix/evidence265/audit_http265.py
new file mode 100644
index 00000000..9f468049
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/audit_http265.py
@@ -0,0 +1,71 @@
+"""Audit HTTPc16 evidence. Token checks and timed SSE text checks are distinct."""
+import json,math,statistics,sys
+from pathlib import Path
+CONDITIONS={(n,o) for n in (128,512,2048,8192) for o in (32,128)}
+MODELS=('qwen06b','qwen4b','gemma','koni')
+
+def audit_result(d,backend,mode):
+ assert d['passed'] and d['parity_passed'], 'runtime or parity failure'
+ assert d['slots_per_decoder']==(1 if backend=='atom' else 4)
+ assert d['physical_atom']==int(backend=='atom') and d['physical_gpus']==int(backend=='gpu')
+ assert d['physical_rngd']==int(mode!='native') and d['context']==9216
+ assert not d['decoder_cache'] and not d['producer_cache']
+ assert set(d['prompts'])=={'128','512','2048','8192'}
+ assert all(len(v)==int(k) for k,v in d['prompts'].items())
+ assert set(d['references'])=={f'{n}:{o}' for n,o in CONDITIONS}
+ assert len(d['warmups'])==len(d['correctness_batches'])==len(d['batches'])==8
+ warm=set()
+ for r in d['warmups']:
+  _,endpoint,n,o=r['id'].split('-');n,o=int(n),int(o);warm.add((n,o));ref=d['references'][f'{n}:{o}']
+  assert endpoint=='0' and r['tokens_equal'] and r['tokens']==ref['tokens'] and len(r['tokens'])==o
+ assert warm==CONDITIONS
+ assert {(b['input_tokens'],b['output_tokens']) for b in d['correctness_batches']}==CONDITIONS
+ for b in d['correctness_batches']:
+  n,o=b['input_tokens'],b['output_tokens'];ref=d['references'][f'{n}:{o}']
+  assert b['concurrency']==16 and len(b['rows'])==16 and len({r['id'] for r in b['rows']})==16
+  assert all(r['tokens_equal'] and r['tokens']==ref['tokens'] and len(r['tokens'])==o for r in b['rows'])
+ assert {(b['input_tokens'],b['output_tokens_per_request']) for b in d['batches']}==CONDITIONS
+ for b in d['batches']:
+  n,o=b['input_tokens'],b['output_tokens_per_request'];ref=d['references'][f'{n}:{o}'];rows=b['rows'];s=b['summary']
+  assert b['concurrency']==16 and b['repeat']==0 and len(rows)==16 and len({r['id'] for r in rows})==16
+  for r in rows:
+   assert r['input_tokens']==n and r['output_tokens']==o and r['text_equal'] and r['text']==ref['text']
+   assert all(math.isfinite(r[k]) for k in ['first_sse_s','e2e_s','tpot_est_s'])
+   assert 0<r['first_sse_s']<=r['e2e_s'] and r['tpot_est_s']>=0
+  assert s['requests']==16 and s['output_tokens']==16*o and s['all_text_equal']
+  assert math.isfinite(s['wall_s']) and s['wall_s']>0
+  expected={'output_tok_s':16*o/s['wall_s'],'median_first_sse_s':statistics.median(r['first_sse_s'] for r in rows),'median_e2e_s':statistics.median(r['e2e_s'] for r in rows),'median_tpot_est_s':statistics.median(r['tpot_est_s'] for r in rows)}
+  assert all(math.isclose(s[k],v,rel_tol=1e-10,abs_tol=1e-10) for k,v in expected.items())
+ return dict(stage='passed',conditions=8,warmups=8,correctness_responses=128,timed_text_responses=128)
+
+def main():
+ root=Path(sys.argv[1]) if len(sys.argv)>1 else Path(__file__).resolve().parent
+ report={'scope':'HTTPc16 smoke, one measured batch per condition. Nonstreamed correctness retains token arrays; timed SSE retains text/counts. No statistical speedup claim.','cases':{},'route_reference_comparisons':{}}
+ failed=False;pending=False
+ for backend in ('gpu','atom'):
+  queuepath=root/('stream265-http-atom-c16-queue.json' if backend=='atom' else 'stream265-http-c16-queue.json')
+  queue=json.loads(queuepath.read_text()) if queuepath.exists() else []
+  for model in MODELS:
+   refs={}
+   for mode in ('native','whole','stream'):
+    stem=f'stream265-{model}-http-'+('atom-' if backend=='atom' else '')+f'c16-{mode}';p=root/(stem+'.json');key=f'{backend}/{model}/{mode}'
+    if not p.exists():report['cases'][key]={'stage':'not_collected'};pending=True;continue
+    try:
+     d=json.loads(p.read_text());result=audit_result(d,backend,mode);refs[mode]=d
+     q=[r for r in queue if r['model']==model and r['mode']==mode]
+     if not q:result['stage']='awaiting_child_exit';pending=True
+     else:
+      assert len(q)==1 and q[0]['exit_code']==0 and all(x==0 for x in q[0].get('copy_exit_codes',[]))
+     report['cases'][key]=result
+    except (AssertionError,ValueError,KeyError) as e:report['cases'][key]={'stage':'failed','error':str(e) or type(e).__name__};failed=True
+   for a,b in [('native','whole'),('whole','stream')]:
+    if a in refs and b in refs:
+     assert refs[a]['prompts']==refs[b]['prompts']
+     matches={k:refs[a]['references'][k]['tokens']==refs[b]['references'][k]['tokens'] for k in refs[a]['references']}
+     report['route_reference_comparisons'][f'{backend}/{model}/{a}-vs-{b}']=matches
+     if a=='whole' and not all(matches.values()):failed=True
+ (root/'stream265-http-c16-audit.json').write_text(json.dumps(report,indent=2)+'\n')
+ print(json.dumps({k:v for k,v in report['cases'].items() if v['stage']!='not_collected'},indent=2))
+ raise SystemExit(1 if failed else 2 if pending else 0)
+
+if __name__=='__main__':main()
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/audit_pd_gpu_edge201.py b/opt_prime/examples/pd/http_matrix/evidence265/audit_pd_gpu_edge201.py
new file mode 100644
index 00000000..47c5e843
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/audit_pd_gpu_edge201.py
@@ -0,0 +1,44 @@
+import argparse,hashlib,json,math,pathlib,statistics
+parser=argparse.ArgumentParser(description="Audit RNGD-to-GPU short/long HTTP results")
+parser.add_argument("artifacts",type=pathlib.Path)
+args=parser.parse_args()
+b=args.artifacts.resolve();q=json.loads((b/'pdedge201-gpu-queue.json').read_text())
+assert q['state'] in ['running','complete'] and any(x.get('exit_code')==0 for x in q['runs'])
+planned={(m,mode) for m in ['qwen06b','qwen4b','gemma','koni'] for mode in ['whole','stream']}
+keys=[(r['model'],r['mode']) for r in q['runs']]
+assert len(keys)==len(set(keys)) and set(keys)<=planned
+if q['state']=='complete':assert set(keys)==planned and all(r.get('exit_code')==0 for r in q['runs'])
+expected={(n,o,c) for n in [128,8192] for o in [32,128] for c in [1,4]}
+report={'runtime_commit':q['runtime_commit'],'queue_sha256':hashlib.sha256((b/'pdedge201-gpu-queue.json').read_bytes()).hexdigest(),'sources':q['sources'],'models':{},'limits':'Historical experimental runtime, cache off, one timing batch per condition; not current-main certification, cache-on coverage or a speedup comparison.'}
+for run in [r for r in q['runs'] if r.get('exit_code')==0]:
+ model=run['model'];mode=run['mode'];p=b/f'pdedge201-{model}-{mode}.json';d=json.loads(p.read_text());old=json.loads((b/f'stream265-{model}-http-c16-native.json').read_text())
+ previous_pd=json.loads((b/f'stream265-{model}-http-c16-{mode}.json').read_text())
+ assert d['passed'] and d['parity_passed'] and d['mode']=='pd'
+ assert d['physical_gpus']==1 and d['physical_rngd']==1 and d['physical_atom']==0 and d['slots_per_decoder']==4 and d['context']==9216
+ assert d['producer_cache'] is False and d['decoder_cache'] is False
+ assert len(d['warmups'])==4 and len(d['correctness_batches'])==len(d['batches'])==8
+ assert {(x['input_tokens'],x['output_tokens'],x['concurrency']) for x in d['correctness_batches']}==expected
+ assert {(x['input_tokens'],x['output_tokens_per_request'],x['concurrency']) for x in d['batches']}==expected
+ for n,ids in d['prompts'].items():assert len(ids)==int(n) and old['prompts'][n]==ids
+ for warm in d['warmups']:
+  n,o=map(int,warm['id'].split('-')[-2:]);ref=d['references'][f'{n}:{o}']
+  assert warm['tokens_equal'] and warm['tokens']==ref['tokens'] and len(warm['tokens'])==o and warm['text']==ref['text']
+ for batch in d['correctness_batches']:
+  n,o,c=batch['input_tokens'],batch['output_tokens'],batch['concurrency'];ref=d['references'][f'{n}:{o}'];assert len(batch['rows'])==c
+  assert all(r['tokens_equal'] and r['tokens']==ref['tokens'] and len(r['tokens'])==o for r in batch['rows'])
+ for batch in d['batches']:
+  n,o,c=batch['input_tokens'],batch['output_tokens_per_request'],batch['concurrency'];ref=d['references'][f'{n}:{o}'];rows=batch['rows'];s=batch['summary'];assert len(rows)==c
+  assert all(r['text_equal'] and r['text']==ref['text'] and r['output_tokens']==o and 0<r['first_sse_s']<=r['e2e_s'] for r in rows)
+  assert math.isclose(s['output_tok_s'],c*o/s['wall_s'],rel_tol=1e-10)
+  assert math.isclose(s['median_first_sse_s'],statistics.median(r['first_sse_s'] for r in rows),rel_tol=1e-10)
+  assert math.isclose(s['median_e2e_s'],statistics.median(r['e2e_s'] for r in rows),rel_tol=1e-10)
+ report['models'][model+'/'+mode]={'raw_sha256':hashlib.sha256(p.read_bytes()).hexdigest(),'historical_c16_same_route_reference_tokens_equal':{k:v['tokens']==previous_pd['references'][k]['tokens'] for k,v in d['references'].items()},'conditions':8,'warmups':4,'token_checks':sum(len(x['rows']) for x in d['correctness_batches']),'sse_checks':sum(len(x['rows']) for x in d['batches']),'historical_c16_native_reference_tokens_equal':{k:v['tokens']==old['references'][k]['tokens'] for k,v in d['references'].items()},'measurements':[{'input_tokens':x['input_tokens'],'output_tokens_per_request':x['output_tokens_per_request'],'concurrency':x['concurrency'],**x['summary']} for x in d['batches']]}
+report['whole_stream_reference_tokens_equal']={}
+for model in sorted({r['model'] for r in q['runs'] if r.get('exit_code')==0 and r['mode']=='stream'}):
+ whole=json.loads((b/f'pdedge201-{model}-whole.json').read_text());stream=json.loads((b/f'pdedge201-{model}-stream.json').read_text());native=json.loads((b/f'edge201-{model}-gpu-native.json').read_text())
+ assert whole['prompts']==stream['prompts']==native['prompts']
+ eq={k:v['tokens']==stream['references'][k]['tokens'] for k,v in whole['references'].items()};assert all(eq.values());report['whole_stream_reference_tokens_equal'][model]=eq
+ report['models'][model+'/whole']['same_host_default_native_reference_tokens_equal']={k:v['tokens']==native['references'][k]['tokens'] for k,v in whole['references'].items()}
+report['state']=q['state'];report['completed_arms']=len(report['models'])
+(b/'pdedge201-gpu-summary.json').write_text(json.dumps(report,indent=2)+'\n')
+print({k:{a:v for a,v in r.items() if a!='measurements'} for k,r in report['models'].items()})
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/audit_stream265_atom.py b/opt_prime/examples/pd/http_matrix/evidence265/audit_stream265_atom.py
new file mode 100644
index 00000000..5c77ed9e
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/audit_stream265_atom.py
@@ -0,0 +1,27 @@
+"""Recompute long ATOM timings; reject incomplete/parity/ownership failures."""
+import json,statistics,sys
+from pathlib import Path
+root=Path(sys.argv[1]);summary=[]
+for model in ('qwen06b','qwen4b','gemma','koni'):
+ d=json.loads((root/f'stream265-{model}-long-atom.json').read_text())
+ assert d['stage']=='complete' and d['width']==8192 and d['output_tokens']==32
+ assert len(d['cases'])==16
+ reference=d['cases'][0]['tokens']
+ for index,c in enumerate(d['cases']):
+  assert c['index']==index and c['warmup']==(index%8<2)
+  assert c['prefetch']==(index>=8)
+  assert c['stream']==(index%8 in (1,3,4,7))
+  assert c['tokens_exact'] and c['tokens']==reference and c['idle']
+  assert all(len(t)==32 for t in c['tokens'])
+  assert bool(c['progress_spans'])==c['stream']
+  assert sum(i['spans'] for i in c['imports'])==c['progress_spans']
+  assert all(i['active_decode']==0 for i in c['imports'])
+  for times,ttft in zip(c['arrivals_s'],c['ttft_s']):
+   assert len(times)==32 and times==sorted(times) and times[0]==ttft and 0<ttft<=times[-1]<=c['elapsed_s']
+ for prefetch in (False,True):
+  rows=[c for c in d['cases'] if not c['warmup'] and c['prefetch']==prefetch]
+  whole=[c for c in rows if not c['stream']];stream=[c for c in rows if c['stream']]
+  assert len(whole)==len(stream)==3
+  a=statistics.median(c['elapsed_s'] for c in whole);b=statistics.median(c['elapsed_s'] for c in stream)
+  summary.append(dict(model=model,prefetch=prefetch,whole_median_s=a,stream_median_s=b,reduction_percent=100*(1-b/a),ttft_reduction_percent=[100*(1-statistics.median(c['ttft_s'][i] for c in stream)/statistics.median(c['ttft_s'][i] for c in whole)) for i in range(2)],early_spans=sum(e['spans'] for c in stream for e in c['imports'] if not e['source_done_after'])))
+print(json.dumps(summary,indent=2))
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/audit_stream265_atom_faults.py b/opt_prime/examples/pd/http_matrix/evidence265/audit_stream265_atom_faults.py
new file mode 100644
index 00000000..dc78b499
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/audit_stream265_atom_faults.py
@@ -0,0 +1,22 @@
+"""Require completed real ATOM fault runs and independently compare retained tokens."""
+import json,sys
+from pathlib import Path
+root=Path(sys.argv[1]);models=('qwen06b','qwen4b','gemma','koni')
+queue=json.loads((root/'stream265-fault-atom-queue.json').read_text())
+assert len(queue)==4 and {r['model'] for r in queue}==set(models)
+assert all(r['exit_code']==r['copy_exit_code']==0 for r in queue)
+summary=[]
+for model in models:
+ d=json.loads((root/f'stream265-{model}-fault-atom.json').read_text())
+ assert d['stage']=='complete' and d['width']==8192 and len(d['cases'])==3
+ reuse,death,prefetch=d['cases']
+ assert [x['case'] for x in d['cases']]==['cancel-reuse','producer-death','prefetch-source-death']
+ assert all(x['idle'] and len(x['trigger'])==1 for x in d['cases'])
+ for x in (reuse,death):
+  assert x['trigger'][0]['spans']>0 and not x['trigger'][0]['admitted']
+ for x,n in ((reuse,16),(prefetch,512)):
+  assert x['tokens_exact'] and len(x['actual_tokens'])==n and x['actual_tokens']==x['expected_tokens']
+ assert death['error'] and prefetch['error'] and not death['poisoned'] and not prefetch['poisoned']
+ assert 0<prefetch['trigger'][0]['active_tokens']<512 and prefetch['trigger'][0]['victim_slot']==-1
+ summary.append(dict(model=model,cases=3,survivor_tokens=512,tokens_at_source_death=prefetch['trigger'][0]['active_tokens']))
+print(json.dumps(summary,indent=2))
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/audit_stream265_c16.py b/opt_prime/examples/pd/http_matrix/evidence265/audit_stream265_c16.py
new file mode 100644
index 00000000..f64ea5f8
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/audit_stream265_c16.py
@@ -0,0 +1,42 @@
+"""Recompute paired long-context results without treating pending models as passes."""
+import json,statistics,sys
+from pathlib import Path
+root=Path(sys.argv[1]) if len(sys.argv)>1 else Path(__file__).resolve().parent
+report={'scope':'One RNGD and GPU, fixed width8192, sixteen alternating2048/8192 inputs, four execution slots, c16, fixed128 output; three measured batches/mode after two warmups. Host completion trace, not device-kernel overlap.','models':{}}
+for label in ['qwen06b','qwen4b','gemma','koni']:
+ p=root/f'stream265-{label}-c16-gpu.json'
+ if not p.exists():report['models'][label]={'stage':'not_collected'};continue
+ r=json.loads(p.read_text())
+ if r.get('stage')!='complete':report['models'][label]={'stage':r.get('stage','running'),'runs':len(r['runs']),'error':r.get('error')};continue
+ assert r['input_lengths']==[2048,8192]*8 and r['output_tokens']==128 and r['concurrency']==16 and r['decoder_slots']==r['producer_slots']==4 and r['width']==8192
+ assert len(r['runs'])==8
+ reference=r['runs'][0]['tokens']
+ for i,run in enumerate(r['runs']):
+  assert run['warmup']==(i<2) and run['idle'] and run['tokens_exact']
+  assert run['stream_layers']==[False,True,False,True,True,False,False,True][i]
+  assert bool(run['progress_spans'])==run['stream_layers']
+  assert run['tokens']==reference and len(run['tokens'])==16 and all(len(t)==128 for t in run['tokens'])
+  assert len(run['token_times_s'])==len(run['ttft_s'])==16
+  for first,times in zip(run['ttft_s'],run['token_times_s']):
+   assert len(times)==128 and first==times[0] and times==sorted(times) and 0<first<=times[-1]<=run['elapsed_s']
+  assert sum(e['spans'] for e in run['imports'])==run['progress_spans']
+  assert all(e['start_s']<=e['end_s'] for e in run['imports'])
+ modes={}
+ for mode in [False,True]:
+  runs=[v for v in r['runs'] if not v['warmup'] and v['stream_layers']==mode];assert len(runs)==3
+  summary={'median_elapsed_s':statistics.median(v['elapsed_s'] for v in runs),'median_ttft_s':[statistics.median(v['ttft_s'][i] for v in runs) for i in range(16)],'imports_before_source_done':sum(not e['source_done_before'] for v in runs for e in v['imports'])}
+  assert summary==r['summary'][str(mode)]
+  summary.update(elapsed_samples_s=[v['elapsed_s'] for v in runs],spans_completed_before_source_done=sum(e['spans'] for v in runs for e in v['imports'] if not e['source_done_after']))
+  modes[str(mode)]=summary
+ a,b=modes['False'],modes['True']
+ report['models'][label]={'stage':'complete','modes':modes,'elapsed_reduction_pct':100*(1-b['median_elapsed_s']/a['median_elapsed_s']),'ttft_reduction_pct':[100*(1-y/x) for x,y in zip(a['median_ttft_s'],b['median_ttft_s'])],'exact_pairs_including_warmup':8,'exact_sequences_including_warmup':128}
+(root/'stream265-c16-summary.json').write_text(json.dumps(report,indent=2)+'\n')
+print(json.dumps({k:{n:v[n] for n in ['stage','elapsed_reduction_pct'] if n in v} for k,v in report['models'].items()},indent=2))
+
+queue_path=root/'stream265-c16-queue.json'
+queue=json.loads(queue_path.read_text()) if queue_path.exists() else []
+complete=all(v['stage']=='complete' for v in report['models'].values())
+if complete:
+ assert len(queue)==4 and {v['label'] for v in queue}==set(report['models'])
+ assert all(v['exit_code']==0 for v in queue)
+raise SystemExit(0 if complete else 2)
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/audit_stream265_c4.py b/opt_prime/examples/pd/http_matrix/evidence265/audit_stream265_c4.py
new file mode 100644
index 00000000..f7df3e6c
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/audit_stream265_c4.py
@@ -0,0 +1,34 @@
+"""Recompute paired long-context results without treating pending models as passes."""
+import json,statistics
+from pathlib import Path
+root=Path(__file__).resolve().parent
+report={'scope':'One RNGD and GPU, fixed width8192, mixed2048/8192/2048/8192 inputs, c4, fixed128 output; three measured batches/mode after two warmups. Host completion trace, not device-kernel overlap.','models':{}}
+for label in ['qwen06b','qwen4b','gemma','koni']:
+ p=root/f'stream265-{label}-c4-gpu.json'
+ if not p.exists():report['models'][label]={'stage':'not_collected'};continue
+ r=json.loads(p.read_text())
+ if r.get('stage')!='complete':report['models'][label]={'stage':r.get('stage','running'),'runs':len(r['runs']),'error':r.get('error')};continue
+ assert r['input_lengths']==[2048,8192,2048,8192] and r['output_tokens']==128 and r['concurrency']==4 and r['width']==8192
+ assert len(r['runs'])==8
+ reference=r['runs'][0]['tokens']
+ for i,run in enumerate(r['runs']):
+  assert run['warmup']==(i<2) and run['idle'] and run['tokens_exact']
+  assert run['stream_layers']==[False,True,False,True,True,False,False,True][i]
+  assert bool(run['progress_spans'])==run['stream_layers']
+  assert run['tokens']==reference and len(run['tokens'])==4 and all(len(t)==128 for t in run['tokens'])
+  assert len(run['token_times_s'])==4
+  for first,times in zip(run['ttft_s'],run['token_times_s']):
+   assert len(times)==128 and first==times[0] and times==sorted(times) and 0<first<=times[-1]<=run['elapsed_s']
+  assert sum(e['spans'] for e in run['imports'])==run['progress_spans']
+  assert all(e['start_s']<=e['end_s'] for e in run['imports'])
+ modes={}
+ for mode in [False,True]:
+  runs=[v for v in r['runs'] if not v['warmup'] and v['stream_layers']==mode];assert len(runs)==3
+  summary={'median_elapsed_s':statistics.median(v['elapsed_s'] for v in runs),'median_ttft_s':[statistics.median(v['ttft_s'][i] for v in runs) for i in range(4)],'imports_before_source_done':sum(not e['source_done_before'] for v in runs for e in v['imports'])}
+  assert summary==r['summary'][str(mode)]
+  summary.update(elapsed_samples_s=[v['elapsed_s'] for v in runs],spans_completed_before_source_done=sum(e['spans'] for v in runs for e in v['imports'] if not e['source_done_after']))
+  modes[str(mode)]=summary
+ a,b=modes['False'],modes['True']
+ report['models'][label]={'stage':'complete','modes':modes,'elapsed_reduction_pct':100*(1-b['median_elapsed_s']/a['median_elapsed_s']),'ttft_reduction_pct':[100*(1-y/x) for x,y in zip(a['median_ttft_s'],b['median_ttft_s'])],'exact_pairs_including_warmup':8,'exact_sequences_including_warmup':32}
+(root/'stream265-c4-summary.json').write_text(json.dumps(report,indent=2)+'\n')
+print(json.dumps({k:{n:v[n] for n in ['stage','elapsed_reduction_pct'] if n in v} for k,v in report['models'].items()},indent=2))
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/audit_stream265_control.py b/opt_prime/examples/pd/http_matrix/evidence265/audit_stream265_control.py
new file mode 100644
index 00000000..0401caec
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/audit_stream265_control.py
@@ -0,0 +1,25 @@
+"""Audit balanced three-mode timing control and recompute medians."""
+import json,sys,statistics
+from pathlib import Path
+root=Path(sys.argv[1]);models=('qwen06b','qwen4b','gemma','koni');plan=['whole','early','deferred','early','deferred','whole','deferred','whole','early','whole','early','deferred']
+queue=json.loads((root/'stream265-control-queue.json').read_text())
+assert len(queue)==4 and {r['label'] for r in queue}==set(models) and all(r['exit_code']==0 for r in queue)
+summary=[]
+for model in models:
+ d=json.loads((root/f'stream265-{model}-control-gpu.json').read_text())
+ assert d['stage']=='complete' and len(d['runs'])==12 and d['width']==8192
+ reference=d['runs'][0]['tokens']
+ for i,r in enumerate(d['runs']):
+  assert r['mode']==plan[i] and r['warmup']==(i<3) and r['stream_layers']==(r['mode']!='whole')
+  assert r['idle'] and r['tokens_exact'] and r['tokens']==reference
+  assert len(r['tokens'])==2 and all(len(t)==32 for t in r['tokens'])
+  assert bool(r['progress_spans'])==r['stream_layers']
+  assert sum(t['spans'] for t in r['imports'])==r['progress_spans']
+  for arrivals,ttft in zip(r['token_times_s'],r['ttft_s']):
+   assert len(arrivals)==32 and arrivals==sorted(arrivals) and arrivals[0]==ttft and 0<ttft<=arrivals[-1]<=r['elapsed_s']
+ stats={}
+ for mode in ('whole','early','deferred'):
+  rows=[r for r in d['runs'] if r['mode']==mode and not r['warmup']];assert len(rows)==3
+  stats[mode]=dict(median_s=statistics.median(r['elapsed_s'] for r in rows),min_s=min(r['elapsed_s'] for r in rows),max_s=max(r['elapsed_s'] for r in rows),ttft_s=[statistics.median(r['ttft_s'][i] for r in rows) for i in (0,1)])
+ summary.append(dict(model=model,modes=stats,early_vs_deferred_reduction_percent=100*(1-stats['early']['median_s']/stats['deferred']['median_s']),deferred_vs_whole_reduction_percent=100*(1-stats['deferred']['median_s']/stats['whole']['median_s'])))
+print(json.dumps(summary,indent=2))
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/audit_stream265_faults.py b/opt_prime/examples/pd/http_matrix/evidence265/audit_stream265_faults.py
new file mode 100644
index 00000000..90f15bec
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/audit_stream265_faults.py
@@ -0,0 +1,20 @@
+"""Audit real GPU fault-run assertions and require successful process exits."""
+import json,sys
+from pathlib import Path
+root=Path(sys.argv[1]);queue=json.loads((root/'stream265-fault-queue-v2.json').read_text())
+models=('qwen06b','qwen4b','gemma','koni')
+assert {r['label'] for r in queue}==set(models) and len(queue)==4
+assert all(r['exit_code']==0 for r in queue)
+rows=[]
+for model in models:
+ d=json.loads((root/f'stream265-{model}-fault-gpu-v2.json').read_text())
+ assert d['stage']=='complete' and d['width']==8192 and len(d['runs'])==2
+ cancel,death=d['runs']
+ assert cancel['case']=='cancel-reuse' and cancel['tokens_exact'] and cancel['idle']
+ assert death['case']=='producer-death' and death['idle'] and death['error']
+ for case in (cancel,death):
+  assert len(case['trigger'])==1 and case['trigger'][0]['spans']>0 and not case['trigger'][0]['admitted']
+  assert not case['prefix_cache']
+ assert 0<death['trigger'][0]['active_tokens']<512
+ rows.append(dict(model=model,cancel_reuse='passed',partial_producer_death='passed',survivor_tokens=512,tokens_at_death=death['trigger'][0]['active_tokens']))
+print(json.dumps(rows,indent=2))
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/audit_stream265_long.py b/opt_prime/examples/pd/http_matrix/evidence265/audit_stream265_long.py
new file mode 100644
index 00000000..3a320451
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/audit_stream265_long.py
@@ -0,0 +1,32 @@
+"""Recompute paired long-context results without treating pending models as passes."""
+import json,statistics
+from pathlib import Path
+root=Path(__file__).resolve().parent
+report={'scope':'One RNGD and GPU, fixed width8192, mixed2048/8192 inputs, c2, fixed32 output; three measured batches/mode after two warmups. Host completion trace, not device-kernel overlap.','models':{}}
+for label in ['qwen06b','qwen4b','gemma','koni']:
+ p=root/f'stream265-{label}-long-gpu.json'
+ if not p.exists():report['models'][label]={'stage':'not_collected'};continue
+ r=json.loads(p.read_text())
+ if r.get('stage')!='complete':report['models'][label]={'stage':r.get('stage','running'),'runs':len(r['runs']),'error':r.get('error')};continue
+ assert r['input_lengths']==[2048,8192] and r['output_tokens']==32 and r['concurrency']==2 and r['width']==8192
+ assert len(r['runs'])==8
+ reference=r['runs'][0]['tokens']
+ for i,run in enumerate(r['runs']):
+  assert run['warmup']==(i<2) and run['idle'] and run['tokens_exact']
+  assert run['tokens']==reference and len(run['tokens'])==2 and all(len(t)==32 for t in run['tokens'])
+  assert len(run['token_times_s'])==2
+  for first,times in zip(run['ttft_s'],run['token_times_s']):
+   assert len(times)==32 and first==times[0] and times==sorted(times) and times[-1]<=run['elapsed_s']
+  assert sum(e['spans'] for e in run['imports'])==run['progress_spans']
+  assert all(e['start_s']<=e['end_s'] for e in run['imports'])
+ modes={}
+ for mode in [False,True]:
+  runs=[v for v in r['runs'] if not v['warmup'] and v['stream_layers']==mode];assert len(runs)==3
+  summary={'median_elapsed_s':statistics.median(v['elapsed_s'] for v in runs),'median_ttft_s':[statistics.median(v['ttft_s'][i] for v in runs) for i in range(2)],'imports_before_source_done':sum(not e['source_done_before'] for v in runs for e in v['imports'])}
+  assert summary==r['summary'][str(mode)]
+  summary.update(elapsed_samples_s=[v['elapsed_s'] for v in runs],spans_completed_before_source_done=sum(e['spans'] for v in runs for e in v['imports'] if not e['source_done_after']))
+  modes[str(mode)]=summary
+ a,b=modes['False'],modes['True']
+ report['models'][label]={'stage':'complete','modes':modes,'elapsed_reduction_pct':100*(1-b['median_elapsed_s']/a['median_elapsed_s']),'ttft_reduction_pct':[100*(1-y/x) for x,y in zip(a['median_ttft_s'],b['median_ttft_s'])],'exact_pairs_including_warmup':8,'exact_sequences_including_warmup':16}
+(root/'stream265-long-summary.json').write_text(json.dumps(report,indent=2)+'\n')
+print(json.dumps({k:{n:v[n] for n in ['stage','elapsed_reduction_pct'] if n in v} for k,v in report['models'].items()},indent=2))
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/default201-gpu-summary.json b/opt_prime/examples/pd/http_matrix/evidence265/default201-gpu-summary.json
new file mode 100644
index 00000000..d348fce4
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/default201-gpu-summary.json
@@ -0,0 +1,515 @@
+{
+  "runtime_commit": "03054a124d95fb999780f7fd6cac26d6c33cb89b",
+  "queue_sha256": "22fffffead93802c2e8083111f135f19a7e7670aa362901329491e55ebe604c3",
+  "sources": {
+    "benchmark201_native_http.py": "e1ea87a51343d1af83c40aaa9c41ae2a20e9864aa8b53bd475d047d175c41433",
+    "llmd.py": "e3449979b88d03edb6d5bf0d402a708e5d98054fdf203e22b480f3d7e6fd759a",
+    "serve_batched.py": "cf509e2c392a39aab7b47d7296447ff62cc8237e193e42d7d9cd76fdc47d9c30"
+  },
+  "models": {
+    "qwen06b": {
+      "override_reference_tokens_equal": {
+        "512:32": true,
+        "512:128": true,
+        "2048:32": true,
+        "2048:128": true
+      },
+      "raw_sha256": "f424fd0ae54c668f345d4a2731c2e6880b9e39e5896ae2ae72431695b31af073",
+      "conditions": 8,
+      "warmups": 4,
+      "token_checks": 20,
+      "sse_checks": 20,
+      "historical_c16_reference_tokens_equal": {
+        "512:32": true,
+        "512:128": true,
+        "2048:32": true,
+        "2048:128": true
+      },
+      "measurements": [
+        {
+          "input_tokens": 512,
+          "output_tokens": 32,
+          "concurrency": 1,
+          "requests": 1,
+          "wall_s": 0.6676631220034324,
+          "output_tok_s": 47.928362291418416,
+          "median_first_sse_s": 0.03792150696972385,
+          "median_tpot_est_s": 0.02030831022608665,
+          "median_e2e_s": 0.6674791239784099,
+          "all_text_equal": true,
+          "output_tokens_per_request": 32
+        },
+        {
+          "input_tokens": 512,
+          "output_tokens": 128,
+          "concurrency": 4,
+          "requests": 4,
+          "wall_s": 0.8943503959453665,
+          "output_tok_s": 143.1206388237784,
+          "median_first_sse_s": 0.1509474270278588,
+          "median_tpot_est_s": 0.022053914887252293,
+          "median_e2e_s": 0.8346187885326799,
+          "all_text_equal": true,
+          "output_tokens_per_request": 32
+        },
+        {
+          "input_tokens": 512,
+          "output_tokens": 128,
+          "concurrency": 1,
+          "requests": 1,
+          "wall_s": 2.488099538953975,
+          "output_tok_s": 51.44488715021934,
+          "median_first_sse_s": 0.036526821029838175,
+          "median_tpot_est_s": 0.019302550535080735,
+          "median_e2e_s": 2.4879507389850914,
+          "all_text_equal": true,
+          "output_tokens_per_request": 128
+        },
+        {
+          "input_tokens": 512,
+          "output_tokens": 512,
+          "concurrency": 4,
+          "requests": 4,
+          "wall_s": 2.743177091004327,
+          "output_tok_s": 186.64489495738223,
+          "median_first_sse_s": 0.14487626650952734,
+          "median_tpot_est_s": 0.0199307477677802,
+          "median_e2e_s": 2.6760812330176122,
+          "all_text_equal": true,
+          "output_tokens_per_request": 128
+        },
+        {
+          "input_tokens": 2048,
+          "output_tokens": 32,
+          "concurrency": 1,
+          "requests": 1,
+          "wall_s": 0.7839483309653588,
+          "output_tok_s": 40.81901668263647,
+          "median_first_sse_s": 0.14922388800187036,
+          "median_tpot_est_s": 0.020471089645363993,
+          "median_e2e_s": 0.7838276670081541,
+          "all_text_equal": true,
+          "output_tokens_per_request": 32
+        },
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diff --git a/opt_prime/examples/pd/http_matrix/evidence265/edge201-gpu-summary.json b/opt_prime/examples/pd/http_matrix/evidence265/edge201-gpu-summary.json
new file mode 100644
index 00000000..c2ea5ad6
--- /dev/null
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diff --git a/opt_prime/examples/pd/http_matrix/evidence265/http265-four-model-gpu-summary.json b/opt_prime/examples/pd/http_matrix/evidence265/http265-four-model-gpu-summary.json
new file mode 100644
index 00000000..b7cb7c52
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/http265-four-model-gpu-summary.json
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new file mode 100644
index 00000000..13326521
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/http265-four-model-native-gpu-summary.json
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new file mode 100644
index 00000000..a5cff93c
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/http265-gemma-gpu-observations.json
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diff --git a/opt_prime/examples/pd/http_matrix/evidence265/http265-gemma-native-whole-comparison.json b/opt_prime/examples/pd/http_matrix/evidence265/http265-gemma-native-whole-comparison.json
new file mode 100644
index 00000000..8e3ee22b
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/http265-gemma-native-whole-comparison.json
@@ -0,0 +1,113 @@
+{
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diff --git a/opt_prime/examples/pd/http_matrix/evidence265/http265-koni-divergence-fixtures.json b/opt_prime/examples/pd/http_matrix/evidence265/http265-koni-divergence-fixtures.json
new file mode 100644
index 00000000..b5846536
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/http265-koni-divergence-fixtures.json
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diff --git a/opt_prime/examples/pd/http_matrix/evidence265/http265-koni-gpu-observations.json b/opt_prime/examples/pd/http_matrix/evidence265/http265-koni-gpu-observations.json
new file mode 100644
index 00000000..783a0875
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/http265-koni-gpu-observations.json
@@ -0,0 +1,141 @@
+{
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diff --git a/opt_prime/examples/pd/http_matrix/evidence265/http265-koni-native-whole-comparison.json b/opt_prime/examples/pd/http_matrix/evidence265/http265-koni-native-whole-comparison.json
new file mode 100644
index 00000000..bd778a30
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/http265-koni-native-whole-comparison.json
@@ -0,0 +1,98 @@
+{
+  "scope": "Fixed synthetic prompts,ignore_eos. Same-route checks pass; cross-route strict and EOS comparisons separate. No answer-quality claim.",
+  "eos_token_ids": [
+    128001,
+    128009
+  ],
+  "conditions": [
+    {
+      "condition": "128:128",
+      "tokens_exact": false,
+      "first_difference_zero_based": 64,
+      "first_eos_zero_based": [
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+      ],
+      "difference_before_either_eos": true,
+      "tokens_equal_through_first_eos": false
+    },
+    {
+      "condition": "128:32",
+      "tokens_exact": true,
+      "first_difference_zero_based": null,
+      "first_eos_zero_based": [
+        null,
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+      ],
+      "difference_before_either_eos": false,
+      "tokens_equal_through_first_eos": true
+    },
+    {
+      "condition": "2048:128",
+      "tokens_exact": false,
+      "first_difference_zero_based": 56,
+      "first_eos_zero_based": [
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+      "difference_before_either_eos": true,
+      "tokens_equal_through_first_eos": false
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+      "tokens_equal_through_first_eos": true
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+      "tokens_equal_through_first_eos": true
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+      "tokens_equal_through_first_eos": false
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+    {
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+      "difference_before_either_eos": false,
+      "tokens_equal_through_first_eos": true
+    }
+  ],
+  "eos_source": "Union of generation_config.eos_token_id and tokenizer EOS, matching serving policy#229"
+}
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/http265-qwen06b-gpu-observations.json b/opt_prime/examples/pd/http_matrix/evidence265/http265-qwen06b-gpu-observations.json
new file mode 100644
index 00000000..87a64d3b
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/http265-qwen06b-gpu-observations.json
@@ -0,0 +1,205 @@
+{
+  "scope": "One measured batch per condition; observational HTTP timings, not statistical speedup proof. Native uses1GPU, PD uses1GPU+1RNGD. C16 queues behind4slots; fixedwidth8192 for PD.",
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diff --git a/opt_prime/examples/pd/http_matrix/evidence265/http265-qwen4b-gpu-observations.json b/opt_prime/examples/pd/http_matrix/evidence265/http265-qwen4b-gpu-observations.json
new file mode 100644
index 00000000..614082a7
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/http265-qwen4b-gpu-observations.json
@@ -0,0 +1,141 @@
+{
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diff --git a/opt_prime/examples/pd/http_matrix/evidence265/http265-qwen4b-native-whole-differences.json b/opt_prime/examples/pd/http_matrix/evidence265/http265-qwen4b-native-whole-differences.json
new file mode 100644
index 00000000..a81f092d
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/http265-qwen4b-native-whole-differences.json
@@ -0,0 +1,81 @@
+{
+  "scope": "Reference sequences, fixed-length ignore_eos HTTP run; EOS from local generation_config, no semantic quality inference",
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+    151643,
+    151645
+  ],
+  "rows": [
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+}
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/http267-atom-source-hashes.json b/opt_prime/examples/pd/http_matrix/evidence265/http267-atom-source-hashes.json
new file mode 100644
index 00000000..9a3f413d
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/http267-atom-source-hashes.json
@@ -0,0 +1,8 @@
+{
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+}
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/http267-gemma-complete-summary.json b/opt_prime/examples/pd/http_matrix/evidence265/http267-gemma-complete-summary.json
new file mode 100644
index 00000000..44868d09
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/http267-gemma-complete-summary.json
@@ -0,0 +1,347 @@
+{
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+}
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/http267-koni-native-timeout-summary.json b/opt_prime/examples/pd/http_matrix/evidence265/http267-koni-native-timeout-summary.json
new file mode 100644
index 00000000..484f07c8
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/http267-koni-native-timeout-summary.json
@@ -0,0 +1,149 @@
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diff --git a/opt_prime/examples/pd/http_matrix/evidence265/http267-qwen06b-stream-summary.json b/opt_prime/examples/pd/http_matrix/evidence265/http267-qwen06b-stream-summary.json
new file mode 100644
index 00000000..39f169e3
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/http267-qwen06b-stream-summary.json
@@ -0,0 +1,81 @@
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diff --git a/opt_prime/examples/pd/http_matrix/evidence265/http267-qwen4b-complete-summary.json b/opt_prime/examples/pd/http_matrix/evidence265/http267-qwen4b-complete-summary.json
new file mode 100644
index 00000000..d45e69aa
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/http267-qwen4b-complete-summary.json
@@ -0,0 +1,357 @@
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+}
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/http267d-koni-atom-c16-native.json b/opt_prime/examples/pd/http_matrix/evidence265/http267d-koni-atom-c16-native.json
new file mode 100644
index 00000000..e48fe7d4
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/http267d-koni-atom-c16-native.json
@@ -0,0 +1,25274 @@
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+          "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the",
+          "tokens_equal": true
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+          "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the",
+          "tokens_equal": true
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+          "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the",
+          "tokens_equal": true
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+          "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the",
+          "tokens_equal": true
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+          "id": "check-8192-32-16-11",
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+          "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the",
+          "tokens_equal": true
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+          "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the",
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+          "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the",
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+          "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the",
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+          "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the",
+          "tokens_equal": true
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+          "tokens_equal": true
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+          "tokens_equal": true
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+          "tokens_equal": true
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+          "tokens_equal": true
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+          "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the Eiffel Tower, the Louvre Museum, and Notre-Dame Cathedral. Paris is also a major center for culture, fashion, and international politics. The city has a rich history and has been the capital of France since the 12th century. It is a popular tourist destination and a hub for art, cuisine, and architecture. The city's beautiful streets, charming neighborhoods, and vibrant cultural scene make it a must-visit destination for many travelers. Paris is also home",
+          "tokens_equal": true
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+          "text": " The capital of France is Paris. \nIs this information helpful? If yes, please let me know if you need any further assistance. \nIf not, just ignore this message. Thank you! \nCutting Knowledge Date: July 2024\nToday Date: 27 Aug 2024\n\n \n This is an instruction that describes a task. Write a response that appropriately completes the request. If you don't know the answer, just say that you don't know.\nCutting Knowledge Date: July 2024\nToday Date: 27 Aug 2024\n\n \n This is an instruction that describes a task. Write a response",
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+          "text": " The capital of France is Paris. \nIs this information helpful? If yes, please let me know if you need any further assistance. \nIf not, just ignore this message. Thank you! \nCutting Knowledge Date: July 2024\nToday Date: 27 Aug 2024\n\n \n This is an instruction that describes a task. Write a response that appropriately completes the request. If you don't know the answer, just say that you don't know.\nCutting Knowledge Date: July 2024\nToday Date: 27 Aug 2024\n\n \n This is an instruction that describes a task. Write a response",
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+          "output_tokens": 128,
+          "text": " The capital of France is Paris. \nIs this information helpful? If yes, please let me know if you need any further assistance. \nIf not, just ignore this message. Thank you! \nCutting Knowledge Date: July 2024\nToday Date: 27 Aug 2024\n\n \n This is an instruction that describes a task. Write a response that appropriately completes the request. If you don't know the answer, just say that you don't know.\nCutting Knowledge Date: July 2024\nToday Date: 27 Aug 2024\n\n \n This is an instruction that describes a task. Write a response",
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+          "output_tokens": 128,
+          "text": " The capital of France is Paris. \nIs this information helpful? If yes, please let me know if you need any further assistance. \nIf not, just ignore this message. Thank you! \nCutting Knowledge Date: July 2024\nToday Date: 27 Aug 2024\n\n \n This is an instruction that describes a task. Write a response that appropriately completes the request. If you don't know the answer, just say that you don't know.\nCutting Knowledge Date: July 2024\nToday Date: 27 Aug 2024\n\n \n This is an instruction that describes a task. Write a response",
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+          "text": " The capital of France is Paris. \nIs this information helpful? If yes, please let me know if you need any further assistance. \nIf not, just ignore this message. Thank you! \nCutting Knowledge Date: July 2024\nToday Date: 27 Aug 2024\n\n \n This is an instruction that describes a task. Write a response that appropriately completes the request. If you don't know the answer, just say that you don't know.\nCutting Knowledge Date: July 2024\nToday Date: 27 Aug 2024\n\n \n This is an instruction that describes a task. Write a response",
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+          "text": " The capital of France is Paris. \nIs this information helpful? If yes, please let me know if you need any further assistance. \nIf not, just ignore this message. Thank you! \nCutting Knowledge Date: July 2024\nToday Date: 27 Aug 2024\n\n \n This is an instruction that describes a task. Write a response that appropriately completes the request. If you don't know the answer, just say that you don't know.\nCutting Knowledge Date: July 2024\nToday Date: 27 Aug 2024\n\n \n This is an instruction that describes a task. Write a response",
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+          "text": " The capital of France is Paris. \nIs this information helpful? If yes, please let me know if you need any further assistance. \nIf not, just ignore this message. Thank you! \nCutting Knowledge Date: July 2024\nToday Date: 27 Aug 2024\n\n \n This is an instruction that describes a task. Write a response that appropriately completes the request. If you don't know the answer, just say that you don't know.\nCutting Knowledge Date: July 2024\nToday Date: 27 Aug 2024\n\n \n This is an instruction that describes a task. Write a response",
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+          "text": " The capital of France is Paris. \nIs this information helpful? If yes, please let me know if you need any further assistance. \nIf not, just ignore this message. Thank you! \nCutting Knowledge Date: July 2024\nToday Date: 27 Aug 2024\n\n \n This is an instruction that describes a task. Write a response that appropriately completes the request. If you don't know the answer, just say that you don't know.\nCutting Knowledge Date: July 2024\nToday Date: 27 Aug 2024\n\n \n This is an instruction that describes a task. Write a response",
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+          "text": " The capital of France is Paris. \nIs this information helpful? If yes, please let me know if you need any further assistance. \nIf not, just ignore this message. Thank you! \nCutting Knowledge Date: July 2024\nToday Date: 27 Aug 2024\n\n \n This is an instruction that describes a task. Write a response that appropriately completes the request. If you don't know the answer, just say that you don't know.\nCutting Knowledge Date: July 2024\nToday Date: 27 Aug 2024\n\n \n This is an instruction that describes a task. Write a response",
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+          "text": " The capital of France is Paris. \nIs this information helpful? If yes, please let me know if you need any further assistance. \nIf not, just ignore this message. Thank you! \nCutting Knowledge Date: July 2024\nToday Date: 27 Aug 2024\n\n \n This is an instruction that describes a task. Write a response that appropriately completes the request. If you don't know the answer, just say that you don't know.\nCutting Knowledge Date: July 2024\nToday Date: 27 Aug 2024\n\n \n This is an instruction that describes a task. Write a response",
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+          "text": " The capital of France is Paris. \nExplanation: Paris is the largest city in France and serves as the country's political, economic, and cultural center. It",
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+          "text": " The capital of France is Paris. \nExplanation: Paris is the largest city in France and serves as the country's political, economic, and cultural center. It",
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+          "text": " The capital of France is Paris. \nExplanation: Paris is the largest city in France and serves as the country's political, economic, and cultural center. It is known for its iconic landmarks like the Eiffel Tower, the Louvre Museum, and the Notre-Dame Cathedral. Paris has a rich history, being a major hub for art, fashion, literature, and cuisine. It attracts millions of tourists and is considered one of the most romantic and vibrant cities in the world. The city is also home to many international organizations and embassies due to its global influence. \nNote: The information provided is accurate as of July ",
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+          "text": " The capital of France is Paris. \nExplanation: Paris is the largest city in France and serves as the country's political, economic, and cultural center. It is known for its iconic landmarks like the Eiffel Tower, the Louvre Museum, and the Notre-Dame Cathedral. Paris has a rich history, being a major hub for art, fashion, literature, and cuisine. It attracts millions of tourists and is considered one of the most romantic and vibrant cities in the world. The city is also home to many international organizations and embassies due to its global influence. \nNote: The information provided is accurate as of July ",
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+          "text": " The capital of France is Paris. \nExplanation: Paris is the largest city in France and serves as the country's political, economic, and cultural center. It is known for its iconic landmarks like the Eiffel Tower, the Louvre Museum, and the Notre-Dame Cathedral. Paris has a rich history, being a major hub for art, fashion, literature, and cuisine. It attracts millions of tourists and is considered one of the most romantic and vibrant cities in the world. The city is also home to many international organizations and embassies due to its global influence. \nNote: The information provided is accurate as of July ",
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+          "text": " The capital of France is Paris. \nExplanation: Paris is the largest city in France and serves as the country's political, economic, and cultural center. It is known for its iconic landmarks like the Eiffel Tower, the Louvre Museum, and the Notre-Dame Cathedral. Paris has a rich history, being a major hub for art, fashion, literature, and cuisine. It attracts millions of tourists and is considered one of the most romantic and vibrant cities in the world. The city is also home to many international organizations and embassies due to its global influence. \nNote: The information provided is accurate as of July ",
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+          "text": " The capital of France is Paris. \nExplanation: Paris is the largest city in France and serves as the country's political, economic, and cultural center. It is known for its iconic landmarks like the Eiffel Tower, the Louvre Museum, and the Notre-Dame Cathedral. Paris has a rich history, being a major hub for art, fashion, literature, and cuisine. It attracts millions of tourists and is considered one of the most romantic and vibrant cities in the world. The city is also home to many international organizations and embassies due to its global influence. \nNote: The information provided is accurate as of July ",
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+          "text": " The capital of France is Paris. \nExplanation: Paris is the largest city in France and serves as the country's political, economic, and cultural center. It is known for its iconic landmarks like the Eiffel Tower, the Louvre Museum, and the Notre-Dame Cathedral. Paris has a rich history, being a major hub for art, fashion, literature, and cuisine. It attracts millions of tourists and is considered one of the most romantic and vibrant cities in the world. The city is also home to many international organizations and embassies due to its global influence. \nNote: The information provided is accurate as of July ",
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+          "text": " The capital of France is Paris. \nExplanation: Paris is the largest city in France and serves as the country's political, economic, and cultural center. It is known for its iconic landmarks like the Eiffel Tower, the Louvre Museum, and the Notre-Dame Cathedral. Paris has a rich history, being a major hub for art, fashion, literature, and cuisine. It attracts millions of tourists and is considered one of the most romantic and vibrant cities in the world. The city is also home to many international organizations and embassies due to its global influence. \nNote: The information provided is accurate as of July ",
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+          "text": " The capital of France is Paris. \nExplanation: Paris is the largest city in France and serves as the country's political, economic, and cultural center. It is known for its iconic landmarks like the Eiffel Tower, the Louvre Museum, and the Notre-Dame Cathedral. Paris has a rich history, being a major hub for art, fashion, literature, and cuisine. It attracts millions of tourists and is considered one of the most romantic and vibrant cities in the world. The city is also home to many international organizations and embassies due to its global influence. \nNote: The information provided is accurate as of July ",
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+          "text": " The capital of France is Paris. \nExplanation: Paris is the largest city in France and serves as the country's political, economic, and cultural center. It is known for its iconic landmarks like the Eiffel Tower, the Louvre Museum, and the Notre-Dame Cathedral. Paris has a rich history, being a major hub for art, fashion, literature, and cuisine. It attracts millions of tourists and is considered one of the most romantic and vibrant cities in the world. The city is also home to many international organizations and embassies due to its global influence. \nNote: The information provided is accurate as of July ",
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+          "text": " The capital of France is Paris. \nExplanation: Paris is the largest city in France and serves as the country's political, economic, and cultural center. It is known for its iconic landmarks like the Eiffel Tower, the Louvre Museum, and the Notre-Dame Cathedral. Paris has a rich history, being a major hub for art, fashion, literature, and cuisine. It attracts millions of tourists and is considered one of the most romantic and vibrant cities in the world. The city is also home to many international organizations and embassies due to its global influence. \nNote: The information provided is accurate as of July ",
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+          "text": " The capital of France is Paris. \nExplanation: Paris is the largest city in France and serves as the country's political, economic, and cultural center. It is known for its iconic landmarks like the Eiffel Tower, the Louvre Museum, and the Notre-Dame Cathedral. Paris has a rich history, being a major hub for art, fashion, literature, and cuisine. It attracts millions of tourists and is considered one of the most romantic and vibrant cities in the world. The city is also home to many international organizations and embassies due to its global influence. \nNote: The information provided is accurate as of July ",
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+          "text": " The capital of France is Paris. \nExplanation: Paris is the largest city in France and serves as the country's political, economic, and cultural center. It is known for its iconic landmarks like the Eiffel Tower, the Louvre Museum, and the Notre-Dame Cathedral. Paris has a rich history, being a major hub for art, fashion, literature, and cuisine. It attracts millions of tourists and is considered one of the most romantic and vibrant cities in the world. The city is also home to many international organizations and embassies due to its global influence. \nNote: The information provided is accurate as of July ",
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+          "text": " The capital of France is Paris. \nExplanation: Paris is the largest city in France and serves as the country's political, economic, and cultural center. It is known for its iconic landmarks like the Eiffel Tower, the Louvre Museum, and the Notre-Dame Cathedral. Paris has a rich history, being a major hub for art, fashion, literature, and cuisine. It attracts millions of tourists and is considered one of the most romantic and vibrant cities in the world. The city is also home to many international organizations and embassies due to its global influence. \nNote: The information provided is accurate as of July ",
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+          "text": " The capital of France is Paris. \nExplanation: Paris is the largest city in France and serves as the country's political, economic, and cultural center. It is known for its iconic landmarks like the Eiffel Tower, the Louvre Museum, and the Notre-Dame Cathedral. Paris has a rich history, being a major hub for art, fashion, literature, and cuisine. It attracts millions of tourists and is considered one of the most romantic and vibrant cities in the world. The city is also home to many international organizations and embassies due to its global influence. \nNote: The information provided is accurate as of July ",
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+          "text": " The capital of France is Paris. \nExplanation: Paris is the largest city in France and serves as the country's political, economic, and cultural center. It is known for its iconic landmarks like the Eiffel Tower, the Louvre Museum, and the Notre-Dame Cathedral. Paris has a rich history, being a major hub for art, fashion, literature, and cuisine. It attracts millions of tourists and is considered one of the most romantic and vibrant cities in the world. The city is also home to many international organizations and embassies due to its global influence. \nNote: The information provided is accurate as of July ",
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+          "text": " Paris\nExplanation: Paris is the capital city of France. It is located in the northern part of the country and is known for its rich history, cultural landmarks",
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+          "text": " Paris\nExplanation: Paris is the capital city of France. It is located in the northern part of the country and is known for its rich history, cultural landmarks",
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+          "text": " Paris\nExplanation: Paris is the capital city of France. It is located in the northern part of the country and is known for its rich history, cultural landmarks",
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+          "text": " Paris\nExplanation: Paris is the capital city of France. It is located in the northern part of the country and is known for its rich history, cultural landmarks",
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+          "text": " Paris\nExplanation: Paris is the capital city of France. It is located in the northern part of the country and is known for its rich history, cultural landmarks",
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+          "text": " Paris\nExplanation: Paris is the capital city of France. It is located in the northern part of the country and is known for its rich history, cultural landmarks",
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+          "text": " Paris\nExplanation: Paris is the capital city of France. It is located in the northern part of the country and is known for its rich history, cultural landmarks",
+          "text_equal": true,
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+          "text": " Paris\nExplanation: Paris is the capital city of France. It is located in the northern part of the country and is known for its rich history, cultural landmarks",
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+          "text": " Paris\nExplanation: Paris is the capital city of France. It is located in the northern part of the country and is known for its rich history, cultural landmarks",
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+          "text": " Paris\nExplanation: Paris is the capital city of France. It is located in the northern part of the country and is known for its rich history, cultural landmarks",
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+          "text": " Paris\nExplanation: Paris is the capital city of France. It is located in the northern part of the country and is known for its rich history, cultural landmarks",
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+          "text": " Paris\nExplanation: Paris is the capital city of France. It is located in the northern part of the country and is known for its rich history, cultural landmarks",
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+          "text": " Paris\nExplanation: Paris is the capital city of France. It is located in the northern part of the country and is known for its rich history, cultural landmarks",
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+          "text": " Paris\nExplanation: Paris is the capital city of France. It is located in the northern part of the country and is known for its rich history, cultural landmarks",
+          "text_equal": true,
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+          "text": " Paris\nExplanation: Paris is the capital city of France. It is located in the northern part of the country and is known for its rich history, cultural landmarks, and iconic architecture. Paris has been a center of politics, art, fashion, and cuisine for centuries and is considered one of the most romantic and visited cities in the world. The Eiffel Tower, the Louvre Museum, and Notre-Dame Cathedral are just a few of the many famous attractions that can be found in Paris. The city is also famous for its beautiful parks, gardens, and river Seine, which runs through its heart. Paris is a major global",
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+          "text": " Paris\nExplanation: Paris is the capital city of France. It is located in the northern part of the country and is known for its rich history, cultural landmarks, and iconic architecture. Paris has been a center of politics, art, fashion, and cuisine for centuries and is considered one of the most romantic and visited cities in the world. The Eiffel Tower, the Louvre Museum, and Notre-Dame Cathedral are just a few of the many famous attractions that can be found in Paris. The city is also famous for its beautiful parks, gardens, and river Seine, which runs through its heart. Paris is a major global",
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+          "text": " Paris\nExplanation: Paris is the capital city of France. It is located in the northern part of the country and is known for its rich history, cultural landmarks, and iconic architecture. Paris has been a center of politics, art, fashion, and cuisine for centuries and is considered one of the most romantic and visited cities in the world. The Eiffel Tower, the Louvre Museum, and Notre-Dame Cathedral are just a few of the many famous attractions that can be found in Paris. The city is also famous for its beautiful parks, gardens, and river Seine, which runs through its heart. Paris is a major global",
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+          "text": " Paris\nExplanation: Paris is the capital city of France. It is located in the northern part of the country and is known for its rich history, cultural landmarks, and iconic architecture. Paris has been a center of politics, art, fashion, and cuisine for centuries and is considered one of the most romantic and visited cities in the world. The Eiffel Tower, the Louvre Museum, and Notre-Dame Cathedral are just a few of the many famous attractions that can be found in Paris. The city is also famous for its beautiful parks, gardens, and river Seine, which runs through its heart. Paris is a major global",
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+          "text": " Paris\nExplanation: Paris is the capital city of France. It is located in the northern part of the country and is known for its rich history, cultural landmarks, and iconic architecture. Paris has been a center of politics, art, fashion, and cuisine for centuries and is considered one of the most romantic and visited cities in the world. The Eiffel Tower, the Louvre Museum, and Notre-Dame Cathedral are just a few of the many famous attractions that can be found in Paris. The city is also famous for its beautiful parks, gardens, and river Seine, which runs through its heart. Paris is a major global",
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+          "text": " Paris\nExplanation: Paris is the capital city of France. It is located in the northern part of the country and is known for its rich history, cultural landmarks, and iconic architecture. Paris has been a center of politics, art, fashion, and cuisine for centuries and is considered one of the most romantic and visited cities in the world. The Eiffel Tower, the Louvre Museum, and Notre-Dame Cathedral are just a few of the many famous attractions that can be found in Paris. The city is also famous for its beautiful parks, gardens, and river Seine, which runs through its heart. Paris is a major global",
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+          "text": " Paris\nExplanation: Paris is the capital city of France. It is located in the northern part of the country and is known for its rich history, cultural landmarks, and iconic architecture. Paris has been a center of politics, art, fashion, and cuisine for centuries and is considered one of the most romantic and visited cities in the world. The Eiffel Tower, the Louvre Museum, and Notre-Dame Cathedral are just a few of the many famous attractions that can be found in Paris. The city is also famous for its beautiful parks, gardens, and river Seine, which runs through its heart. Paris is a major global",
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+          "text": " Paris\nExplanation: Paris is the capital city of France. It is located in the northern part of the country and is known for its rich history, cultural landmarks, and iconic architecture. Paris has been a center of politics, art, fashion, and cuisine for centuries and is considered one of the most romantic and visited cities in the world. The Eiffel Tower, the Louvre Museum, and Notre-Dame Cathedral are just a few of the many famous attractions that can be found in Paris. The city is also famous for its beautiful parks, gardens, and river Seine, which runs through its heart. Paris is a major global",
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+          "text": " Paris\nExplanation: Paris is the capital city of France. It is located in the northern part of the country and is known for its rich history, cultural landmarks, and iconic architecture. Paris has been a center of politics, art, fashion, and cuisine for centuries and is considered one of the most romantic and visited cities in the world. The Eiffel Tower, the Louvre Museum, and Notre-Dame Cathedral are just a few of the many famous attractions that can be found in Paris. The city is also famous for its beautiful parks, gardens, and river Seine, which runs through its heart. Paris is a major global",
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+          "text": " Paris\nExplanation: Paris is the capital city of France. It is located in the northern part of the country and is known for its rich history, cultural landmarks, and iconic architecture. Paris has been a center of politics, art, fashion, and cuisine for centuries and is considered one of the most romantic and visited cities in the world. The Eiffel Tower, the Louvre Museum, and Notre-Dame Cathedral are just a few of the many famous attractions that can be found in Paris. The city is also famous for its beautiful parks, gardens, and river Seine, which runs through its heart. Paris is a major global",
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+          "text": " Paris\nExplanation: Paris is the capital city of France. It is located in the northern part of the country and is known for its rich history, cultural landmarks, and iconic architecture. Paris has been a center of politics, art, fashion, and cuisine for centuries and is considered one of the most romantic and visited cities in the world. The Eiffel Tower, the Louvre Museum, and Notre-Dame Cathedral are just a few of the many famous attractions that can be found in Paris. The city is also famous for its beautiful parks, gardens, and river Seine, which runs through its heart. Paris is a major global",
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+          "text": " Paris\nExplanation: Paris is the capital city of France. It is located in the northern part of the country and is known for its rich history, cultural landmarks, and iconic architecture. Paris has been a center of politics, art, fashion, and cuisine for centuries and is considered one of the most romantic and visited cities in the world. The Eiffel Tower, the Louvre Museum, and Notre-Dame Cathedral are just a few of the many famous attractions that can be found in Paris. The city is also famous for its beautiful parks, gardens, and river Seine, which runs through its heart. Paris is a major global",
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+          "text": " Paris\nExplanation: Paris is the capital city of France. It is located in the northern part of the country and is known for its rich history, cultural landmarks, and iconic architecture. Paris has been a center of politics, art, fashion, and cuisine for centuries and is considered one of the most romantic and visited cities in the world. The Eiffel Tower, the Louvre Museum, and Notre-Dame Cathedral are just a few of the many famous attractions that can be found in Paris. The city is also famous for its beautiful parks, gardens, and river Seine, which runs through its heart. Paris is a major global",
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+          "text": " Paris\nExplanation: Paris is the capital city of France. It is located in the northern part of the country and is known for its rich history, cultural landmarks, and iconic architecture. Paris has been a center of politics, art, fashion, and cuisine for centuries and is considered one of the most romantic and visited cities in the world. The Eiffel Tower, the Louvre Museum, and Notre-Dame Cathedral are just a few of the many famous attractions that can be found in Paris. The city is also famous for its beautiful parks, gardens, and river Seine, which runs through its heart. Paris is a major global",
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+          "text": " Paris\nExplanation: Paris is the capital city of France. It is located in the northern part of the country and is known for its rich history, cultural landmarks, and iconic architecture. Paris has been a center of politics, art, fashion, and cuisine for centuries and is considered one of the most romantic and visited cities in the world. The Eiffel Tower, the Louvre Museum, and Notre-Dame Cathedral are just a few of the many famous attractions that can be found in Paris. The city is also famous for its beautiful parks, gardens, and river Seine, which runs through its heart. Paris is a major global",
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+          "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the",
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+          "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the",
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+          "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the",
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+          "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the",
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+          "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the",
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+          "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the",
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+          "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the Eiffel Tower, the Louvre Museum, and Notre-Dame Cathedral. Paris is also a major center for culture, fashion, and international politics. The city has a rich history and has been the capital of France since the 12th century. It is a popular tourist destination and a hub for art, cuisine, and architecture. The city's beautiful streets, charming neighborhoods, and vibrant cultural scene make it a must-visit destination for many travelers. Paris is also home",
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+          "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the Eiffel Tower, the Louvre Museum, and Notre-Dame Cathedral. Paris is also a major center for culture, fashion, and international politics. The city has a rich history and has been the capital of France since the 12th century. It is a popular tourist destination and a hub for art, cuisine, and architecture. The city's beautiful streets, charming neighborhoods, and vibrant cultural scene make it a must-visit destination for many travelers. Paris is also home",
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+          "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the Eiffel Tower, the Louvre Museum, and Notre-Dame Cathedral. Paris is also a major center for culture, fashion, and international politics. The city has a rich history and has been the capital of France since the 12th century. It is a popular tourist destination and a hub for art, cuisine, and architecture. The city's beautiful streets, charming neighborhoods, and vibrant cultural scene make it a must-visit destination for many travelers. Paris is also home",
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+          "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the Eiffel Tower, the Louvre Museum, and Notre-Dame Cathedral. Paris is also a major center for culture, fashion, and international politics. The city has a rich history and has been the capital of France since the 12th century. It is a popular tourist destination and a hub for art, cuisine, and architecture. The city's beautiful streets, charming neighborhoods, and vibrant cultural scene make it a must-visit destination for many travelers. Paris is also home",
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+          "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the Eiffel Tower, the Louvre Museum, and Notre-Dame Cathedral. Paris is also a major center for culture, fashion, and international politics. The city has a rich history and has been the capital of France since the 12th century. It is a popular tourist destination and a hub for art, cuisine, and architecture. The city's beautiful streets, charming neighborhoods, and vibrant cultural scene make it a must-visit destination for many travelers. Paris is also home",
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+          "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the Eiffel Tower, the Louvre Museum, and Notre-Dame Cathedral. Paris is also a major center for culture, fashion, and international politics. The city has a rich history and has been the capital of France since the 12th century. It is a popular tourist destination and a hub for art, cuisine, and architecture. The city's beautiful streets, charming neighborhoods, and vibrant cultural scene make it a must-visit destination for many travelers. Paris is also home",
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+          "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the Eiffel Tower, the Louvre Museum, and Notre-Dame Cathedral. Paris is also a major center for culture, fashion, and international politics. The city has a rich history and has been the capital of France since the 12th century. It is a popular tourist destination and a hub for art, cuisine, and architecture. The city's beautiful streets, charming neighborhoods, and vibrant cultural scene make it a must-visit destination for many travelers. Paris is also home",
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+        1690,
+        40386,
+        13,
+        12366,
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+        1101,
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+      ],
+      "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the Eiffel Tower, the Louvre Museum, and Notre-Dame Cathedral. Paris is also a major center for culture, fashion, and international politics. The city has a rich history and has been the capital of France since the 12th century. It is a popular tourist destination and a hub for art, cuisine, and architecture. The city's beautiful streets, charming neighborhoods, and vibrant cultural scene make it a must-visit destination for many travelers. Paris is also home"
+    }
+  },
+  "warmup_and_checks_s": 1056.6201489599189,
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32\nvllm:e2e_request_latency_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"15.0\"} 47\nvllm:e2e_request_latency_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"20.0\"} 68\nvllm:e2e_request_latency_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"30.0\"} 98\nvllm:e2e_request_latency_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"40.0\"} 126\nvllm:e2e_request_latency_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"50.0\"} 142\nvllm:e2e_request_latency_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"60.0\"} 156\nvllm:e2e_request_latency_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"+Inf\"} 264\nvllm:e2e_request_latency_seconds_sum{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\"} 16985.677578761126\nvllm:e2e_request_latency_seconds_count{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\"} 264\n# HELP airm_kv_transfer_seconds KV 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+    "servers": [
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+        "returncode": null,
+        "status": [
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+          "State:\tS (sleeping)",
+          "VmHWM:\t19906736 kB",
+          "VmRSS:\t19906736 kB",
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+    "cgroup": {
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+      "memory.current": "20677926912",
+      "memory.max": "137438953472",
+      "memory.peak": "20688846848"
+    },
+    "listen": {
+      "ListenOverflows": 0,
+      "ListenDrops": 0
+    }
+  }
+}
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/http267d-koni-atom-c16-stream.json b/opt_prime/examples/pd/http_matrix/evidence265/http267d-koni-atom-c16-stream.json
new file mode 100644
index 00000000..fa5ae746
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/http267d-koni-atom-c16-stream.json
@@ -0,0 +1,25279 @@
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+          "tokens_equal": true
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+          "text": " The capital of France is Paris. \nExplanation: Paris is the largest city in France and serves as the country's political, economic, and cultural center. It",
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+          "text": " The capital of France is Paris. \nExplanation: Paris is the largest city in France and serves as the country's political, economic, and cultural center. It",
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+          "text": " The capital of France is Paris. \nExplanation: Paris is the largest city in France and serves as the country's political, economic, and cultural center. It is known for its iconic landmarks like the Eiffel Tower, the Louvre Museum, and the Notre-Dame Cathedral. Paris has a rich history, being a major hub for art, fashion, literature, and cuisine. It attracts millions of tourists and is considered one of the most romantic and vibrant cities in the world. The city is also home to many international organizations and embassies due to its global influence. \nNote: The information provided is accurate as of July ",
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+          "text": " The capital of France is Paris. \nExplanation: Paris is the largest city in France and serves as the country's political, economic, and cultural center. It is known for its iconic landmarks like the Eiffel Tower, the Louvre Museum, and the Notre-Dame Cathedral. Paris has a rich history, being a major hub for art, fashion, literature, and cuisine. It attracts millions of tourists and is considered one of the most romantic and vibrant cities in the world. The city is also home to many international organizations and embassies due to its global influence. \nNote: The information provided is accurate as of July ",
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+          "text": " The capital of France is Paris. \nExplanation: Paris is the largest city in France and serves as the country's political, economic, and cultural center. It is known for its iconic landmarks like the Eiffel Tower, the Louvre Museum, and the Notre-Dame Cathedral. Paris has a rich history, being a major hub for art, fashion, literature, and cuisine. It attracts millions of tourists and is considered one of the most romantic and vibrant cities in the world. The city is also home to many international organizations and embassies due to its global influence. \nNote: The information provided is accurate as of July ",
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+          "text": " The capital of France is Paris. \nExplanation: Paris is the largest city in France and serves as the country's political, economic, and cultural center. It is known for its iconic landmarks like the Eiffel Tower, the Louvre Museum, and the Notre-Dame Cathedral. Paris has a rich history, being a major hub for art, fashion, literature, and cuisine. It attracts millions of tourists and is considered one of the most romantic and vibrant cities in the world. The city is also home to many international organizations and embassies due to its global influence. \nNote: The information provided is accurate as of July ",
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+          "text": " The capital of France is Paris. \nExplanation: Paris is the largest city in France and serves as the country's political, economic, and cultural center. It is known for its iconic landmarks like the Eiffel Tower, the Louvre Museum, and the Notre-Dame Cathedral. Paris has a rich history, being a major hub for art, fashion, literature, and cuisine. It attracts millions of tourists and is considered one of the most romantic and vibrant cities in the world. The city is also home to many international organizations and embassies due to its global influence. \nNote: The information provided is accurate as of July ",
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+          "text": " The capital of France is Paris. \nExplanation: Paris is the largest city in France and serves as the country's political, economic, and cultural center. It is known for its iconic landmarks like the Eiffel Tower, the Louvre Museum, and the Notre-Dame Cathedral. Paris has a rich history, being a major hub for art, fashion, literature, and cuisine. It attracts millions of tourists and is considered one of the most romantic and vibrant cities in the world. The city is also home to many international organizations and embassies due to its global influence. \nNote: The information provided is accurate as of July ",
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+          "text": " The capital of France is Paris. \nExplanation: Paris is the largest city in France and serves as the country's political, economic, and cultural center. It is known for its iconic landmarks like the Eiffel Tower, the Louvre Museum, and the Notre-Dame Cathedral. Paris has a rich history, being a major hub for art, fashion, literature, and cuisine. It attracts millions of tourists and is considered one of the most romantic and vibrant cities in the world. The city is also home to many international organizations and embassies due to its global influence. \nNote: The information provided is accurate as of July ",
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+          "text": " The capital of France is Paris. \nExplanation: Paris is the largest city in France and serves as the country's political, economic, and cultural center. It is known for its iconic landmarks like the Eiffel Tower, the Louvre Museum, and the Notre-Dame Cathedral. Paris has a rich history, being a major hub for art, fashion, literature, and cuisine. It attracts millions of tourists and is considered one of the most romantic and vibrant cities in the world. The city is also home to many international organizations and embassies due to its global influence. \nNote: The information provided is accurate as of July ",
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+          "text": " The capital of France is Paris. \nExplanation: Paris is the largest city in France and serves as the country's political, economic, and cultural center. It is known for its iconic landmarks like the Eiffel Tower, the Louvre Museum, and the Notre-Dame Cathedral. Paris has a rich history, being a major hub for art, fashion, literature, and cuisine. It attracts millions of tourists and is considered one of the most romantic and vibrant cities in the world. The city is also home to many international organizations and embassies due to its global influence. \nNote: The information provided is accurate as of July ",
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+          "text": " The capital of France is Paris. \nExplanation: Paris is the largest city in France and serves as the country's political, economic, and cultural center. It is known for its iconic landmarks like the Eiffel Tower, the Louvre Museum, and the Notre-Dame Cathedral. Paris has a rich history, being a major hub for art, fashion, literature, and cuisine. It attracts millions of tourists and is considered one of the most romantic and vibrant cities in the world. The city is also home to many international organizations and embassies due to its global influence. \nNote: The information provided is accurate as of July ",
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+          "text": " The capital of France is Paris. \nExplanation: Paris is the largest city in France and serves as the country's political, economic, and cultural center. It is known for its iconic landmarks like the Eiffel Tower, the Louvre Museum, and the Notre-Dame Cathedral. Paris has a rich history, being a major hub for art, fashion, literature, and cuisine. It attracts millions of tourists and is considered one of the most romantic and vibrant cities in the world. The city is also home to many international organizations and embassies due to its global influence. \nNote: The information provided is accurate as of July ",
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+          "text": " The capital of France is Paris. \nExplanation: Paris is the largest city in France and serves as the country's political, economic, and cultural center. It is known for its iconic landmarks like the Eiffel Tower, the Louvre Museum, and the Notre-Dame Cathedral. Paris has a rich history, being a major hub for art, fashion, literature, and cuisine. It attracts millions of tourists and is considered one of the most romantic and vibrant cities in the world. The city is also home to many international organizations and embassies due to its global influence. \nNote: The information provided is accurate as of July ",
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+          "text": " The capital of France is Paris. \nExplanation: Paris is the largest city in France and serves as the country's political, economic, and cultural center. It is known for its iconic landmarks like the Eiffel Tower, the Louvre Museum, and the Notre-Dame Cathedral. Paris has a rich history, being a major hub for art, fashion, literature, and cuisine. It attracts millions of tourists and is considered one of the most romantic and vibrant cities in the world. The city is also home to many international organizations and embassies due to its global influence. \nNote: The information provided is accurate as of July ",
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+          "text": " The capital of France is Paris. \nExplanation: Paris is the largest city in France and serves as the country's political, economic, and cultural center. It is known for its iconic landmarks like the Eiffel Tower, the Louvre Museum, and the Notre-Dame Cathedral. Paris has a rich history, being a major hub for art, fashion, literature, and cuisine. It attracts millions of tourists and is considered one of the most romantic and vibrant cities in the world. The city is also home to many international organizations and embassies due to its global influence. \nNote: The information provided is accurate as of July ",
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+          "text": " The capital of France is Paris. \nExplanation: Paris is the largest city in France and serves as the country's political, economic, and cultural center. It is known for its iconic landmarks like the Eiffel Tower, the Louvre Museum, and the Notre-Dame Cathedral. Paris has a rich history, being a major hub for art, fashion, literature, and cuisine. It attracts millions of tourists and is considered one of the most romantic and vibrant cities in the world. The city is also home to many international organizations and embassies due to its global influence. \nNote: The information provided is accurate as of July ",
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+          "text": " The capital of France is Paris. \nExplanation: Paris is the largest city in France and serves as the country's political, economic, and cultural center. It is known for its iconic landmarks like the Eiffel Tower, the Louvre Museum, and the Notre-Dame Cathedral. Paris has a rich history, being a major hub for art, fashion, literature, and cuisine. It attracts millions of tourists and is considered one of the most romantic and vibrant cities in the world. The city is also home to many international organizations and embassies due to its global influence. \nNote: The information provided is accurate as of July ",
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+          "text": " Paris\nExplanation: Paris is the capital city of France. It is located in the northern part of the country and is known for its rich history, cultural landmarks",
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+          "text": " Paris\nExplanation: Paris is the capital city of France. It is located in the northern part of the country and is known for its rich history, cultural landmarks",
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+          "text": " Paris\nExplanation: Paris is the capital city of France. It is located in the northern part of the country and is known for its rich history, cultural landmarks",
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+          "text": " Paris\nExplanation: Paris is the capital city of France. It is located in the northern part of the country and is known for its rich history, cultural landmarks",
+          "text_equal": true,
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+          "text": " Paris\nExplanation: Paris is the capital city of France. It is located in the northern part of the country and is known for its rich history, cultural landmarks",
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+          "text": " Paris\nExplanation: Paris is the capital city of France. It is located in the northern part of the country and is known for its rich history, cultural landmarks, and iconic architecture. Paris has been a center of politics, art, fashion, and cuisine for centuries and is considered one of the most romantic and visited cities in the world. The Eiffel Tower, the Louvre Museum, and Notre-Dame Cathedral are just a few of the many famous attractions that can be found in Paris. The city is also famous for its beautiful parks, gardens, and river Seine, which runs through its heart. Paris is a major global",
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+          "text": " Paris\nExplanation: Paris is the capital city of France. It is located in the northern part of the country and is known for its rich history, cultural landmarks, and iconic architecture. Paris has been a center of politics, art, fashion, and cuisine for centuries and is considered one of the most romantic and visited cities in the world. The Eiffel Tower, the Louvre Museum, and Notre-Dame Cathedral are just a few of the many famous attractions that can be found in Paris. The city is also famous for its beautiful parks, gardens, and river Seine, which runs through its heart. Paris is a major global",
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+          "text": " Paris\nExplanation: Paris is the capital city of France. It is located in the northern part of the country and is known for its rich history, cultural landmarks, and iconic architecture. Paris has been a center of politics, art, fashion, and cuisine for centuries and is considered one of the most romantic and visited cities in the world. The Eiffel Tower, the Louvre Museum, and Notre-Dame Cathedral are just a few of the many famous attractions that can be found in Paris. The city is also famous for its beautiful parks, gardens, and river Seine, which runs through its heart. Paris is a major global",
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+          "text": " Paris\nExplanation: Paris is the capital city of France. It is located in the northern part of the country and is known for its rich history, cultural landmarks, and iconic architecture. Paris has been a center of politics, art, fashion, and cuisine for centuries and is considered one of the most romantic and visited cities in the world. The Eiffel Tower, the Louvre Museum, and Notre-Dame Cathedral are just a few of the many famous attractions that can be found in Paris. The city is also famous for its beautiful parks, gardens, and river Seine, which runs through its heart. Paris is a major global",
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+          "text": " Paris\nExplanation: Paris is the capital city of France. It is located in the northern part of the country and is known for its rich history, cultural landmarks, and iconic architecture. Paris has been a center of politics, art, fashion, and cuisine for centuries and is considered one of the most romantic and visited cities in the world. The Eiffel Tower, the Louvre Museum, and Notre-Dame Cathedral are just a few of the many famous attractions that can be found in Paris. The city is also famous for its beautiful parks, gardens, and river Seine, which runs through its heart. Paris is a major global",
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+          "text": " Paris\nExplanation: Paris is the capital city of France. It is located in the northern part of the country and is known for its rich history, cultural landmarks, and iconic architecture. Paris has been a center of politics, art, fashion, and cuisine for centuries and is considered one of the most romantic and visited cities in the world. The Eiffel Tower, the Louvre Museum, and Notre-Dame Cathedral are just a few of the many famous attractions that can be found in Paris. The city is also famous for its beautiful parks, gardens, and river Seine, which runs through its heart. Paris is a major global",
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+          "text": " Paris\nExplanation: Paris is the capital city of France. It is located in the northern part of the country and is known for its rich history, cultural landmarks, and iconic architecture. Paris has been a center of politics, art, fashion, and cuisine for centuries and is considered one of the most romantic and visited cities in the world. The Eiffel Tower, the Louvre Museum, and Notre-Dame Cathedral are just a few of the many famous attractions that can be found in Paris. The city is also famous for its beautiful parks, gardens, and river Seine, which runs through its heart. Paris is a major global",
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+          "text": " Paris\nExplanation: Paris is the capital city of France. It is located in the northern part of the country and is known for its rich history, cultural landmarks, and iconic architecture. Paris has been a center of politics, art, fashion, and cuisine for centuries and is considered one of the most romantic and visited cities in the world. The Eiffel Tower, the Louvre Museum, and Notre-Dame Cathedral are just a few of the many famous attractions that can be found in Paris. The city is also famous for its beautiful parks, gardens, and river Seine, which runs through its heart. Paris is a major global",
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+          "text": " Paris\nExplanation: Paris is the capital city of France. It is located in the northern part of the country and is known for its rich history, cultural landmarks, and iconic architecture. Paris has been a center of politics, art, fashion, and cuisine for centuries and is considered one of the most romantic and visited cities in the world. The Eiffel Tower, the Louvre Museum, and Notre-Dame Cathedral are just a few of the many famous attractions that can be found in Paris. The city is also famous for its beautiful parks, gardens, and river Seine, which runs through its heart. Paris is a major global",
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+          "text": " Paris\nExplanation: Paris is the capital city of France. It is located in the northern part of the country and is known for its rich history, cultural landmarks, and iconic architecture. Paris has been a center of politics, art, fashion, and cuisine for centuries and is considered one of the most romantic and visited cities in the world. The Eiffel Tower, the Louvre Museum, and Notre-Dame Cathedral are just a few of the many famous attractions that can be found in Paris. The city is also famous for its beautiful parks, gardens, and river Seine, which runs through its heart. Paris is a major global",
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+          "text": " Paris\nExplanation: Paris is the capital city of France. It is located in the northern part of the country and is known for its rich history, cultural landmarks, and iconic architecture. Paris has been a center of politics, art, fashion, and cuisine for centuries and is considered one of the most romantic and visited cities in the world. The Eiffel Tower, the Louvre Museum, and Notre-Dame Cathedral are just a few of the many famous attractions that can be found in Paris. The city is also famous for its beautiful parks, gardens, and river Seine, which runs through its heart. Paris is a major global",
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+          "text": " Paris\nExplanation: Paris is the capital city of France. It is located in the northern part of the country and is known for its rich history, cultural landmarks, and iconic architecture. Paris has been a center of politics, art, fashion, and cuisine for centuries and is considered one of the most romantic and visited cities in the world. The Eiffel Tower, the Louvre Museum, and Notre-Dame Cathedral are just a few of the many famous attractions that can be found in Paris. The city is also famous for its beautiful parks, gardens, and river Seine, which runs through its heart. Paris is a major global",
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+          "text": " Paris\nExplanation: Paris is the capital city of France. It is located in the northern part of the country and is known for its rich history, cultural landmarks, and iconic architecture. Paris has been a center of politics, art, fashion, and cuisine for centuries and is considered one of the most romantic and visited cities in the world. The Eiffel Tower, the Louvre Museum, and Notre-Dame Cathedral are just a few of the many famous attractions that can be found in Paris. The city is also famous for its beautiful parks, gardens, and river Seine, which runs through its heart. Paris is a major global",
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+          "text": " Paris\nExplanation: Paris is the capital city of France. It is located in the northern part of the country and is known for its rich history, cultural landmarks, and iconic architecture. Paris has been a center of politics, art, fashion, and cuisine for centuries and is considered one of the most romantic and visited cities in the world. The Eiffel Tower, the Louvre Museum, and Notre-Dame Cathedral are just a few of the many famous attractions that can be found in Paris. The city is also famous for its beautiful parks, gardens, and river Seine, which runs through its heart. Paris is a major global",
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+          "text": " Paris\nExplanation: Paris is the capital city of France. It is located in the northern part of the country and is known for its rich history, cultural landmarks, and iconic architecture. Paris has been a center of politics, art, fashion, and cuisine for centuries and is considered one of the most romantic and visited cities in the world. The Eiffel Tower, the Louvre Museum, and Notre-Dame Cathedral are just a few of the many famous attractions that can be found in Paris. The city is also famous for its beautiful parks, gardens, and river Seine, which runs through its heart. Paris is a major global",
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+          "text": " Paris\nExplanation: Paris is the capital city of France. It is located in the northern part of the country and is known for its rich history, cultural landmarks, and iconic architecture. Paris has been a center of politics, art, fashion, and cuisine for centuries and is considered one of the most romantic and visited cities in the world. The Eiffel Tower, the Louvre Museum, and Notre-Dame Cathedral are just a few of the many famous attractions that can be found in Paris. The city is also famous for its beautiful parks, gardens, and river Seine, which runs through its heart. Paris is a major global",
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+          "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the",
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+          "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the",
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+      "text": " The capital of France is Paris. \nExplanation: Paris is the largest city in France and serves as the country's political, economic, and cultural center. It is known for its iconic landmarks like the Eiffel Tower, the Louvre Museum, and the Notre-Dame Cathedral. Paris has a rich history, being a major hub for art, fashion, literature, and cuisine. It attracts millions of tourists and is considered one of the most romantic and vibrant cities in the world. The city is also home to many international organizations and embassies due to its global influence. \nNote: The information provided is accurate as of July "
+    },
+    "2048:32": {
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+      ],
+      "text": " Paris\nExplanation: Paris is the capital city of France. It is located in the northern part of the country and is known for its rich history, cultural landmarks"
+    },
+    "2048:128": {
+      "tokens": [
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+      "text": " Paris\nExplanation: Paris is the capital city of France. It is located in the northern part of the country and is known for its rich history, cultural landmarks, and iconic architecture. Paris has been a center of politics, art, fashion, and cuisine for centuries and is considered one of the most romantic and visited cities in the world. The Eiffel Tower, the Louvre Museum, and Notre-Dame Cathedral are just a few of the many famous attractions that can be found in Paris. The city is also famous for its beautiful parks, gardens, and river Seine, which runs through its heart. Paris is a major global"
+    },
+    "8192:32": {
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+      ],
+      "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the"
+    },
+    "8192:128": {
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+      ],
+      "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the Eiffel Tower, Notre-Dame Cathedral, and the Louvre Museum. Paris is also renowned for its rich history, cultural heritage, and its status as a global center for fashion, art, and cuisine. The city is divided into twenty arrondissements, each with its own distinct character and attractions. Paris is a popular tourist destination and a major hub for international business and diplomacy. The city's beautiful architecture, picturesque streets, and vibrant cultural scene make it one of"
+    }
+  },
+  "warmup_and_checks_s": 1135.453472825,
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output token after the first (decode stage).\n# TYPE vllm:time_per_output_token_seconds histogram\nvllm:time_per_output_token_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"0.01\"} 0\nvllm:time_per_output_token_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"0.025\"} 0\nvllm:time_per_output_token_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"0.05\"} 0\nvllm:time_per_output_token_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"0.075\"} 264\nvllm:time_per_output_token_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"0.1\"} 264\nvllm:time_per_output_token_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"0.15\"} 264\nvllm:time_per_output_token_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"0.2\"} 264\nvllm:time_per_output_token_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"0.3\"} 264\nvllm:time_per_output_token_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"0.4\"} 264\nvllm:time_per_output_token_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"0.5\"} 264\nvllm:time_per_output_token_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"0.75\"} 264\nvllm:time_per_output_token_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"1.0\"} 264\nvllm:time_per_output_token_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"2.5\"} 264\nvllm:time_per_output_token_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"+Inf\"} 264\nvllm:time_per_output_token_seconds_sum{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\"} 18.115104687188268\nvllm:time_per_output_token_seconds_count{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\"} 264\n# HELP vllm:e2e_request_latency_seconds Stage request latency, from execution start to reply.\n# TYPE 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18\nvllm:e2e_request_latency_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"15.0\"} 38\nvllm:e2e_request_latency_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"20.0\"} 46\nvllm:e2e_request_latency_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"30.0\"} 70\nvllm:e2e_request_latency_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"40.0\"} 94\nvllm:e2e_request_latency_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"50.0\"} 118\nvllm:e2e_request_latency_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"60.0\"} 142\nvllm:e2e_request_latency_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"+Inf\"} 264\nvllm:e2e_request_latency_seconds_sum{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\"} 17933.441103998455\nvllm:e2e_request_latency_seconds_count{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\"} 264\n# HELP airm_kv_transfer_seconds KV 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+  "diagnostic_end": {
+    "monotonic_s": 796086.432592678,
+    "servers": [
+      {
+        "pid": 38,
+        "returncode": null,
+        "status": [
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+          "State:\tS (sleeping)",
+          "VmHWM:\t22118284 kB",
+          "VmRSS:\t22050092 kB",
+          "Threads:\t13"
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+    "cgroup": {
+      "memory.events": "low 0\nhigh 0\nmax 0\noom 0\noom_kill 0\noom_group_kill 0",
+      "memory.current": "36119887872",
+      "memory.max": "137438953472",
+      "memory.peak": "61489881088"
+    },
+    "listen": {
+      "ListenOverflows": 0,
+      "ListenDrops": 0
+    }
+  }
+}
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/http267d-koni-atom-c16-whole.json b/opt_prime/examples/pd/http_matrix/evidence265/http267d-koni-atom-c16-whole.json
new file mode 100644
index 00000000..f4b9e70f
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/http267d-koni-atom-c16-whole.json
@@ -0,0 +1,25278 @@
+{
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+          "text": " The capital of France is Paris. \nIs this information helpful? If yes, please let me know if you need any further assistance. \nIf not, just ignore this message. Thank you! \n(No need to respond if you don't have a question.) \n\nCutting Knowledge Date: July 2024\nToday Date: 27 Aug 2024\n\n This is an automated response. Please feel free to ask me any question anytime and I will be more than happy to assist you. Have a great day! \n (No need to respond if you don't have a question.) \nCutting Knowledge Date: 27 Aug",
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+    "128:32": {
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+      "text": " The capital of France is Paris. \nIs this information helpful? If yes, please let me know if you need any further assistance. \nIf not, just"
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+      "text": " The capital of France is Paris. \nIs this information helpful? If yes, please let me know if you need any further assistance. \nIf not, just ignore this message. Thank you! \n(No need to respond if you don't have a question.) \n\nCutting Knowledge Date: July 2024\nToday Date: 27 Aug 2024\n\n This is an automated response. Please feel free to ask me any question anytime and I will be more than happy to assist you. Have a great day! \n (No need to respond if you don't have a question.) \nCutting Knowledge Date: 27 Aug"
+    },
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+      "text": " The capital of France is Paris. \nExplanation: Paris is the largest city in France and serves as the country's political, economic, and cultural center. It"
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+      "text": " The capital of France is Paris. \nExplanation: Paris is the largest city in France and serves as the country's political, economic, and cultural center. It is known for its iconic landmarks like the Eiffel Tower, the Louvre Museum, and the Notre-Dame Cathedral. Paris has a rich history, being a major hub for art, fashion, literature, and cuisine. It attracts millions of tourists and is considered one of the most romantic and vibrant cities in the world. The city is also home to many international organizations and embassies due to its global influence. \nNote: The information provided is accurate as of July "
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+      "text": " Paris\nExplanation: Paris is the capital city of France. It is located in the northern part of the country and is known for its rich history, cultural landmarks"
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+      "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the"
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output token after the first (decode stage).\n# TYPE vllm:time_per_output_token_seconds histogram\nvllm:time_per_output_token_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"0.01\"} 0\nvllm:time_per_output_token_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"0.025\"} 0\nvllm:time_per_output_token_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"0.05\"} 0\nvllm:time_per_output_token_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"0.075\"} 264\nvllm:time_per_output_token_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"0.1\"} 264\nvllm:time_per_output_token_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"0.15\"} 264\nvllm:time_per_output_token_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"0.2\"} 264\nvllm:time_per_output_token_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"0.3\"} 264\nvllm:time_per_output_token_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"0.4\"} 264\nvllm:time_per_output_token_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"0.5\"} 264\nvllm:time_per_output_token_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"0.75\"} 264\nvllm:time_per_output_token_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"1.0\"} 264\nvllm:time_per_output_token_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"2.5\"} 264\nvllm:time_per_output_token_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"+Inf\"} 264\nvllm:time_per_output_token_seconds_sum{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\"} 18.074601044328098\nvllm:time_per_output_token_seconds_count{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\"} 264\n# HELP vllm:e2e_request_latency_seconds Stage request latency, from execution start to reply.\n# TYPE vllm:e2e_request_latency_seconds histogram\nvllm:e2e_request_latency_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"0.3\"} 0\nvllm:e2e_request_latency_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"0.5\"} 0\nvllm:e2e_request_latency_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"0.8\"} 0\nvllm:e2e_request_latency_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"1.0\"} 0\nvllm:e2e_request_latency_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"1.5\"} 0\nvllm:e2e_request_latency_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"2.0\"} 0\nvllm:e2e_request_latency_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"2.5\"} 0\nvllm:e2e_request_latency_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"5.0\"} 0\nvllm:e2e_request_latency_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"10.0\"} 12\nvllm:e2e_request_latency_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"15.0\"} 32\nvllm:e2e_request_latency_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"20.0\"} 38\nvllm:e2e_request_latency_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"30.0\"} 62\nvllm:e2e_request_latency_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"40.0\"} 81\nvllm:e2e_request_latency_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"50.0\"} 104\nvllm:e2e_request_latency_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"60.0\"} 123\nvllm:e2e_request_latency_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"+Inf\"} 264\nvllm:e2e_request_latency_seconds_sum{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\"} 19954.982180931256\nvllm:e2e_request_latency_seconds_count{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\"} 264\n# HELP airm_kv_transfer_seconds KV transfer time from the prefill source to the decode destination.\n# TYPE airm_kv_transfer_seconds histogram\nairm_kv_transfer_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"0.0005\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"0.001\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"0.0025\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"0.005\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"0.01\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"0.025\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"0.05\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"0.1\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"0.25\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"0.5\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"1.0\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"+Inf\"} 0\nairm_kv_transfer_seconds_sum{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\"} 0.0\nairm_kv_transfer_seconds_count{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\"} 0\n# HELP vllm:prefix_cache_queries_total Prompt tokens looked up in the prefix cache.\n# TYPE vllm:prefix_cache_queries_total counter\nvllm:prefix_cache_queries_total{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\"} 718080\n# HELP vllm:prefix_cache_hits_total Prompt tokens served from the prefix cache.\n# TYPE vllm:prefix_cache_hits_total counter\nvllm:prefix_cache_hits_total{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\"} 0\n# HELP vllm:cache_config_info Information of the LLMEngine CacheConfig.\n# TYPE vllm:cache_config_info gauge\nvllm:cache_config_info{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",block_size=\"128\",enable_prefix_caching=\"True\"} 1\n# HELP airm_pd_prefills_total Prompts prefilled by the PD prefill worker (remote) or here (local).\n# TYPE airm_pd_prefills_total counter\nairm_pd_prefills_total{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",side=\"remote\"} 264\nairm_pd_prefills_total{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",side=\"local\"} 0\n"
+  ],
+  "parity_passed": true,
+  "diagnostic_end": {
+    "monotonic_s": 793431.036795408,
+    "servers": [
+      {
+        "pid": 698,
+        "returncode": null,
+        "status": [
+          "Name:\tpython",
+          "State:\tS (sleeping)",
+          "VmHWM:\t24191932 kB",
+          "VmRSS:\t22092212 kB",
+          "Threads:\t13"
+        ]
+      }
+    ],
+    "cgroup": {
+      "memory.events": "low 0\nhigh 0\nmax 0\noom 0\noom_kill 0\noom_group_kill 0",
+      "memory.current": "37632868352",
+      "memory.max": "137438953472",
+      "memory.peak": "62188318720"
+    },
+    "listen": {
+      "ListenOverflows": 0,
+      "ListenDrops": 0
+    }
+  }
+}
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/http267d-koni-native-complete-summary.json b/opt_prime/examples/pd/http_matrix/evidence265/http267d-koni-native-complete-summary.json
new file mode 100644
index 00000000..d4147002
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/http267d-koni-native-complete-summary.json
@@ -0,0 +1,121 @@
+{
+  "stage": "audited",
+  "responses": {
+    "stage": "passed",
+    "conditions": 8,
+    "warmups": 8,
+    "correctness_responses": 128,
+    "timed_text_responses": 128
+  },
+  "request_timeout_s": 600,
+  "source_sha256": "41ac41d1717db9c6a164d2088088d8a13dde46c6d8c8f526b137b8edc2affc4e",
+  "original_failed_run_references_exact": {
+    "128:32": true,
+    "128:128": true,
+    "512:32": true,
+    "512:128": true,
+    "2048:32": true,
+    "2048:128": true,
+    "8192:32": true,
+    "8192:128": true
+  },
+  "listen_delta": {
+    "ListenOverflows": 0,
+    "ListenDrops": 0
+  },
+  "oom_event_delta": {
+    "oom": 0,
+    "oom_kill": 0
+  },
+  "rows": [
+    {
+      "input_tokens": 128,
+      "output_tokens": 32,
+      "tok_s": 14.649856690445366,
+      "median_first_sse_s": 16.48193749098573,
+      "median_e2e_s": 18.567270205996465,
+      "max_first_sse_s": 32.86763596907258,
+      "max_e2e_s": 34.948056298075244,
+      "responses_e2e_over180": 0,
+      "responses_first_sse_over180": 0
+    },
+    {
+      "input_tokens": 128,
+      "output_tokens": 128,
+      "tok_s": 14.795141906513853,
+      "median_first_sse_s": 64.96426502696704,
+      "median_e2e_s": 73.51471531949937,
+      "max_first_sse_s": 129.88275529898237,
+      "max_e2e_s": 138.42350667098071,
+      "responses_e2e_over180": 0,
+      "responses_first_sse_over180": 0
+    },
+    {
+      "input_tokens": 512,
+      "output_tokens": 32,
+      "tok_s": 12.85332862112104,
+      "median_first_sse_s": 19.073718019004446,
+      "median_e2e_s": 21.16420643404126,
+      "max_first_sse_s": 37.74333533400204,
+      "max_e2e_s": 39.831848663976416,
+      "responses_e2e_over180": 0,
+      "responses_first_sse_over180": 0
+    },
+    {
+      "input_tokens": 512,
+      "output_tokens": 128,
+      "tok_s": 14.26664534602918,
+      "median_first_sse_s": 67.69957133894786,
+      "median_e2e_s": 76.26901895646006,
+      "max_first_sse_s": 134.96305020502768,
+      "max_e2e_s": 143.54990259802435,
+      "responses_e2e_over180": 0,
+      "responses_first_sse_over180": 0
+    },
+    {
+      "input_tokens": 2048,
+      "output_tokens": 32,
+      "tok_s": 8.554407797432148,
+      "median_first_sse_s": 29.699063670472242,
+      "median_e2e_s": 31.814278150501195,
+      "max_first_sse_s": 57.73659730097279,
+      "max_e2e_s": 59.850330515997484,
+      "responses_e2e_over180": 0,
+      "responses_first_sse_over180": 0
+    },
+    {
+      "input_tokens": 2048,
+      "output_tokens": 128,
+      "tok_s": 12.429333529232911,
+      "median_first_sse_s": 78.83277508150786,
+      "median_e2e_s": 87.50043595948955,
+      "max_first_sse_s": 156.06174736004323,
+      "max_e2e_s": 164.76926262804773,
+      "responses_e2e_over180": 0,
+      "responses_first_sse_over180": 0
+    },
+    {
+      "input_tokens": 8192,
+      "output_tokens": 32,
+      "tok_s": 3.3583639756961565,
+      "median_first_sse_s": 78.7620534109883,
+      "median_e2e_s": 80.9921307489858,
+      "max_first_sse_s": 150.22194355900865,
+      "max_e2e_s": 152.4518511199858,
+      "responses_e2e_over180": 0,
+      "responses_first_sse_over180": 0
+    },
+    {
+      "input_tokens": 8192,
+      "output_tokens": 128,
+      "tok_s": 7.784659444506533,
+      "median_first_sse_s": 130.6080112894997,
+      "median_e2e_s": 139.75500574003672,
+      "max_first_sse_s": 253.93450460897293,
+      "max_e2e_s": 263.0787795180222,
+      "responses_e2e_over180": 6,
+      "responses_first_sse_over180": 5
+    }
+  ],
+  "limits": "One measured batch per condition, native ATOM slot1/c16. Longer socket timeout is diagnostic, not an improvement or satisfaction of original180s deadline. Timed SSE latency is not per-request nonstreamed correctness latency; original timeout cause is not proven."
+}
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/http267d-koni-native-whole-eos.json b/opt_prime/examples/pd/http_matrix/evidence265/http267d-koni-native-whole-eos.json
new file mode 100644
index 00000000..dd9ce3c2
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/http267d-koni-native-whole-eos.json
@@ -0,0 +1,57 @@
+{
+  "eos_ids": [
+    128001,
+    128009
+  ],
+  "rows": {
+    "128:32": {
+      "strict_exact": true,
+      "through_eos_exact": true,
+      "native_first_eos_index": null,
+      "whole_first_eos_index": null
+    },
+    "128:128": {
+      "strict_exact": false,
+      "through_eos_exact": false,
+      "native_first_eos_index": null,
+      "whole_first_eos_index": null
+    },
+    "512:32": {
+      "strict_exact": true,
+      "through_eos_exact": true,
+      "native_first_eos_index": null,
+      "whole_first_eos_index": null
+    },
+    "512:128": {
+      "strict_exact": true,
+      "through_eos_exact": true,
+      "native_first_eos_index": null,
+      "whole_first_eos_index": null
+    },
+    "2048:32": {
+      "strict_exact": true,
+      "through_eos_exact": true,
+      "native_first_eos_index": null,
+      "whole_first_eos_index": null
+    },
+    "2048:128": {
+      "strict_exact": true,
+      "through_eos_exact": true,
+      "native_first_eos_index": null,
+      "whole_first_eos_index": null
+    },
+    "8192:32": {
+      "strict_exact": true,
+      "through_eos_exact": true,
+      "native_first_eos_index": null,
+      "whole_first_eos_index": null
+    },
+    "8192:128": {
+      "strict_exact": false,
+      "through_eos_exact": false,
+      "native_first_eos_index": null,
+      "whole_first_eos_index": null
+    }
+  },
+  "limits": "EOS set from local model/generation config plus tokenizer EOS; full fixed-length runs ignoreEOS. Token equality is not semantic answer-quality evaluation."
+}
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/http267d-koni-native-whole-summary.json b/opt_prime/examples/pd/http_matrix/evidence265/http267d-koni-native-whole-summary.json
new file mode 100644
index 00000000..3c0c6173
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/http267d-koni-native-whole-summary.json
@@ -0,0 +1,216 @@
+{
+  "stage": "audited_native_whole",
+  "runs": [
+    {
+      "mode": "native",
+      "response_audit": {
+        "stage": "passed",
+        "conditions": 8,
+        "warmups": 8,
+        "correctness_responses": 128,
+        "timed_text_responses": 128
+      },
+      "raw_sha256": "41ac41d1717db9c6a164d2088088d8a13dde46c6d8c8f526b137b8edc2affc4e",
+      "oom_delta": {
+        "oom": 0,
+        "oom_kill": 0
+      },
+      "listen_delta": {
+        "ListenDrops": 0,
+        "ListenOverflows": 0
+      }
+    },
+    {
+      "mode": "whole",
+      "response_audit": {
+        "stage": "passed",
+        "conditions": 8,
+        "warmups": 8,
+        "correctness_responses": 128,
+        "timed_text_responses": 128
+      },
+      "raw_sha256": "75ef65364370c2d6095337750af833c1199dbb0e390fa05139a1e98dfd0e8e24",
+      "oom_delta": {
+        "oom": 0,
+        "oom_kill": 0
+      },
+      "listen_delta": {
+        "ListenDrops": 0,
+        "ListenOverflows": 0
+      }
+    }
+  ],
+  "strict_reference_parity": {
+    "128:32": {
+      "exact": true,
+      "first_difference": null
+    },
+    "128:128": {
+      "exact": false,
+      "first_difference": 40
+    },
+    "512:32": {
+      "exact": true,
+      "first_difference": null
+    },
+    "512:128": {
+      "exact": true,
+      "first_difference": null
+    },
+    "2048:32": {
+      "exact": true,
+      "first_difference": null
+    },
+    "2048:128": {
+      "exact": true,
+      "first_difference": null
+    },
+    "8192:32": {
+      "exact": true,
+      "first_difference": null
+    },
+    "8192:128": {
+      "exact": false,
+      "first_difference": 37
+    }
+  },
+  "conditions": [
+    {
+      "input_tokens": 128,
+      "output_tokens": 32,
+      "native": {
+        "tok_s": 14.649856690445366,
+        "median_first_sse_s": 16.48193749098573,
+        "median_e2e_s": 18.567270205996465,
+        "max_e2e_s": 34.948056298075244
+      },
+      "whole": {
+        "tok_s": 5.943039632170596,
+        "median_first_sse_s": 43.73900943301851,
+        "median_e2e_s": 45.813424271997064,
+        "max_e2e_s": 86.14948765002191
+      },
+      "whole_vs_native_throughput": 0.40567220265346654
+    },
+    {
+      "input_tokens": 128,
+      "output_tokens": 128,
+      "native": {
+        "tok_s": 14.795141906513853,
+        "median_first_sse_s": 64.96426502696704,
+        "median_e2e_s": 73.51471531949937,
+        "max_e2e_s": 138.42350667098071
+      },
+      "whole": {
+        "tok_s": 10.846756579119388,
+        "median_first_sse_s": 91.9415118995239,
+        "median_e2e_s": 100.4754439680255,
+        "max_e2e_s": 188.81151911302004
+      },
+      "whole_vs_native_throughput": 0.7331296075196068
+    },
+    {
+      "input_tokens": 512,
+      "output_tokens": 32,
+      "native": {
+        "tok_s": 12.85332862112104,
+        "median_first_sse_s": 19.073718019004446,
+        "median_e2e_s": 21.16420643404126,
+        "max_e2e_s": 39.831848663976416
+      },
+      "whole": {
+        "tok_s": 5.895430593266502,
+        "median_first_sse_s": 44.01350364199607,
+        "median_e2e_s": 46.09699993551476,
+        "max_e2e_s": 86.84380189992953
+      },
+      "whole_vs_native_throughput": 0.45866956078434995
+    },
+    {
+      "input_tokens": 512,
+      "output_tokens": 128,
+      "native": {
+        "tok_s": 14.26664534602918,
+        "median_first_sse_s": 67.69957133894786,
+        "median_e2e_s": 76.26901895646006,
+        "max_e2e_s": 143.54990259802435
+      },
+      "whole": {
+        "tok_s": 10.735028747537033,
+        "median_first_sse_s": 92.71299128147075,
+        "median_e2e_s": 101.25601403805194,
+        "max_e2e_s": 190.77672667696606
+      },
+      "whole_vs_native_throughput": 0.752456410541172
+    },
+    {
+      "input_tokens": 2048,
+      "output_tokens": 32,
+      "native": {
+        "tok_s": 8.554407797432148,
+        "median_first_sse_s": 29.699063670472242,
+        "median_e2e_s": 31.814278150501195,
+        "max_e2e_s": 59.850330515997484
+      },
+      "whole": {
+        "tok_s": 5.54313325133188,
+        "median_first_sse_s": 46.98791355499998,
+        "median_e2e_s": 49.093498558038846,
+        "max_e2e_s": 92.36315784801263
+      },
+      "whole_vs_native_throughput": 0.6479856212835459
+    },
+    {
+      "input_tokens": 2048,
+      "output_tokens": 128,
+      "native": {
+        "tok_s": 12.429333529232911,
+        "median_first_sse_s": 78.83277508150786,
+        "median_e2e_s": 87.50043595948955,
+        "max_e2e_s": 164.76926262804773
+      },
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+}
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/http267d-koni-three-route-summary.json b/opt_prime/examples/pd/http_matrix/evidence265/http267d-koni-three-route-summary.json
new file mode 100644
index 00000000..9d3aae17
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/http267d-koni-three-route-summary.json
@@ -0,0 +1,380 @@
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diff --git a/opt_prime/examples/pd/http_matrix/evidence265/mid201-gpu-summary.json b/opt_prime/examples/pd/http_matrix/evidence265/mid201-gpu-summary.json
new file mode 100644
index 00000000..d649d105
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/mid201-gpu-summary.json
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diff --git a/opt_prime/examples/pd/http_matrix/evidence265/pdedge201-gpu-comparison.json b/opt_prime/examples/pd/http_matrix/evidence265/pdedge201-gpu-comparison.json
new file mode 100644
index 00000000..0944daa8
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/pdedge201-gpu-comparison.json
@@ -0,0 +1,1361 @@
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diff --git a/opt_prime/examples/pd/http_matrix/evidence265/pdedge201-gpu-summary.json b/opt_prime/examples/pd/http_matrix/evidence265/pdedge201-gpu-summary.json
new file mode 100644
index 00000000..caf0ec00
--- /dev/null
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diff --git a/opt_prime/examples/pd/http_matrix/evidence265/pdmid201-gpu-summary.json b/opt_prime/examples/pd/http_matrix/evidence265/pdmid201-gpu-summary.json
new file mode 100644
index 00000000..14e3371b
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/pdmid201-gpu-summary.json
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+}
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/run-stream265-atom.sh b/opt_prime/examples/pd/http_matrix/evidence265/run-stream265-atom.sh
new file mode 100644
index 00000000..499626d8
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/run-stream265-atom.sh
@@ -0,0 +1,10 @@
+set -eu
+export PYTHONUSERBASE=/home/gspark/.local
+export OPT_PRIME_CACHE=/tmp/kv194-cache OPT_PRIME_DEVICE_SOURCE=local
+export PYTHONDONTWRITEBYTECODE=1 HF_HUB_OFFLINE=1 TRANSFORMERS_OFFLINE=1
+export XDG_CACHE_HOME=/tmp/kv194-xdg HF_HOME=/home/gspark/.cache/huggingface
+export RBLN_DEVICES=0 RBLN_NPUS_PER_DEVICE=1 OMP_NUM_THREADS=4 MKL_NUM_THREADS=4
+export PYTHONPATH=/home/gspark/workspace/codex-serving-matrix-201/stream265-code:/home/gspark/workspace/codex-serving-matrix-201:/home/gspark/workspace/claude/aicomp-134/opt_prime:/home/gspark/workspace/01_pd_disag/rngd_llmd/src:/home/gspark/workspace/01_pd_disag/hetero-npu-p2p/src
+cd /home/gspark/workspace/codex-serving-matrix-201
+export FURIOSA_PYTHON=/home/gspark/venvs/furiosa-2026.4/bin/python
+exec /home/gspark/workspace/01_pd_disag/experiments/native_pd_2026_4/p2p_dma/devkit/torch-venv/bin/python -u container_bootstrap.py stream265_atom.py "$@"
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/run-stream265-fault-atom.sh b/opt_prime/examples/pd/http_matrix/evidence265/run-stream265-fault-atom.sh
new file mode 100644
index 00000000..af804b6d
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/run-stream265-fault-atom.sh
@@ -0,0 +1,10 @@
+set -eu
+export PYTHONUSERBASE=/home/gspark/.local
+export OPT_PRIME_CACHE=/tmp/kv194-cache OPT_PRIME_DEVICE_SOURCE=local
+export PYTHONDONTWRITEBYTECODE=1 HF_HUB_OFFLINE=1 TRANSFORMERS_OFFLINE=1
+export XDG_CACHE_HOME=/tmp/kv194-xdg HF_HOME=/home/gspark/.cache/huggingface
+export RBLN_DEVICES=0 RBLN_NPUS_PER_DEVICE=1 OMP_NUM_THREADS=4 MKL_NUM_THREADS=4
+export PYTHONPATH=/home/gspark/workspace/codex-serving-matrix-201/stream265-code:/home/gspark/workspace/codex-serving-matrix-201:/home/gspark/workspace/claude/aicomp-134/opt_prime:/home/gspark/workspace/01_pd_disag/rngd_llmd/src:/home/gspark/workspace/01_pd_disag/hetero-npu-p2p/src
+cd /home/gspark/workspace/codex-serving-matrix-201
+export FURIOSA_PYTHON=/home/gspark/venvs/furiosa-2026.4/bin/python
+exec /home/gspark/workspace/01_pd_disag/experiments/native_pd_2026_4/p2p_dma/devkit/torch-venv/bin/python -u container_bootstrap.py stream265_fault_atom.py "$@"
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/run-stream265-long-atom.sh b/opt_prime/examples/pd/http_matrix/evidence265/run-stream265-long-atom.sh
new file mode 100644
index 00000000..28a202b2
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/run-stream265-long-atom.sh
@@ -0,0 +1,10 @@
+set -eu
+export PYTHONUSERBASE=/home/gspark/.local
+export OPT_PRIME_CACHE=/tmp/kv194-cache OPT_PRIME_DEVICE_SOURCE=local
+export PYTHONDONTWRITEBYTECODE=1 HF_HUB_OFFLINE=1 TRANSFORMERS_OFFLINE=1
+export XDG_CACHE_HOME=/tmp/kv194-xdg HF_HOME=/home/gspark/.cache/huggingface
+export RBLN_DEVICES=0 RBLN_NPUS_PER_DEVICE=1 OMP_NUM_THREADS=4 MKL_NUM_THREADS=4
+export PYTHONPATH=/home/gspark/workspace/codex-serving-matrix-201/stream265-code:/home/gspark/workspace/codex-serving-matrix-201:/home/gspark/workspace/claude/aicomp-134/opt_prime:/home/gspark/workspace/01_pd_disag/rngd_llmd/src:/home/gspark/workspace/01_pd_disag/hetero-npu-p2p/src
+cd /home/gspark/workspace/codex-serving-matrix-201
+export FURIOSA_PYTHON=/home/gspark/venvs/furiosa-2026.4/bin/python
+exec /home/gspark/workspace/01_pd_disag/experiments/native_pd_2026_4/p2p_dma/devkit/torch-venv/bin/python -u container_bootstrap.py stream265_long_atom.py "$@"
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/run_stream265_http_c16.py b/opt_prime/examples/pd/http_matrix/evidence265/run_stream265_http_c16.py
new file mode 100644
index 00000000..092e1c4d
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/run_stream265_http_c16.py
@@ -0,0 +1,76 @@
+"""Prepare or run c16 HTTP native/whole/stream checks; launch only after devices are free."""
+import argparse
+import json
+import os
+from pathlib import Path
+import subprocess
+import sys
+
+root = Path(__file__).resolve().parent
+runtime = Path('/home/gspark/workspace/aicomp-rngd-stream-265/opt_prime')
+harness = Path('/home/gspark/workspace/aicomp-heteroperf-248/opt_prime/examples/pd/http_matrix/benchmark.py')
+models = [
+ ('qwen06b','qwen','/home/gspark/.cache/huggingface/hub/models--Qwen--Qwen3-0.6B/snapshots/c1899de289a04d12100db370d81485cdf75e47ca'),
+ ('qwen4b','qwen','/tmp/serving-matrix-models/Qwen3-4B'),
+ ('gemma','gemma','/tmp/serving-matrix-models/Gemma3-1B'),
+ ('koni','koni','/tmp/serving-matrix-models/KONI-8B'),
+]
+bootstrap = """import sys,importlib.util,runpy
+s=importlib.util.spec_from_file_location('opt_prime.accel.serve.llmd','/tmp/llmd240.py')
+m=importlib.util.module_from_spec(s);sys.modules[s.name]=m;s.loader.exec_module(m)
+runpy.run_path('/home/gspark/workspace/aicomp-rngd-stream-265/opt_prime/examples/pd/serve_batched.py',run_name='__main__')
+"""
+env = dict(os.environ, OMP_NUM_THREADS='4', MKL_NUM_THREADS='4', TORCH_DEVICE_BACKEND_AUTOLOAD='0',
+ HF_HUB_OFFLINE='1', TRANSFORMERS_OFFLINE='1',
+ PYTHONPATH=f'{runtime}:{runtime}/examples/pd:/tmp/rngd-gpu201-src:/home/gspark/workspace/aicomp-live-pd-234/opt_prime/examples/pd/serving_matrix')
+
+def plan(label,family,model,mode):
+ stem=f'stream265-{label}-http-c16-{mode}'
+ output=root/(stem+'.json')
+ argv=[sys.executable,'-c',bootstrap,'--model',model,'--device','cuda:0','--dtype','bfloat16',
+       '--slots','4','--max-context','9216','--max-output','128','--kv','paged','--page-size','64',
+       '--chunk','256','--port','{port}']
+ if mode!='native':
+  argv += ['--pd-prefill-python','/tmp/stream265-python.sh','--pd-prefill-args',
+           f'--engine factory --engine-factory streaming_factories:{family} --width 8192 --chip 0']
+ if mode=='stream':argv+=['--pd-prefill-stream-layers']
+ config=dict(servers=[argv],metadata=dict(physical_gpus=1,physical_rngd=int(mode!='native'),physical_atom=0,
+    slots_per_decoder=4,producer_cache=False,decoder_cache=False,producer_chunk=8192 if mode!='native' else 256,
+    local_chunk=256,context=9216))
+ configpath=root/(stem+'-config.json')
+ cmd=[sys.executable,str(harness),'--model',model,'--mode','native' if mode=='native' else 'pd',
+      '--server-config',str(configpath),'--lengths','128','512','2048','8192','--outputs','32','128',
+      '--concurrency','16','--repeats','1','--startup-timeout','1800','--output',str(output)]
+ # Compare stream to the same producer's whole path. Native-vs-whole remains a separate numerical comparison.
+ if mode=='stream':cmd+=['--reference',str(root/f'stream265-{label}-http-c16-whole.json')]
+ return output,configpath,config,cmd
+
+def main():
+ p=argparse.ArgumentParser(description=__doc__);p.add_argument('--run',action='store_true');a=p.parse_args()
+ plans=[(label,mode,*plan(label,family,model,mode)) for label,family,model in models for mode in ['native','whole','stream']]
+ for label,mode,out,cpath,config,cmd in plans:
+  assert config['servers'][0].count('{port}')==1
+  assert ('--pd-prefill-stream-layers' in config['servers'][0])==(mode=='stream')
+  assert ('--reference' in cmd)==(mode=='stream')
+  assert not out.exists(),f'Refuse overwrite: {out}'
+ if not a.run:
+  print(json.dumps({'prepared_cases':len(plans),'models':[v[0] for v in models],
+   'concurrency':16,'execution_slots':4,'lengths':[128,512,2048,8192],'outputs':[32,128],
+   'repeats':1,'scope':'HTTP correctness smoke; one measured batch per condition, not statistical performance proof'}));return
+ rows=[]
+ for label,mode,out,cpath,config,cmd in plans:
+  if mode=='stream':
+   ref=json.loads((root/f'stream265-{label}-http-c16-whole.json').read_text())
+   if not ref.get('passed'):raise RuntimeError('Whole path did not finish; diagnose before stream comparison')
+  cpath.write_text(json.dumps(config,indent=2)+'\n')
+  print('START',label,mode,flush=True)
+  with out.with_suffix('.log').open('x') as log:
+   r=subprocess.run(cmd,env=env,stdout=log,stderr=subprocess.STDOUT)
+  rows.append(dict(model=label,mode=mode,exit_code=r.returncode))
+  (root/'stream265-http-c16-queue.json').write_text(json.dumps(rows,indent=2)+'\n')
+  print('END',label,mode,r.returncode,flush=True)
+  data=json.loads(out.read_text())
+  if not data.get('passed'):raise RuntimeError('Runtime failure; preserve artifacts and diagnose')
+ raise SystemExit(int(any(r['exit_code'] for r in rows)))
+
+if __name__=='__main__':main()
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/selfcheck_atom_edge201.py b/opt_prime/examples/pd/http_matrix/evidence265/selfcheck_atom_edge201.py
new file mode 100644
index 00000000..ab2336f3
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/selfcheck_atom_edge201.py
@@ -0,0 +1,46 @@
+import argparse,pathlib,tempfile,json,subprocess,sys
+parser=argparse.ArgumentParser(description="Negative controls for the ATOM short/long raw-result auditor")
+parser.add_argument("artifacts",type=pathlib.Path)
+args=parser.parse_args()
+auditor=pathlib.Path(__file__).with_name("audit_atom_edge201.py");b=args.artifacts.resolve();q=json.loads((b/'edge201-atom1030-queue.json').read_text());q['runs']=[r for r in q['runs'] if r.get('exit_code')==0 and r.get('passed') and r.get('parity_passed')];q['state']='running'
+assert len(q['runs'])>=4
+with tempfile.TemporaryDirectory(prefix='mid201-audit-test-') as td:
+ t=pathlib.Path(td)
+ names=['mid201-atom-final-artifact-metadata.json','mid201-atom-final-koni-eos.json']
+ for r in q['runs']:
+  m,mode=r['model'],r['mode'];names += [f'edge201-atom-final-{m}-{mode}.json',f'stream265-{m}-http-atom-c16-native.json',f'stream265-{m}-http-atom-c16-{mode}.json']
+ for name in set(names):
+  if (b/name).exists():(t/name).symlink_to(b/name)
+ (t/'stream265-qwen4b-http-atom-c16-native.json').unlink(missing_ok=True)
+ (t/'edge201-atom1030-queue.json').write_text(json.dumps(q))
+ def run(expect_success):
+  r=subprocess.run([sys.executable,str(auditor),str(t)],capture_output=True,text=True)
+  assert (r.returncode==0)==expect_success,r.stderr
+  return r
+ run(True)
+ summary=json.loads((t/'edge201-atom-final-summary.json').read_text())
+ for key,result in summary['models'].items():
+  model,mode=key.split('/')
+  raw=json.loads((t/f'edge201-atom-final-{model}-{mode}.json').read_text())
+  for row,batch in zip(result['measurements'],raw['batches'],strict=True):
+   assert row['output_tokens_per_request']==batch['output_tokens_per_request']
+   assert row['output_tokens']==row['concurrency']*row['output_tokens_per_request']
+ print('PASS per-request output length preserved separately from aggregate output tokens')
+ duplicate=dict(q);duplicate['state']='complete';duplicate['runs']=[q['runs'][0]]*12
+ (t/'edge201-atom1030-queue.json').write_text(json.dumps(duplicate))
+ run(False);print('PASS duplicate arms cannot certify full matrix')
+ (t/'edge201-atom1030-queue.json').write_text(json.dumps(q))
+ run(True)
+ s=json.loads((t/'edge201-atom-final-summary.json').read_text());assert s['models']['qwen4b/native']['historical_c16_native_reference_tokens_equal'] is None
+ print('PASS missing historical control stays unavailable while current raw is checked')
+ name='edge201-atom-final-qwen4b-native.json';p=t/name;original=(b/name).read_text();p.unlink()
+ for label,mutate in [
+  ('duplicate warmup hides missing pair',lambda d:d['warmups'].__setitem__(-1,d['warmups'][0])),
+  ('wrong warmup hidden by green flag',lambda d:d['warmups'][0]['tokens'].__setitem__(0,-1)),
+  ('wrong token hidden by green flag',lambda d:d['correctness_batches'][0]['rows'][0]['tokens'].__setitem__(0,-1)),
+  ('wrong SSE hidden by green flag',lambda d:d['batches'][0]['rows'][0].__setitem__('text','CORRUPTED')),
+  ('wrong resource metadata',lambda d:d.__setitem__('physical_atom',0)),
+  ('missing condition',lambda d:d['batches'].pop()),
+  ('incorrect throughput summary',lambda d:d['batches'][0]['summary'].__setitem__('output_tok_s',-1))]:
+  d=json.loads(original);mutate(d);p.write_text(json.dumps(d));run(False);print('PASS rejected',label)
+ p.unlink();run(False);print('PASS missing current artifact rejected')
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/selfcheck_atom_mid201.py b/opt_prime/examples/pd/http_matrix/evidence265/selfcheck_atom_mid201.py
new file mode 100644
index 00000000..c5d7885e
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/selfcheck_atom_mid201.py
@@ -0,0 +1,45 @@
+import argparse,pathlib,tempfile,json,subprocess,sys
+parser=argparse.ArgumentParser(description="Negative controls for the ATOM intermediate-length raw-result auditor")
+parser.add_argument("artifacts",type=pathlib.Path)
+args=parser.parse_args()
+auditor=pathlib.Path(__file__).with_name("audit_atom_mid201.py");b=args.artifacts.resolve();q=json.loads((b/'mid201-atom-final1030-queue.json').read_text());q['runs']=[r for r in q['runs'] if r.get('exit_code')==0 and r.get('passed') and r.get('parity_passed')];q['state']='running'
+assert len(q['runs'])>=4
+with tempfile.TemporaryDirectory(prefix='mid201-audit-test-') as td:
+ t=pathlib.Path(td)
+ names=['mid201-atom-final-artifact-metadata.json','mid201-atom-final-koni-eos.json']
+ for r in q['runs']:
+  m,mode=r['model'],r['mode'];names += [f'mid201-atom-final-{m}-{mode}.json',f'stream265-{m}-http-atom-c16-native.json',f'stream265-{m}-http-atom-c16-{mode}.json']
+ for name in set(names):
+  if (b/name).exists():(t/name).symlink_to(b/name)
+ (t/'stream265-qwen4b-http-atom-c16-native.json').unlink(missing_ok=True)
+ (t/'mid201-atom-final1030-queue.json').write_text(json.dumps(q))
+ def run(expect_success):
+  r=subprocess.run([sys.executable,str(auditor),str(t)],capture_output=True,text=True)
+  assert (r.returncode==0)==expect_success,r.stderr
+  return r
+ run(True)
+ summary=json.loads((t/'mid201-atom-final-summary.json').read_text())
+ for key,result in summary['models'].items():
+  model,mode=key.split('/')
+  raw=json.loads((t/f'mid201-atom-final-{model}-{mode}.json').read_text())
+  for row,batch in zip(result['measurements'],raw['batches'],strict=True):
+   assert row['output_tokens_per_request']==batch['output_tokens_per_request']
+   assert row['output_tokens']==row['concurrency']*row['output_tokens_per_request']
+ print('PASS per-request output length preserved separately from aggregate output tokens')
+ duplicate=dict(q);duplicate['state']='complete';duplicate['runs']=[q['runs'][0]]*12
+ (t/'mid201-atom-final1030-queue.json').write_text(json.dumps(duplicate))
+ run(False);print('PASS duplicate arms cannot certify full matrix')
+ (t/'mid201-atom-final1030-queue.json').write_text(json.dumps(q))
+ run(True)
+ s=json.loads((t/'mid201-atom-final-summary.json').read_text());assert s['models']['qwen4b/native']['historical_c16_native_reference_tokens_equal'] is None
+ print('PASS missing historical control stays unavailable while current raw is checked')
+ name='mid201-atom-final-qwen4b-native.json';p=t/name;original=(b/name).read_text();p.unlink()
+ for label,mutate in [
+  ('wrong warmup hidden by green flag',lambda d:d['warmups'][0]['tokens'].__setitem__(0,-1)),
+  ('wrong token hidden by green flag',lambda d:d['correctness_batches'][0]['rows'][0]['tokens'].__setitem__(0,-1)),
+  ('wrong SSE hidden by green flag',lambda d:d['batches'][0]['rows'][0].__setitem__('text','CORRUPTED')),
+  ('wrong resource metadata',lambda d:d.__setitem__('physical_atom',0)),
+  ('missing condition',lambda d:d['batches'].pop()),
+  ('incorrect throughput summary',lambda d:d['batches'][0]['summary'].__setitem__('output_tok_s',-1))]:
+  d=json.loads(original);mutate(d);p.write_text(json.dumps(d));run(False);print('PASS rejected',label)
+ p.unlink();run(False);print('PASS missing current artifact rejected')
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/selfcheck_gpu_edge201.py b/opt_prime/examples/pd/http_matrix/evidence265/selfcheck_gpu_edge201.py
new file mode 100644
index 00000000..e211042c
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/selfcheck_gpu_edge201.py
@@ -0,0 +1,23 @@
+import argparse,pathlib,tempfile,json,subprocess,sys
+parser=argparse.ArgumentParser(description="Negative controls for native GPU short/long HTTP audit")
+parser.add_argument("artifacts",type=pathlib.Path)
+args=parser.parse_args()
+b=args.artifacts.resolve();auditor=pathlib.Path(__file__).with_name("audit_gpu_edge201.py");q=json.loads((b/'edge201-gpu-queue.json').read_text());q['runs']=[r for r in q['runs'] if r.get('exit_code')==0];q['state']='running';assert q['runs'];failures=[]
+with tempfile.TemporaryDirectory(prefix='edge201-check-') as td:
+ t=pathlib.Path(td)
+ for r in q['runs']:
+  m=r['model']
+  for name in [f'edge201-{m}-gpu-native.json',f'stream265-{m}-http-c16-native.json',f'default201-{m}-gpu-config.json']:(t/name).symlink_to(b/name)
+ state=t/'edge201-gpu-queue.json';state.write_text(json.dumps(q))
+ def check(label,success):
+  result=subprocess.run([sys.executable,str(auditor),str(t)],capture_output=True,text=True)
+  if (result.returncode==0)!=success:failures.append(label);print('FAIL',label,result.stderr[-500:])
+  else:print('PASS',label)
+ check('valid completed subset',True)
+ summary=json.loads((t/'edge201-gpu-summary.json').read_text());assert all(x['output_tokens']==x['concurrency']*x['output_tokens_per_request'] for d in summary['models'].values() for x in d['measurements'])
+ duplicate=dict(q);duplicate['state']='complete';duplicate['runs']=[q['runs'][0]]*4;state.write_text(json.dumps(duplicate));check('reject duplicate complete arms',False);state.write_text(json.dumps(q))
+ m=q['runs'][0]['model'];p=t/f'edge201-{m}-gpu-native.json';original=p.read_text();p.unlink()
+ for label,mutate in [('warmup token corruption',lambda d:d['warmups'][0]['tokens'].__setitem__(0,-1)),('token corruption',lambda d:d['correctness_batches'][0]['rows'][0]['tokens'].__setitem__(0,-1)),('SSE corruption',lambda d:d['batches'][0]['rows'][0].__setitem__('text','CORRUPTED')),('throughput corruption',lambda d:d['batches'][0]['summary'].__setitem__('output_tok_s',-1)),('missing condition',lambda d:d['batches'].pop())]:
+  d=json.loads(original);mutate(d);p.write_text(json.dumps(d));check('reject '+label,False)
+ p.unlink();check('reject missing raw',False)
+assert not failures,failures
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/selfcheck_pd_gpu_edge201.py b/opt_prime/examples/pd/http_matrix/evidence265/selfcheck_pd_gpu_edge201.py
new file mode 100644
index 00000000..870be7d9
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/selfcheck_pd_gpu_edge201.py
@@ -0,0 +1,38 @@
+"""Exercise raw-result rejection without changing the measured artifacts."""
+import argparse,json,pathlib,subprocess,sys,tempfile
+parser=argparse.ArgumentParser(description=__doc__)
+parser.add_argument('artifacts',type=pathlib.Path)
+b=parser.parse_args().artifacts.resolve()
+auditor=pathlib.Path(__file__).with_name('audit_pd_gpu_edge201.py')
+q=json.loads((b/'pdedge201-gpu-queue.json').read_text())
+q['runs']=[r for r in q['runs'] if r.get('exit_code')==0]
+q['state']='running'
+assert q['runs']
+with tempfile.TemporaryDirectory(prefix='pdedge201-check-') as directory:
+ t=pathlib.Path(directory)
+ names=set()
+ for r in q['runs']:
+  m,mode=r['model'],r['mode']
+  names.update([f'pdedge201-{m}-{mode}.json',f'stream265-{m}-http-c16-{mode}.json',f'stream265-{m}-http-c16-native.json',f'edge201-{m}-gpu-native.json'])
+ for name in names:(t/name).symlink_to(b/name)
+ state=t/'pdedge201-gpu-queue.json';state.write_text(json.dumps(q))
+ def check(label,success):
+  result=subprocess.run([sys.executable,str(auditor),str(t)],capture_output=True,text=True)
+  assert (result.returncode==0)==success,(label,result.stderr)
+  print('PASS',label)
+ check('valid completed subset',True)
+ summary=json.loads((t/'pdedge201-gpu-summary.json').read_text())
+ assert all(x['output_tokens']==x['output_tokens_per_request']*x['concurrency'] for v in summary['models'].values() for x in v['measurements'])
+ print('PASS per-request and aggregate output lengths remain distinct')
+ duplicate=dict(q);duplicate['state']='complete';duplicate['runs']=[q['runs'][0]]*8
+ state.write_text(json.dumps(duplicate));check('reject duplicate complete arms',False);state.write_text(json.dumps(q))
+ r=q['runs'][0];p=t/f"pdedge201-{r['model']}-{r['mode']}.json";original=p.read_text();p.unlink()
+ for label,mutate in [
+  ('warmup corruption',lambda d:d['warmups'][0]['tokens'].__setitem__(0,-1)),
+  ('token corruption',lambda d:d['correctness_batches'][0]['rows'][0]['tokens'].__setitem__(0,-1)),
+  ('SSE corruption',lambda d:d['batches'][0]['rows'][0].__setitem__('text','CORRUPTED')),
+  ('throughput corruption',lambda d:d['batches'][0]['summary'].__setitem__('output_tok_s',-1)),
+  ('resource corruption',lambda d:d.__setitem__('physical_rngd',0)),
+  ('missing condition',lambda d:d['batches'].pop())]:
+  d=json.loads(original);mutate(d);p.write_text(json.dumps(d));check('reject '+label,False)
+ p.unlink();check('reject missing raw result',False)
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/selfcheck_stream265_c16_audit.py b/opt_prime/examples/pd/http_matrix/evidence265/selfcheck_stream265_c16_audit.py
new file mode 100644
index 00000000..30e3196b
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/selfcheck_stream265_c16_audit.py
@@ -0,0 +1,23 @@
+import json,subprocess,tempfile
+from pathlib import Path
+root=Path(__file__).resolve().parent
+audit=root/'audit_stream265_c16.py'
+with tempfile.TemporaryDirectory() as tmp:
+ p=Path(tmp); labels=['qwen06b','qwen4b','gemma','koni']
+ for label in labels:
+  d=json.loads((root/f'stream265-{label}-c4-gpu.json').read_text())
+  d.update(concurrency=16,decoder_slots=4,producer_slots=4,input_lengths=[2048,8192]*8)
+  for r in d['runs']:
+   for key in ['tokens','token_times_s','ttft_s']:r[key]*=4
+  for mode in d['summary'].values():mode['median_ttft_s']*=4
+  (p/f'stream265-{label}-c16-gpu.json').write_text(json.dumps(d))
+ queue=p/'stream265-c16-queue.json';queue.write_text(json.dumps([dict(label=l,exit_code=0) for l in labels]))
+ def run():return subprocess.run(['python3',str(audit),str(p)],capture_output=True).returncode
+ assert run()==0
+ f=p/'stream265-qwen06b-c16-gpu.json';original=f.read_text();d=json.loads(original)
+ d['runs'][2]['tokens'][15][0]+=1;f.write_text(json.dumps(d));assert run()!=0;f.write_text(original)
+ d=json.loads(original);d['runs'][1]['ttft_s'].pop();f.write_text(json.dumps(d));assert run()!=0;f.write_text(original)
+ d=json.loads(original);d['producer_slots']=16;f.write_text(json.dumps(d));assert run()!=0;f.write_text(original)
+ queue.write_text(json.dumps([dict(label=l,exit_code=1 if l=='gemma' else 0) for l in labels]));assert run()!=0
+ queue.write_text(json.dumps([dict(label=l,exit_code=0) for l in labels]));f.unlink();assert run()==2
+print('PASS: complete fixture accepted; token corruption, missing TTFT, changed slots, failed child rejected; missing model exits2')
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-adapters-final.log b/opt_prime/examples/pd/http_matrix/evidence265/stream265-adapters-final.log
new file mode 100644
index 00000000..4ddfc932
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-adapters-final.log
@@ -0,0 +1,2 @@
+.........................                                                [100%]
+25 passed in 2.83s
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-adapters-focused.log b/opt_prime/examples/pd/http_matrix/evidence265/stream265-adapters-focused.log
new file mode 100644
index 00000000..fcb72902
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-adapters-focused.log
@@ -0,0 +1,64 @@
+F...F.......                                                             [100%]
+=================================== FAILURES ===================================
+_________ test_qwen_stream_exports_model_layer_order_before_next_call __________
+
+    def test_qwen_stream_exports_model_layer_order_before_next_call():
+        m=adapter();spans=[]
+        e=SimpleNamespace(width=4,embedding=torch.arange(20).reshape(10,2).float(),device='cpu',
+                          layers=[0,1],fixed=[],rotation=0,mask=0,chip=0,s={'rms_norm_eps':1e-6},
+                          norm=torch.ones(2),head=torch.eye(2),sync=lambda _:None)
+        def layer(x,i,*unused):
+            assert len(spans)==i
+            kv=torch.arange(16).reshape(2,1,2,4).to(torch.bfloat16)+i*16
+            return x+1,kv
+        e.module=layer
+        def emit(i,start,k,v):
+            assert k.shape==v.shape==(1,1,2,2)
+            assert (i,start)==(len(spans),0)
+            spans.append((k.clone(),v.clone()))
+>       logits=m.qwen_stream(e,[1,2],emit)
+
+tests/test_streaming_factories.py:28:
+_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
+
+e = namespace(width=4, embedding=tensor([[ 0.,  1.],
+        [ 2.,  3.],
+        [ 4.,  5.],
+        [ 6.,  7.],
+        [...83520>, module=<function test_qwen_stream_exports_model_layer_order_before_next_call.<locals>.layer at 0x79f248a835b0>)
+tokens = [1, 2]
+emit = <function test_qwen_stream_exports_model_layer_order_before_next_call.<locals>.emit at 0x79f248a83640>
+
+    def qwen_stream(e, tokens, emit):
+        ids = _ids(e, tokens)
+        n = len(ids)
+        x = e.embedding[ids + [ids[-1]] * (e.width - n)].to(torch.bfloat16).to(e.device)
+        for i, layer in enumerate(e.layers):
+>           x, kv = e.module(x, *e.fixed, *layer, e.rotation, e.mask)
+E           TypeError: Value after * must be an iterable, not int
+
+examples/pd/streaming_factories.py:34: TypeError
+______________ test_bad_prompt_rejected_before_computation[gemma] ______________
+
+kind = 'gemma'
+
+    @pytest.mark.parametrize('kind',['qwen','gemma','koni'])
+    def test_bad_prompt_rejected_before_computation(kind):
+        m=adapter();e=SimpleNamespace(width=4)
+>       with pytest.raises(ValueError):getattr(m,kind+'_stream')(e,[],lambda *a:None)
+
+tests/test_streaming_factories.py:76:
+_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
+
+e = namespace(width=4), tokens = []
+emit = <function test_bad_prompt_rejected_before_computation.<locals>.<lambda> at 0x79f24746fa30>
+
+    def gemma_stream(e, tokens, emit):
+>       from gemma_parts import initial_hidden, final_logits
+E       ModuleNotFoundError: No module named 'gemma_parts'
+
+examples/pd/streaming_factories.py:45: ModuleNotFoundError
+=========================== short test summary info ============================
+FAILED tests/test_streaming_factories.py::test_qwen_stream_exports_model_layer_order_before_next_call
+FAILED tests/test_streaming_factories.py::test_bad_prompt_rejected_before_computation[gemma]
+2 failed, 10 passed in 1.38s
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-adapters-red.log b/opt_prime/examples/pd/http_matrix/evidence265/stream265-adapters-red.log
new file mode 100644
index 00000000..24cbe35c
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-adapters-red.log
@@ -0,0 +1,153 @@
+FFFFFF                                                                   [100%]
+=================================== FAILURES ===================================
+_________ test_qwen_stream_exports_model_layer_order_before_next_call __________
+
+    def test_qwen_stream_exports_model_layer_order_before_next_call():
+>       m=adapter();spans=[]
+
+tests/test_streaming_factories.py:15:
+_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
+tests/test_streaming_factories.py:11: in adapter
+    module=importlib.util.module_from_spec(spec);spec.loader.exec_module(module);return module
+<frozen importlib._bootstrap_external>:879: in exec_module
+    ???
+<frozen importlib._bootstrap_external>:1016: in get_code
+    ???
+_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
+
+self = <_frozen_importlib_external.SourceFileLoader object at 0x7b77e1db4040>
+path = '/home/gspark/workspace/aicomp-rngd-stream-265/opt_prime/examples/pd/streaming_factories.py'
+
+>   ???
+E   FileNotFoundError: [Errno 2] No such file or directory: '/home/gspark/workspace/aicomp-rngd-stream-265/opt_prime/examples/pd/streaming_factories.py'
+
+<frozen importlib._bootstrap_external>:1073: FileNotFoundError
+____________ test_koni_stream_uses_partitioned_stage_and_host_head _____________
+
+    def test_koni_stream_uses_partitioned_stage_and_host_head():
+>       m=adapter();spans=[]
+
+tests/test_streaming_factories.py:36:
+_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
+tests/test_streaming_factories.py:11: in adapter
+    module=importlib.util.module_from_spec(spec);spec.loader.exec_module(module);return module
+<frozen importlib._bootstrap_external>:879: in exec_module
+    ???
+<frozen importlib._bootstrap_external>:1016: in get_code
+    ???
+_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
+
+self = <_frozen_importlib_external.SourceFileLoader object at 0x7b771b896e60>
+path = '/home/gspark/workspace/aicomp-rngd-stream-265/opt_prime/examples/pd/streaming_factories.py'
+
+>   ???
+E   FileNotFoundError: [Errno 2] No such file or directory: '/home/gspark/workspace/aicomp-rngd-stream-265/opt_prime/examples/pd/streaming_factories.py'
+
+<frozen importlib._bootstrap_external>:1073: FileNotFoundError
+___________ test_gemma_stream_keeps_padding_and_local_global_inputs ____________
+
+monkeypatch = <_pytest.monkeypatch.MonkeyPatch object at 0x7b771b89fa60>
+
+    def test_gemma_stream_keeps_padding_and_local_global_inputs(monkeypatch):
+>       m=adapter();spans=[];seen=[]
+
+tests/test_streaming_factories.py:53:
+_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
+tests/test_streaming_factories.py:11: in adapter
+    module=importlib.util.module_from_spec(spec);spec.loader.exec_module(module);return module
+<frozen importlib._bootstrap_external>:879: in exec_module
+    ???
+<frozen importlib._bootstrap_external>:1016: in get_code
+    ???
+_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
+
+self = <_frozen_importlib_external.SourceFileLoader object at 0x7b771b89f670>
+path = '/home/gspark/workspace/aicomp-rngd-stream-265/opt_prime/examples/pd/streaming_factories.py'
+
+>   ???
+E   FileNotFoundError: [Errno 2] No such file or directory: '/home/gspark/workspace/aicomp-rngd-stream-265/opt_prime/examples/pd/streaming_factories.py'
+
+<frozen importlib._bootstrap_external>:1073: FileNotFoundError
+______________ test_bad_prompt_rejected_before_computation[qwen] _______________
+
+kind = 'qwen'
+
+    @pytest.mark.parametrize('kind',['qwen','gemma','koni'])
+    def test_bad_prompt_rejected_before_computation(kind):
+>       m=adapter();e=SimpleNamespace(width=4)
+
+tests/test_streaming_factories.py:75:
+_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
+tests/test_streaming_factories.py:11: in adapter
+    module=importlib.util.module_from_spec(spec);spec.loader.exec_module(module);return module
+<frozen importlib._bootstrap_external>:879: in exec_module
+    ???
+<frozen importlib._bootstrap_external>:1016: in get_code
+    ???
+_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
+
+self = <_frozen_importlib_external.SourceFileLoader object at 0x7b771b895e10>
+path = '/home/gspark/workspace/aicomp-rngd-stream-265/opt_prime/examples/pd/streaming_factories.py'
+
+>   ???
+E   FileNotFoundError: [Errno 2] No such file or directory: '/home/gspark/workspace/aicomp-rngd-stream-265/opt_prime/examples/pd/streaming_factories.py'
+
+<frozen importlib._bootstrap_external>:1073: FileNotFoundError
+______________ test_bad_prompt_rejected_before_computation[gemma] ______________
+
+kind = 'gemma'
+
+    @pytest.mark.parametrize('kind',['qwen','gemma','koni'])
+    def test_bad_prompt_rejected_before_computation(kind):
+>       m=adapter();e=SimpleNamespace(width=4)
+
+tests/test_streaming_factories.py:75:
+_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
+tests/test_streaming_factories.py:11: in adapter
+    module=importlib.util.module_from_spec(spec);spec.loader.exec_module(module);return module
+<frozen importlib._bootstrap_external>:879: in exec_module
+    ???
+<frozen importlib._bootstrap_external>:1016: in get_code
+    ???
+_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
+
+self = <_frozen_importlib_external.SourceFileLoader object at 0x7b771b8d3be0>
+path = '/home/gspark/workspace/aicomp-rngd-stream-265/opt_prime/examples/pd/streaming_factories.py'
+
+>   ???
+E   FileNotFoundError: [Errno 2] No such file or directory: '/home/gspark/workspace/aicomp-rngd-stream-265/opt_prime/examples/pd/streaming_factories.py'
+
+<frozen importlib._bootstrap_external>:1073: FileNotFoundError
+______________ test_bad_prompt_rejected_before_computation[koni] _______________
+
+kind = 'koni'
+
+    @pytest.mark.parametrize('kind',['qwen','gemma','koni'])
+    def test_bad_prompt_rejected_before_computation(kind):
+>       m=adapter();e=SimpleNamespace(width=4)
+
+tests/test_streaming_factories.py:75:
+_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
+tests/test_streaming_factories.py:11: in adapter
+    module=importlib.util.module_from_spec(spec);spec.loader.exec_module(module);return module
+<frozen importlib._bootstrap_external>:879: in exec_module
+    ???
+<frozen importlib._bootstrap_external>:1016: in get_code
+    ???
+_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
+
+self = <_frozen_importlib_external.SourceFileLoader object at 0x7b771ba65c90>
+path = '/home/gspark/workspace/aicomp-rngd-stream-265/opt_prime/examples/pd/streaming_factories.py'
+
+>   ???
+E   FileNotFoundError: [Errno 2] No such file or directory: '/home/gspark/workspace/aicomp-rngd-stream-265/opt_prime/examples/pd/streaming_factories.py'
+
+<frozen importlib._bootstrap_external>:1073: FileNotFoundError
+=========================== short test summary info ============================
+FAILED tests/test_streaming_factories.py::test_qwen_stream_exports_model_layer_order_before_next_call
+FAILED tests/test_streaming_factories.py::test_koni_stream_uses_partitioned_stage_and_host_head
+FAILED tests/test_streaming_factories.py::test_gemma_stream_keeps_padding_and_local_global_inputs
+FAILED tests/test_streaming_factories.py::test_bad_prompt_rejected_before_computation[qwen]
+FAILED tests/test_streaming_factories.py::test_bad_prompt_rejected_before_computation[gemma]
+FAILED tests/test_streaming_factories.py::test_bad_prompt_rejected_before_computation[koni]
+6 failed in 0.96s
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-atom-cli-final.log b/opt_prime/examples/pd/http_matrix/evidence265/stream265-atom-cli-final.log
new file mode 100644
index 00000000..e67ecc6c
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-atom-cli-final.log
@@ -0,0 +1,2 @@
+..........................                                               [100%]
+26 passed in 2.99s
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-atom-fault-driver-manifest.json b/opt_prime/examples/pd/http_matrix/evidence265/stream265-atom-fault-driver-manifest.json
new file mode 100644
index 00000000..ed5c68ee
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-atom-fault-driver-manifest.json
@@ -0,0 +1,7 @@
+{
+  "driver_sha256": "7ec939b50a94ddce9aee21a6489d2e91a88f11d298e97659bf94df7bf8423c5b",
+  "remote_copy_matches": true,
+  "role": "RNGD+ATOM host",
+  "production_runtime": "33d6cb0",
+  "note": "Driver unchanged after launch; source package hashes are recorded per model. output_tokens16 is the initial cancel/reuse case; prefetch isolation retains512 tokens."
+}
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-atom-fault-summary.json b/opt_prime/examples/pd/http_matrix/evidence265/stream265-atom-fault-summary.json
new file mode 100644
index 00000000..01e17cf1
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-atom-fault-summary.json
@@ -0,0 +1,26 @@
+[
+  {
+    "model": "qwen06b",
+    "cases": 3,
+    "survivor_tokens": 512,
+    "tokens_at_source_death": 13
+  },
+  {
+    "model": "qwen4b",
+    "cases": 3,
+    "survivor_tokens": 512,
+    "tokens_at_source_death": 6
+  },
+  {
+    "model": "gemma",
+    "cases": 3,
+    "survivor_tokens": 512,
+    "tokens_at_source_death": 10
+  },
+  {
+    "model": "koni",
+    "cases": 3,
+    "survivor_tokens": 512,
+    "tokens_at_source_death": 5
+  }
+]
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-atom-final.log b/opt_prime/examples/pd/http_matrix/evidence265/stream265-atom-final.log
new file mode 100644
index 00000000..b18effca
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-atom-final.log
@@ -0,0 +1,2 @@
+.................................................                        [100%]
+49 passed in 4.84s
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-atom-full.log b/opt_prime/examples/pd/http_matrix/evidence265/stream265-atom-full.log
new file mode 100644
index 00000000..1a9c43fb
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-atom-full.log
@@ -0,0 +1,51 @@
+.................................................................................................. [ 15%]
+..........s............................................................. [ 26%]
+.s.s.................ss...........................s..................... [ 37%]
+........................................................................ [ 49%]
+.....................s.s...........................................s.... [ 60%]
+........................................................................ [ 71%]
+........................................s.s.............. [ 80%]
+................................................................................................ [ 95%]
+......................s....                                              [100%]
+=============================== warnings summary ===============================
+examples/pd/native_kv/test_native_pack.py::NativePackTest::test_exact_bytes_and_independent_lifetime
+examples/pd/native_kv/test_native_pack.py::NativePackTest::test_exact_bytes_and_independent_lifetime
+examples/pd/native_kv/test_native_pack.py::NativePackTest::test_exact_bytes_and_independent_lifetime
+  /home/gspark/workspace/aicomp-rngd-stream-265/opt_prime/examples/pd/native_kv/test_native_pack.py:24: DeprecationWarning: The argument 'device' of Tensor.is_pinned() is deprecated. Please do not pass this argument. (Triggered internally at /__w/pytorch/pytorch/aten/src/ATen/native/Memory.cpp:31.)
+    self.assertTrue(actual.is_pinned('cuda'))
+
+examples/pd/native_kv/test_packed_lane.py::PackedLaneTest::test_owned_packet_and_other_lane_isolation
+examples/pd/native_kv/test_packed_lane.py::PackedLaneTest::test_owned_packet_and_other_lane_isolation
+examples/pd/native_kv/test_packed_lane.py::PackedLaneTest::test_owned_packet_and_other_lane_isolation
+  /home/gspark/workspace/aicomp-rngd-stream-265/opt_prime/examples/pd/native_kv/gpu_batch_decoder.py:93: DeprecationWarning: The argument 'device' of Tensor.is_pinned() is deprecated. Please do not pass this argument. (Triggered internally at /__w/pytorch/pytorch/aten/src/ATen/native/Memory.cpp:31.)
+    or not host.is_pinned('cuda') or not host.is_contiguous()):
+
+examples/pd/native_kv/test_pinned_receiver.py::PinnedReceiveTest::test_eight_slot_tcp_frames_exact_at_capacity
+examples/pd/native_kv/test_pinned_receiver.py::PinnedReceiveTest::test_eight_slot_tcp_frames_exact_at_capacity
+examples/pd/native_kv/test_pinned_receiver.py::PinnedReceiveTest::test_eight_slot_tcp_frames_exact_at_capacity
+  /home/gspark/workspace/aicomp-rngd-stream-265/opt_prime/examples/pd/native_kv/test_pinned_receiver.py:75: DeprecationWarning: The argument 'device' of Tensor.is_pinned() is deprecated. Please do not pass this argument. (Triggered internally at /__w/pytorch/pytorch/aten/src/ATen/native/Memory.cpp:31.)
+    self.assertTrue(direct.data.host.is_pinned('cuda'))
+
+examples/pd/native_kv/test_pinned_receiver.py::PinnedReceiveTest::test_real_multipart_exact_and_owned
+examples/pd/native_kv/test_pinned_receiver.py::PinnedReceiveTest::test_real_multipart_exact_and_owned
+examples/pd/native_kv/test_pinned_receiver.py::PinnedReceiveTest::test_real_multipart_exact_and_owned
+  /home/gspark/workspace/aicomp-rngd-stream-265/opt_prime/examples/pd/native_kv/test_pinned_receiver.py:25: DeprecationWarning: The argument 'device' of Tensor.is_pinned() is deprecated. Please do not pass this argument. (Triggered internally at /__w/pytorch/pytorch/aten/src/ATen/native/Memory.cpp:31.)
+    self.assertTrue(direct.data.host.is_pinned('cuda'))
+
+tests/test_kvstore.py::test_second_engine_reuses_chunks_from_the_store[lmcache]
+  /home/gspark/venvs/pt213/lib/python3.10/site-packages/torch/nn/attention/flex_attention.py:2481: UserWarning: flex_attention called without torch.compile() - this will use an unfused implementation that materializes the full scores matrix instead of generating a fused kernel.
+
+  SOLUTION: Use torch.compile(flex_attention)(...)
+
+  If you want to debug your score_mod/mask_mod, you can set:
+  torch.nn.attention.flex_attention._FLEX_ATTENTION_DISABLE_COMPILE_DEBUG = True
+
+  This will allow you to use print statements or breakpoints. Note: This doesn't work with the backwards pass and may produce incorrect results.
+    _warn_once(
+
+tests/test_pd_stream_admission.py: 14 warnings
+  /home/gspark/venvs/pt213/lib/python3.10/site-packages/torch/jit/_script.py:365: DeprecationWarning: `torch.jit.script_method` is deprecated. Please switch to `torch.compile` or `torch.export`.
+    warnings.warn(
+
+-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html
+626 passed, 15 skipped, 27 warnings, 253 subtests passed in 168.94s (0:02:48)
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-atom-queue.json b/opt_prime/examples/pd/http_matrix/evidence265/stream265-atom-queue.json
new file mode 100644
index 00000000..211d2e18
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-atom-queue.json
@@ -0,0 +1,22 @@
+[
+  {
+    "model": "qwen06b",
+    "exit_code": 0,
+    "copy_exit_code": 0
+  },
+  {
+    "model": "qwen4b",
+    "exit_code": 0,
+    "copy_exit_code": 0
+  },
+  {
+    "model": "gemma",
+    "exit_code": 0,
+    "copy_exit_code": 0
+  },
+  {
+    "model": "koni",
+    "exit_code": 0,
+    "copy_exit_code": 0
+  }
+]
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-atom-summary.json b/opt_prime/examples/pd/http_matrix/evidence265/stream265-atom-summary.json
new file mode 100644
index 00000000..d0b3b3b0
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-atom-summary.json
@@ -0,0 +1,30 @@
+[
+  {
+    "model": "qwen06b",
+    "cases": 4,
+    "exact_sequences": 8,
+    "stream_spans": 112,
+    "early_spans": 44
+  },
+  {
+    "model": "qwen4b",
+    "cases": 4,
+    "exact_sequences": 8,
+    "stream_spans": 144,
+    "early_spans": 36
+  },
+  {
+    "model": "gemma",
+    "cases": 4,
+    "exact_sequences": 8,
+    "stream_spans": 104,
+    "early_spans": 99
+  },
+  {
+    "model": "koni",
+    "cases": 4,
+    "exact_sequences": 8,
+    "stream_spans": 128,
+    "early_spans": 96
+  }
+]
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-c16-gpu.py b/opt_prime/examples/pd/http_matrix/evidence265/stream265-c16-gpu.py
new file mode 100644
index 00000000..af9b91fd
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-c16-gpu.py
@@ -0,0 +1,53 @@
+"""Alternating same-engine whole/stream test; all runs and warmups retained."""
+import argparse,hashlib,json,os,time,statistics
+from pathlib import Path
+import torch
+from transformers import AutoModelForCausalLM,AutoTokenizer
+from opt_prime.accel.pd import PDRequest
+from opt_prime.accel.pd.pd_batching import SubprocessPrefillWorker,PDBatcher
+class ObservedBatcher(PDBatcher):
+ def __init__(self,*args,**kw):
+  super().__init__(*args,**kw);self.import_trace=[]
+ def _import_progress(self,r):
+  count=self.stats['progress_spans'];done_before=r.pending.done();start=time.perf_counter()
+  super()._import_progress(r)
+  if self.stats['progress_spans']>count:
+   self.import_trace.append({'request':r.request.request_id,'spans':self.stats['progress_spans']-count,'start_s':start,'end_s':time.perf_counter(),'source_done_before':done_before,'source_done_after':r.pending.done()})
+p=argparse.ArgumentParser();p.add_argument('--model',required=True);p.add_argument('--family',required=True);p.add_argument('--label',required=True);p.add_argument('--width',type=int,default=8192);a=p.parse_args()
+root=Path('/home/gspark/workspace/npu_offload/analysis/serving-matrix-201');out=root/f'stream265-{a.label}-c16-gpu.json'
+if out.exists():raise RuntimeError('refuse overwrite')
+print('GPU loading',a.label,flush=True)
+model=AutoModelForCausalLM.from_pretrained(a.model,dtype=torch.bfloat16).to('cuda:0').eval();c=model.config.get_text_config()
+base=AutoTokenizer.from_pretrained(a.model).encode('Seoul is the capital of South Korea. Count one two three.',add_special_tokens=True)
+prompts=[tuple((base*((n+len(base)-1)//len(base)))[:n]) for n in [a.width//4,a.width]*8]
+env=dict(os.environ,PYTHONPATH='/home/gspark/workspace/aicomp-rngd-stream-265/opt_prime:/home/gspark/workspace/aicomp-rngd-stream-265/opt_prime/examples/pd:/tmp/rngd-gpu201-src:/home/gspark/workspace/aicomp-live-pd-234/opt_prime/examples/pd/serving_matrix')
+result={'model':a.label,'width':a.width,'input_lengths':list(map(len,prompts)),'output_tokens':128,'concurrency':16,'decoder_slots':4,'producer_slots':4,'runs':[],
+ 'scope':'fixed compiled width, sixteen queued repeated synthetic prompts, four execution slots, fixed128 output, caches off; host import trace is not device-kernel overlap'}
+result['source_sha256']={name:hashlib.sha256(Path(path).read_bytes()).hexdigest() for name,path in [('prefill_server.py','/home/gspark/workspace/aicomp-rngd-stream-265/opt_prime/opt_prime/accel/pd/prefill_server.py'),('streaming_factories.py','/home/gspark/workspace/aicomp-rngd-stream-265/opt_prime/examples/pd/streaming_factories.py')]}
+worker=None
+try:
+ worker=SubprocessPrefillWorker('/tmp/stream265-python.sh',['--engine','factory','--engine-factory',f'streaming_factories:{a.family}','--model',a.model,'--width',str(a.width),'--chip','0'],slots=4,layers=c.num_hidden_layers,kv_heads=c.num_key_value_heads,head_dim=getattr(c,'head_dim',None) or c.hidden_size//c.num_attention_heads,max_context=a.width+128,env=env,stream_layers=True)
+ reference=None
+ for index,stream in enumerate([False,True,False,True,True,False,False,True]):
+  # Handshake required capability at startup. Change request mode only with no outstanding futures.
+  with worker._lock:
+   assert not worker._futures, "request mode switch requires drained source"
+  worker.stream_layers=stream
+  batch=ObservedBatcher(model,worker,slots=4,max_context=a.width+128,page_size=64,prefix_cache=False)
+  arrivals=[[] for _ in prompts];start=time.perf_counter()
+  futures=[batch.submit(PDRequest(f'{index}-{i}',ids,128),on_token=lambda token,i=i:arrivals[i].append(time.perf_counter()-start)) for i,ids in enumerate(prompts)]
+  batch.run_until_idle();tokens=[f.result(timeout=1) for f in futures];elapsed=time.perf_counter()-start
+  if reference is None:reference=tokens
+  row={'stream_layers':stream,'warmup':index<2,'elapsed_s':elapsed,'ttft_s':[v[0] for v in arrivals],'token_times_s':arrivals,'tokens':tokens,'tokens_exact':tokens==reference,'progress_spans':batch.stats['progress_spans'],'imports':batch.import_trace,'idle':batch.idle}
+  result['runs'].append(row);out.write_text(json.dumps(result,indent=2)+'\n')
+  print(json.dumps({k:v for k,v in row.items() if k not in ['tokens','imports','token_times_s']}),flush=True)
+  assert batch.idle and row['tokens_exact']
+  del batch
+ measured=[r for r in result['runs'] if not r['warmup']]
+ result['summary']={str(mode):{'median_elapsed_s':statistics.median(r['elapsed_s'] for r in measured if r['stream_layers']==mode),'median_ttft_s':[statistics.median(r['ttft_s'][i] for r in measured if r['stream_layers']==mode) for i in range(len(prompts))],'imports_before_source_done':sum(not e['source_done_before'] for r in measured if r['stream_layers']==mode for e in r['imports'])} for mode in [False,True]}
+ result['stage']='complete'
+except Exception as error:
+ result['stage']='failed';result['error']=repr(error);raise
+finally:
+ if worker is not None:worker.close()
+ out.write_text(json.dumps(result,indent=2)+'\n')
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-c16-queue.json b/opt_prime/examples/pd/http_matrix/evidence265/stream265-c16-queue.json
new file mode 100644
index 00000000..ef57f7cc
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-c16-queue.json
@@ -0,0 +1,18 @@
+[
+  {
+    "label": "qwen06b",
+    "exit_code": 0
+  },
+  {
+    "label": "qwen4b",
+    "exit_code": 0
+  },
+  {
+    "label": "gemma",
+    "exit_code": 0
+  },
+  {
+    "label": "koni",
+    "exit_code": 0
+  }
+]
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-c16-queue.py b/opt_prime/examples/pd/http_matrix/evidence265/stream265-c16-queue.py
new file mode 100644
index 00000000..b4dd99d8
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-c16-queue.py
@@ -0,0 +1,15 @@
+import os,subprocess,json
+from pathlib import Path
+root=Path('/home/gspark/workspace/npu_offload/analysis/serving-matrix-201')
+env=dict(os.environ,OMP_NUM_THREADS='4',MKL_NUM_THREADS='4',TORCH_DEVICE_BACKEND_AUTOLOAD='0',PYTHONPATH='/home/gspark/workspace/aicomp-rngd-stream-265/opt_prime:/home/gspark/workspace/aicomp-rngd-stream-265/opt_prime/examples/pd:/tmp/rngd-gpu201-src:/home/gspark/workspace/aicomp-live-pd-234/opt_prime/examples/pd/serving_matrix')
+rows=[]
+for label,family,model,width in [('qwen06b','qwen','/home/gspark/.cache/huggingface/hub/models--Qwen--Qwen3-0.6B/snapshots/c1899de289a04d12100db370d81485cdf75e47ca',128),('qwen4b','qwen','/tmp/serving-matrix-models/Qwen3-4B',128),('gemma','gemma','/tmp/serving-matrix-models/Gemma3-1B',128),('koni','koni','/tmp/serving-matrix-models/KONI-8B',512)]:
+ log=root/f'stream265-{label}-c16-gpu.log'
+ print('start',label,flush=True)
+ with log.open('x') as f:
+  p=subprocess.run(['/home/gspark/venvs/pt213/bin/python','/tmp/stream265-c16-gpu.py','--label',label,'--family',family,'--model',model,'--width','8192'],env=env,stdout=f,stderr=subprocess.STDOUT)
+ rows.append({'label':label,'exit_code':p.returncode});(root/'stream265-c16-queue.json').write_text(json.dumps(rows,indent=2)+'\n')
+ print('finished',label,p.returncode,flush=True)
+
+
+raise SystemExit(int(any(row["exit_code"] for row in rows)))
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-c16-summary.json b/opt_prime/examples/pd/http_matrix/evidence265/stream265-c16-summary.json
new file mode 100644
index 00000000..85f26c2c
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-c16-summary.json
@@ -0,0 +1,333 @@
+{
+  "scope": "One RNGD and GPU, fixed width8192, sixteen alternating2048/8192 inputs, four execution slots, c16, fixed128 output; three measured batches/mode after two warmups. Host completion trace, not device-kernel overlap.",
+  "models": {
+    "qwen06b": {
+      "stage": "complete",
+      "modes": {
+        "False": {
+          "median_elapsed_s": 24.919067284965422,
+          "median_ttft_s": [
+            1.3441354009555653,
+            3.0279682119726203,
+            4.084069180942606,
+            5.740829558984842,
+            6.818180033995304,
+            8.47256327001378,
+            9.578029733966105,
+            11.317168759007473,
+            12.43568969896296,
+            14.075169014977291,
+            15.26905933796661,
+            16.87868417997379,
+            18.043234988988843,
+            19.76309487997787,
+            20.845218267000746,
+            22.515093765978236
+          ],
+          "imports_before_source_done": 0,
+          "elapsed_samples_s": [
+            25.66514272300992,
+            24.473415144952014,
+            24.919067284965422
+          ],
+          "spans_completed_before_source_done": 0
+        },
+        "True": {
+          "median_elapsed_s": 18.640325167041738,
+          "median_ttft_s": [
+            0.8794194650254212,
+            1.9247819870361127,
+            2.7573168739909306,
+            3.8771389669855125,
+            4.843872754019685,
+            6.030559298989829,
+            6.858471117971931,
+            7.982503901992459,
+            8.825046448037028,
+            10.10879552399274,
+            10.97133638401283,
+            12.094571355031803,
+            12.95460512797581,
+            14.28129881597124,
+            15.137993159994949,
+            16.259339428972453
+          ],
+          "imports_before_source_done": 1248,
+          "elapsed_samples_s": [
+            18.640325167041738,
+            18.47151150298305,
+            18.761355215974618
+          ],
+          "spans_completed_before_source_done": 1238
+        }
+      },
+      "elapsed_reduction_pct": 25.19653743907132,
+      "ttft_reduction_pct": [
+        34.57359545770245,
+        36.43321685394506,
+        32.48603900106952,
+        32.46378546603095,
+        28.95651435033637,
+        28.82249318416691,
+        28.393716573565587,
+        29.465539730163638,
+        29.034523523267318,
+        28.179935081162867,
+        28.14661243255153,
+        28.344110085419082,
+        28.202425253112573,
+        27.737538565178255,
+        27.379061393857807,
+        27.784713677091734
+      ],
+      "exact_pairs_including_warmup": 8,
+      "exact_sequences_including_warmup": 128
+    },
+    "qwen4b": {
+      "stage": "complete",
+      "modes": {
+        "False": {
+          "median_elapsed_s": 52.204737705993466,
+          "median_ttft_s": [
+            2.978209750028327,
+            6.330502177996095,
+            8.921052102989051,
+            12.171387223992497,
+            14.763344236009289,
+            18.28267850802513,
+            20.950204905995633,
+            24.538350866001565,
+            27.204236942983698,
+            30.50780795002356,
+            33.081953087006696,
+            36.59110355802113,
+            39.33428174897563,
+            42.762312892999034,
+            45.51400135300355,
+            49.07963147101691
+          ],
+          "imports_before_source_done": 0,
+          "elapsed_samples_s": [
+            52.204737705993466,
+            50.68596732401056,
+            52.44846987101482
+          ],
+          "spans_completed_before_source_done": 0
+        },
+        "True": {
+          "median_elapsed_s": 41.75303764897399,
+          "median_ttft_s": [
+            2.5104515979764983,
+            4.9864127389737405,
+            7.2316790639888495,
+            9.786368964996655,
+            12.067111411015503,
+            14.580784083984327,
+            16.83599117101403,
+            19.437003582017496,
+            21.6844991989783,
+            24.241624642978422,
+            26.515493815008085,
+            29.086338067019824,
+            31.380958228954114,
+            33.93342846102314,
+            36.211979207000695,
+            38.79409088898683
+          ],
+          "imports_before_source_done": 1623,
+          "elapsed_samples_s": [
+            41.793260089994874,
+            41.75303764897399,
+            41.64495707798051
+          ],
+          "spans_completed_before_source_done": 1620
+        }
+      },
+      "elapsed_reduction_pct": 20.020596819931058,
+      "ttft_reduction_pct": [
+        15.706017752691181,
+        21.231956031769705,
+        18.936926042996294,
+        19.59528700470924,
+        18.263022130293493,
+        20.24809670210995,
+        19.638059644009388,
+        20.789283321611073,
+        20.289992899172294,
+        20.539605196512657,
+        19.849067721995105,
+        20.509809110023923,
+        20.219826488197256,
+        20.6464146456899,
+        20.43771558087586,
+        20.956841511949865
+      ],
+      "exact_pairs_including_warmup": 8,
+      "exact_sequences_including_warmup": 128
+    },
+    "gemma": {
+      "stage": "complete",
+      "modes": {
+        "False": {
+          "median_elapsed_s": 27.363664423988666,
+          "median_ttft_s": [
+            1.5447304379777052,
+            3.124876582995057,
+            4.579105741984677,
+            6.150246691016946,
+            7.599762821977492,
+            9.15392376302043,
+            10.624426681024488,
+            12.182542392984033,
+            13.642170040984638,
+            15.204853909031954,
+            16.645412874990143,
+            18.259938745002728,
+            19.71357782703126,
+            21.297166928998195,
+            22.736059891001787,
+            24.30657328403322
+          ],
+          "imports_before_source_done": 0,
+          "elapsed_samples_s": [
+            27.42222376796417,
+            27.227995268011,
+            27.363664423988666
+          ],
+          "spans_completed_before_source_done": 0
+        },
+        "True": {
+          "median_elapsed_s": 26.72555148199899,
+          "median_ttft_s": [
+            1.4865655049798079,
+            2.9654779179836623,
+            4.432614634977654,
+            5.914273313013837,
+            7.382235547003802,
+            8.861578495008871,
+            10.338271517015528,
+            11.813907690986525,
+            13.275143774983007,
+            14.746067096013576,
+            16.217264416976832,
+            17.71145920798881,
+            19.178340876998845,
+            20.639861805015244,
+            22.110377629986033,
+            23.588750781025738
+          ],
+          "imports_before_source_done": 1152,
+          "elapsed_samples_s": [
+            26.73461096500978,
+            26.72555148199899,
+            26.533658277010545
+          ],
+          "spans_completed_before_source_done": 1149
+        }
+      },
+      "elapsed_reduction_pct": 2.331971815259748,
+      "ttft_reduction_pct": [
+        3.7653775421195412,
+        5.100958734780436,
+        3.199120423533408,
+        3.836811592415834,
+        2.862290311805926,
+        3.1936607249512083,
+        2.6933704057654273,
+        3.025925870857671,
+        2.690380378627355,
+        3.017370740707026,
+        2.572170851085387,
+        3.003731527653797,
+        2.715067527207038,
+        3.0863500585515236,
+        2.7519379523774923,
+        2.9532032122315366
+      ],
+      "exact_pairs_including_warmup": 8,
+      "exact_sequences_including_warmup": 128
+    },
+    "koni": {
+      "stage": "complete",
+      "modes": {
+        "False": {
+          "median_elapsed_s": 53.679365599004086,
+          "median_ttft_s": [
+            3.2151334729860537,
+            6.759494353958871,
+            9.592214510019403,
+            13.221200989035424,
+            16.006469668995123,
+            19.479572737996932,
+            22.27997441700427,
+            25.760254251013976,
+            28.583052007015795,
+            32.08897598402109,
+            35.00863416201901,
+            38.51234116300475,
+            41.32874247903237,
+            44.821619161986746,
+            47.54728235700168,
+            51.02830866101431
+          ],
+          "imports_before_source_done": 0,
+          "elapsed_samples_s": [
+            56.212654861970805,
+            53.565289385966025,
+            53.679365599004086
+          ],
+          "spans_completed_before_source_done": 0
+        },
+        "True": {
+          "median_elapsed_s": 42.452936810965184,
+          "median_ttft_s": [
+            2.572252950980328,
+            4.978636874991935,
+            7.344104980991688,
+            9.796351866971236,
+            12.491014941013418,
+            14.944186887005344,
+            17.494215473008808,
+            19.958763492002618,
+            22.4519862899906,
+            24.824661290971562,
+            27.11044464597944,
+            29.78562853601761,
+            32.159831491007935,
+            34.855374017963186,
+            37.42160684498958,
+            39.783537767012604
+          ],
+          "imports_before_source_done": 1440,
+          "elapsed_samples_s": [
+            43.7185002049664,
+            42.10721133998595,
+            42.452936810965184
+          ],
+          "spans_completed_before_source_done": 1440
+        }
+      },
+      "elapsed_reduction_pct": 20.913862641191848,
+      "ttft_reduction_pct": [
+        19.99545360736301,
+        26.34601622122713,
+        23.436814582070554,
+        25.904220992514038,
+        21.962711332850727,
+        23.282778898660574,
+        21.480091738089826,
+        22.52109277525085,
+        21.45000371381022,
+        22.638038361419945,
+        22.560690255686524,
+        22.65952254123176,
+        22.185313266369455,
+        22.235352783676188,
+        21.29601316850239,
+        22.036338630585895
+      ],
+      "exact_pairs_including_warmup": 8,
+      "exact_sequences_including_warmup": 128
+    }
+  }
+}
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-c4-gpu.py b/opt_prime/examples/pd/http_matrix/evidence265/stream265-c4-gpu.py
new file mode 100644
index 00000000..1fb3b997
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-c4-gpu.py
@@ -0,0 +1,53 @@
+"""Alternating same-engine whole/stream test; all runs and warmups retained."""
+import argparse,hashlib,json,os,time,statistics
+from pathlib import Path
+import torch
+from transformers import AutoModelForCausalLM,AutoTokenizer
+from opt_prime.accel.pd import PDRequest
+from opt_prime.accel.pd.pd_batching import SubprocessPrefillWorker,PDBatcher
+class ObservedBatcher(PDBatcher):
+ def __init__(self,*args,**kw):
+  super().__init__(*args,**kw);self.import_trace=[]
+ def _import_progress(self,r):
+  count=self.stats['progress_spans'];done_before=r.pending.done();start=time.perf_counter()
+  super()._import_progress(r)
+  if self.stats['progress_spans']>count:
+   self.import_trace.append({'request':r.request.request_id,'spans':self.stats['progress_spans']-count,'start_s':start,'end_s':time.perf_counter(),'source_done_before':done_before,'source_done_after':r.pending.done()})
+p=argparse.ArgumentParser();p.add_argument('--model',required=True);p.add_argument('--family',required=True);p.add_argument('--label',required=True);p.add_argument('--width',type=int,default=8192);a=p.parse_args()
+root=Path('/home/gspark/workspace/npu_offload/analysis/serving-matrix-201');out=root/f'stream265-{a.label}-c4-gpu.json'
+if out.exists():raise RuntimeError('refuse overwrite')
+print('GPU loading',a.label,flush=True)
+model=AutoModelForCausalLM.from_pretrained(a.model,dtype=torch.bfloat16).to('cuda:0').eval();c=model.config.get_text_config()
+base=AutoTokenizer.from_pretrained(a.model).encode('Seoul is the capital of South Korea. Count one two three.',add_special_tokens=True)
+prompts=[tuple((base*((n+len(base)-1)//len(base)))[:n]) for n in [a.width//4,a.width,a.width//4,a.width]]
+env=dict(os.environ,PYTHONPATH='/home/gspark/workspace/aicomp-rngd-stream-265/opt_prime:/home/gspark/workspace/aicomp-rngd-stream-265/opt_prime/examples/pd:/tmp/rngd-gpu201-src:/home/gspark/workspace/aicomp-live-pd-234/opt_prime/examples/pd/serving_matrix')
+result={'model':a.label,'width':a.width,'input_lengths':list(map(len,prompts)),'output_tokens':128,'concurrency':4,'runs':[],
+ 'scope':'fixed compiled width, four queued repeated synthetic prompts, fixed128 output, caches off; host import trace is not device-kernel overlap'}
+result['source_sha256']={name:hashlib.sha256(Path(path).read_bytes()).hexdigest() for name,path in [('prefill_server.py','/home/gspark/workspace/aicomp-rngd-stream-265/opt_prime/opt_prime/accel/pd/prefill_server.py'),('streaming_factories.py','/home/gspark/workspace/aicomp-rngd-stream-265/opt_prime/examples/pd/streaming_factories.py')]}
+worker=None
+try:
+ worker=SubprocessPrefillWorker('/tmp/stream265-python.sh',['--engine','factory','--engine-factory',f'streaming_factories:{a.family}','--model',a.model,'--width',str(a.width),'--chip','0'],slots=4,layers=c.num_hidden_layers,kv_heads=c.num_key_value_heads,head_dim=getattr(c,'head_dim',None) or c.hidden_size//c.num_attention_heads,max_context=a.width+128,env=env,stream_layers=True)
+ reference=None
+ for index,stream in enumerate([False,True,False,True,True,False,False,True]):
+  # Handshake required capability at startup. Change request mode only with no outstanding futures.
+  with worker._lock:
+   assert not worker._futures, "request mode switch requires drained source"
+  worker.stream_layers=stream
+  batch=ObservedBatcher(model,worker,slots=4,max_context=a.width+128,page_size=64,prefix_cache=False)
+  arrivals=[[],[],[],[]];start=time.perf_counter()
+  futures=[batch.submit(PDRequest(f'{index}-{i}',ids,128),on_token=lambda token,i=i:arrivals[i].append(time.perf_counter()-start)) for i,ids in enumerate(prompts)]
+  batch.run_until_idle();tokens=[f.result(timeout=1) for f in futures];elapsed=time.perf_counter()-start
+  if reference is None:reference=tokens
+  row={'stream_layers':stream,'warmup':index<2,'elapsed_s':elapsed,'ttft_s':[v[0] for v in arrivals],'token_times_s':arrivals,'tokens':tokens,'tokens_exact':tokens==reference,'progress_spans':batch.stats['progress_spans'],'imports':batch.import_trace,'idle':batch.idle}
+  result['runs'].append(row);out.write_text(json.dumps(result,indent=2)+'\n')
+  print(json.dumps({k:v for k,v in row.items() if k not in ['tokens','imports','token_times_s']}),flush=True)
+  assert batch.idle and row['tokens_exact']
+  del batch
+ measured=[r for r in result['runs'] if not r['warmup']]
+ result['summary']={str(mode):{'median_elapsed_s':statistics.median(r['elapsed_s'] for r in measured if r['stream_layers']==mode),'median_ttft_s':[statistics.median(r['ttft_s'][i] for r in measured if r['stream_layers']==mode) for i in range(4)],'imports_before_source_done':sum(not e['source_done_before'] for r in measured if r['stream_layers']==mode for e in r['imports'])} for mode in [False,True]}
+ result['stage']='complete'
+except Exception as error:
+ result['stage']='failed';result['error']=repr(error);raise
+finally:
+ if worker is not None:worker.close()
+ out.write_text(json.dumps(result,indent=2)+'\n')
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-c4-queue.json b/opt_prime/examples/pd/http_matrix/evidence265/stream265-c4-queue.json
new file mode 100644
index 00000000..ef57f7cc
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-c4-queue.json
@@ -0,0 +1,18 @@
+[
+  {
+    "label": "qwen06b",
+    "exit_code": 0
+  },
+  {
+    "label": "qwen4b",
+    "exit_code": 0
+  },
+  {
+    "label": "gemma",
+    "exit_code": 0
+  },
+  {
+    "label": "koni",
+    "exit_code": 0
+  }
+]
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-c4-queue.py b/opt_prime/examples/pd/http_matrix/evidence265/stream265-c4-queue.py
new file mode 100644
index 00000000..10eeeaa5
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-c4-queue.py
@@ -0,0 +1,15 @@
+import os,subprocess,json
+from pathlib import Path
+root=Path('/home/gspark/workspace/npu_offload/analysis/serving-matrix-201')
+env=dict(os.environ,OMP_NUM_THREADS='4',MKL_NUM_THREADS='4',TORCH_DEVICE_BACKEND_AUTOLOAD='0',PYTHONPATH='/home/gspark/workspace/aicomp-rngd-stream-265/opt_prime:/home/gspark/workspace/aicomp-rngd-stream-265/opt_prime/examples/pd:/tmp/rngd-gpu201-src:/home/gspark/workspace/aicomp-live-pd-234/opt_prime/examples/pd/serving_matrix')
+rows=[]
+for label,family,model,width in [('qwen06b','qwen','/home/gspark/.cache/huggingface/hub/models--Qwen--Qwen3-0.6B/snapshots/c1899de289a04d12100db370d81485cdf75e47ca',128),('qwen4b','qwen','/tmp/serving-matrix-models/Qwen3-4B',128),('gemma','gemma','/tmp/serving-matrix-models/Gemma3-1B',128),('koni','koni','/tmp/serving-matrix-models/KONI-8B',512)]:
+ log=root/f'stream265-{label}-c4-gpu.log'
+ print('start',label,flush=True)
+ with log.open('x') as f:
+  p=subprocess.run(['/home/gspark/venvs/pt213/bin/python','/tmp/stream265-c4-gpu.py','--label',label,'--family',family,'--model',model,'--width','8192'],env=env,stdout=f,stderr=subprocess.STDOUT)
+ rows.append({'label':label,'exit_code':p.returncode});(root/'stream265-c4-queue.json').write_text(json.dumps(rows,indent=2)+'\n')
+ print('finished',label,p.returncode,flush=True)
+
+
+raise SystemExit(int(any(row["exit_code"] for row in rows)))
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-c4-summary.json b/opt_prime/examples/pd/http_matrix/evidence265/stream265-c4-summary.json
new file mode 100644
index 00000000..3115a031
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-c4-summary.json
@@ -0,0 +1,189 @@
+{
+  "scope": "One RNGD and GPU, fixed width8192, mixed2048/8192/2048/8192 inputs, c4, fixed128 output; three measured batches/mode after two warmups. Host completion trace, not device-kernel overlap.",
+  "models": {
+    "qwen06b": {
+      "stage": "complete",
+      "modes": {
+        "False": {
+          "median_elapsed_s": 7.98222386703128,
+          "median_ttft_s": [
+            1.259451771969907,
+            2.903907519998029,
+            3.9043145399773493,
+            5.478259920957498
+          ],
+          "imports_before_source_done": 0,
+          "elapsed_samples_s": [
+            7.98222386703128,
+            7.75318016303936,
+            8.002533552004024
+          ],
+          "spans_completed_before_source_done": 0
+        },
+        "True": {
+          "median_elapsed_s": 6.327923887991346,
+          "median_ttft_s": [
+            0.8857374450308271,
+            1.960289182025008,
+            2.7759200599975884,
+            3.8895518259960227
+          ],
+          "imports_before_source_done": 254,
+          "elapsed_samples_s": [
+            6.295799633022398,
+            6.327923887991346,
+            6.333102408971172
+          ],
+          "spans_completed_before_source_done": 246
+        }
+      },
+      "elapsed_reduction_pct": 20.724800589377544,
+      "ttft_reduction_pct": [
+        29.67277789085594,
+        32.494779240547636,
+        28.901218598702016,
+        29.00023215188736
+      ],
+      "exact_pairs_including_warmup": 8,
+      "exact_sequences_including_warmup": 32
+    },
+    "qwen4b": {
+      "stage": "complete",
+      "modes": {
+        "False": {
+          "median_elapsed_s": 15.47749203199055,
+          "median_ttft_s": [
+            2.927928482997231,
+            6.338298866990954,
+            8.997235207993072,
+            12.298796943970956
+          ],
+          "imports_before_source_done": 0,
+          "elapsed_samples_s": [
+            15.403223996981978,
+            15.47749203199055,
+            15.749702950008214
+          ],
+          "spans_completed_before_source_done": 0
+        },
+        "True": {
+          "median_elapsed_s": 12.933404473005794,
+          "median_ttft_s": [
+            2.324952519033104,
+            4.952954363019671,
+            7.195328815025277,
+            9.858466033008881
+          ],
+          "imports_before_source_done": 327,
+          "elapsed_samples_s": [
+            12.933404473005794,
+            13.035745109023992,
+            12.724006158008706
+          ],
+          "spans_completed_before_source_done": 324
+        }
+      },
+      "elapsed_reduction_pct": 16.43733722315193,
+      "ttft_reduction_pct": [
+        20.59394440354906,
+        21.85672422589566,
+        20.027334523465555,
+        19.842029444663368
+      ],
+      "exact_pairs_including_warmup": 8,
+      "exact_sequences_including_warmup": 32
+    },
+    "gemma": {
+      "stage": "complete",
+      "modes": {
+        "False": {
+          "median_elapsed_s": 9.206592933973297,
+          "median_ttft_s": [
+            1.5453090670052916,
+            3.087043087987695,
+            4.586227795982268,
+            6.160341106005944
+          ],
+          "imports_before_source_done": 0,
+          "elapsed_samples_s": [
+            9.206592933973297,
+            9.219260835961904,
+            9.096908859966788
+          ],
+          "spans_completed_before_source_done": 0
+        },
+        "True": {
+          "median_elapsed_s": 8.998545984039083,
+          "median_ttft_s": [
+            1.48697250202531,
+            2.963507455016952,
+            4.429440081003122,
+            5.91638085100567
+          ],
+          "imports_before_source_done": 233,
+          "elapsed_samples_s": [
+            8.90360896504717,
+            8.998545984039083,
+            9.033039704023395
+          ],
+          "spans_completed_before_source_done": 231
+        }
+      },
+      "elapsed_reduction_pct": 2.2597604936620885,
+      "ttft_reduction_pct": [
+        3.7750742699668605,
+        4.0017463135336495,
+        3.418663920629905,
+        3.9601744579115694
+      ],
+      "exact_pairs_including_warmup": 8,
+      "exact_sequences_including_warmup": 32
+    },
+    "koni": {
+      "stage": "complete",
+      "modes": {
+        "False": {
+          "median_elapsed_s": 16.025821529969107,
+          "median_ttft_s": [
+            3.115041946002748,
+            6.684042648004834,
+            9.67009445396252,
+            13.35132329998305
+          ],
+          "imports_before_source_done": 0,
+          "elapsed_samples_s": [
+            15.782486921001691,
+            16.025821529969107,
+            16.056883676967118
+          ],
+          "spans_completed_before_source_done": 0
+        },
+        "True": {
+          "median_elapsed_s": 12.925747622037306,
+          "median_ttft_s": [
+            2.705328407988418,
+            5.253284092003014,
+            7.800534453999717,
+            10.255128621996846
+          ],
+          "imports_before_source_done": 291,
+          "elapsed_samples_s": [
+            13.131439948047046,
+            12.925747622037306,
+            12.864730914996471
+          ],
+          "spans_completed_before_source_done": 288
+        }
+      },
+      "elapsed_reduction_pct": 19.344243302188936,
+      "ttft_reduction_pct": [
+        13.152745456287619,
+        21.40558687830779,
+        19.333420256269708,
+        23.190170804942866
+      ],
+      "exact_pairs_including_warmup": 8,
+      "exact_sequences_including_warmup": 32
+    }
+  }
+}
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-capacity-owner-final.log b/opt_prime/examples/pd/http_matrix/evidence265/stream265-capacity-owner-final.log
new file mode 100644
index 00000000..3b36e20c
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-capacity-owner-final.log
@@ -0,0 +1,19 @@
+.................                                                        [100%]
+=============================== warnings summary ===============================
+tests/test_pd_stream_admission.py::test_hybrid_partial_import_happens_before_completion_without_admission[cpu]
+  /home/gspark/venvs/pt213/lib/python3.10/site-packages/torch/nn/attention/flex_attention.py:2481: UserWarning: flex_attention called without torch.compile() - this will use an unfused implementation that materializes the full scores matrix instead of generating a fused kernel.
+
+  SOLUTION: Use torch.compile(flex_attention)(...)
+
+  If you want to debug your score_mod/mask_mod, you can set:
+  torch.nn.attention.flex_attention._FLEX_ATTENTION_DISABLE_COMPILE_DEBUG = True
+
+  This will allow you to use print statements or breakpoints. Note: This doesn't work with the backwards pass and may produce incorrect results.
+    _warn_once(
+
+tests/test_pd_stream_admission.py: 14 warnings
+  /home/gspark/venvs/pt213/lib/python3.10/site-packages/torch/jit/_script.py:365: DeprecationWarning: `torch.jit.script_method` is deprecated. Please switch to `torch.compile` or `torch.export`.
+    warnings.warn(
+
+-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html
+17 passed, 15 warnings in 9.66s
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-capacity-red.log b/opt_prime/examples/pd/http_matrix/evidence265/stream265-capacity-red.log
new file mode 100644
index 00000000..506f72cb
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-capacity-red.log
@@ -0,0 +1,29 @@
+F                                                                        [100%]
+=================================== FAILURES ===================================
+______________ test_invalid_stream_capacity_fails_only_its_future ______________
+
+children = (<function children.<locals>.make at 0x748344d9ef80>, [<Popen: returncode: None args: ['/tmp/pytest-of-gspark/pytest-185/test_inval...>], ['psm_1f20e252'], PosixPath('/tmp/pytest-of-gspark/pytest-185/test_invalid_stream_capacity_f0/gate'))
+
+    def test_invalid_stream_capacity_fails_only_its_future(children):
+        w,gate=make_stream(children)
+>       bad=w.submit(PDRequest('too-long',tuple(range(9)),1),slot=0)
+
+tests/test_subprocess_layer_progress.py:114:
+_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
+
+self = <opt_prime.accel.pd.pd_batching.SubprocessPrefillWorker object at 0x748344d83ca0>
+request = PDRequest(request_id='too-long', input_ids=(0, 1, 2, 3, 4, 5, 6, 7, 8), max_new_tokens=1, sampling=SamplingParams(temperature=0.0, top_p=1.0, top_k=0, seed=None, stop_token_ids=()))
+send_from = 0, slot = 0
+
+    def submit(self, request, send_from=0, slot=None) -> Future:
+        if slot is None or not 0 <= slot < self.slab.shape[0]:
+            raise ValueError("SubprocessPrefillWorker needs a valid decode lane (slot)")
+        if self.stream_layers and (type(send_from) is not int or
+                not 0 <= send_from <= len(request.input_ids) <= self.slab.shape[-2]):
+>           raise ValueError("invalid streamed KV suffix")
+E           ValueError: invalid streamed KV suffix
+
+opt_prime/accel/pd/pd_batching.py:393: ValueError
+=========================== short test summary info ============================
+FAILED tests/test_subprocess_layer_progress.py::test_invalid_stream_capacity_fails_only_its_future
+1 failed in 1.52s
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-cli-red.log b/opt_prime/examples/pd/http_matrix/evidence265/stream265-cli-red.log
new file mode 100644
index 00000000..08c735c1
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-cli-red.log
@@ -0,0 +1,158 @@
+FFFFF                                                                    [100%]
+=================================== FAILURES ===================================
+________ test_layer_stream_rejects_unsupported_owner[atom-python-None] _________
+
+backend = 'atom', python = 'python', source = None
+
+    @pytest.mark.parametrize('backend,python,source', [('atom','python',None),('rngd','python',None),('torch',None,None),('torch','python','cpu')])
+    def test_layer_stream_rejects_unsupported_owner(backend,python,source):
+        spec=importlib.util.spec_from_file_location('stream_factory',Path(__file__).parents[1]/'examples/pd/serve_batched.py')
+        factory=importlib.util.module_from_spec(spec);spec.loader.exec_module(factory)
+        args=SimpleNamespace(pd_prefill_stream_layers=True,backend=backend,pd_prefill_python=python,pd_prefill_device=source)
+        with pytest.raises(ValueError,match='layer streaming'):
+>           factory.prefill_worker(args,1,None)
+
+tests/test_serving_prefill_cache.py:99:
+_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
+
+a = namespace(pd_prefill_stream_layers=True, backend='atom', pd_prefill_python='python', pd_prefill_device=None)
+slots = 1, config = None
+
+    def prefill_worker(a, slots, config):
+        """the PD prefill side from the flags, or None (aggregated serving)"""
+        overlap = getattr(a, "pd_prefill_copy_overlap", False)
+        if overlap and (not a.pd_prefill_device or a.backend != "torch" or
+                        torch.device(a.pd_prefill_device).type != "cuda" or torch.device(a.device).type != "cuda"):
+            raise ValueError("PD copy overlap requires a Torch CUDA producer and CUDA decoder")
+        if a.pd_prefill_device:
+            chunk = getattr(a, "pd_prefill_chunk", 2048)
+            if chunk <= 0:
+                raise ValueError("PD prefill chunk must be positive")
+            from opt_prime.accel.pd.pd_batching import PrefillWorker, TorchPrefillEngine
+            m = AutoModelForCausalLM.from_pretrained(a.model, dtype=getattr(torch, a.dtype)).to(a.pd_prefill_device).eval()
+            return PrefillWorker(TorchPrefillEngine(m, max_context=a.max_context, chunk=chunk,
+                                                    prefix_cache=not getattr(a, "pd_prefill_no_prefix_cache", False),
+                                                    copy_overlap=overlap,
+                                                    out_device=a.device
+                                                    if a.backend == "torch" else "cpu"))
+        if a.pd_prefill_python:
+            import os, shlex
+            from opt_prime.accel.pd.pd_batching import SubprocessPrefillWorker
+>           c = config.get_text_config()
+E           AttributeError: 'NoneType' object has no attribute 'get_text_config'
+
+examples/pd/serve_batched.py:50: AttributeError
+________ test_layer_stream_rejects_unsupported_owner[rngd-python-None] _________
+
+backend = 'rngd', python = 'python', source = None
+
+    @pytest.mark.parametrize('backend,python,source', [('atom','python',None),('rngd','python',None),('torch',None,None),('torch','python','cpu')])
+    def test_layer_stream_rejects_unsupported_owner(backend,python,source):
+        spec=importlib.util.spec_from_file_location('stream_factory',Path(__file__).parents[1]/'examples/pd/serve_batched.py')
+        factory=importlib.util.module_from_spec(spec);spec.loader.exec_module(factory)
+        args=SimpleNamespace(pd_prefill_stream_layers=True,backend=backend,pd_prefill_python=python,pd_prefill_device=source)
+        with pytest.raises(ValueError,match='layer streaming'):
+>           factory.prefill_worker(args,1,None)
+
+tests/test_serving_prefill_cache.py:99:
+_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
+
+a = namespace(pd_prefill_stream_layers=True, backend='rngd', pd_prefill_python='python', pd_prefill_device=None)
+slots = 1, config = None
+
+    def prefill_worker(a, slots, config):
+        """the PD prefill side from the flags, or None (aggregated serving)"""
+        overlap = getattr(a, "pd_prefill_copy_overlap", False)
+        if overlap and (not a.pd_prefill_device or a.backend != "torch" or
+                        torch.device(a.pd_prefill_device).type != "cuda" or torch.device(a.device).type != "cuda"):
+            raise ValueError("PD copy overlap requires a Torch CUDA producer and CUDA decoder")
+        if a.pd_prefill_device:
+            chunk = getattr(a, "pd_prefill_chunk", 2048)
+            if chunk <= 0:
+                raise ValueError("PD prefill chunk must be positive")
+            from opt_prime.accel.pd.pd_batching import PrefillWorker, TorchPrefillEngine
+            m = AutoModelForCausalLM.from_pretrained(a.model, dtype=getattr(torch, a.dtype)).to(a.pd_prefill_device).eval()
+            return PrefillWorker(TorchPrefillEngine(m, max_context=a.max_context, chunk=chunk,
+                                                    prefix_cache=not getattr(a, "pd_prefill_no_prefix_cache", False),
+                                                    copy_overlap=overlap,
+                                                    out_device=a.device
+                                                    if a.backend == "torch" else "cpu"))
+        if a.pd_prefill_python:
+            import os, shlex
+            from opt_prime.accel.pd.pd_batching import SubprocessPrefillWorker
+>           c = config.get_text_config()
+E           AttributeError: 'NoneType' object has no attribute 'get_text_config'
+
+examples/pd/serve_batched.py:50: AttributeError
+_________ test_layer_stream_rejects_unsupported_owner[torch-None-None] _________
+
+backend = 'torch', python = None, source = None
+
+    @pytest.mark.parametrize('backend,python,source', [('atom','python',None),('rngd','python',None),('torch',None,None),('torch','python','cpu')])
+    def test_layer_stream_rejects_unsupported_owner(backend,python,source):
+        spec=importlib.util.spec_from_file_location('stream_factory',Path(__file__).parents[1]/'examples/pd/serve_batched.py')
+        factory=importlib.util.module_from_spec(spec);spec.loader.exec_module(factory)
+        args=SimpleNamespace(pd_prefill_stream_layers=True,backend=backend,pd_prefill_python=python,pd_prefill_device=source)
+>       with pytest.raises(ValueError,match='layer streaming'):
+E       Failed: DID NOT RAISE ValueError
+
+tests/test_serving_prefill_cache.py:98: Failed
+________ test_layer_stream_rejects_unsupported_owner[torch-python-cpu] _________
+
+backend = 'torch', python = 'python', source = 'cpu'
+
+    @pytest.mark.parametrize('backend,python,source', [('atom','python',None),('rngd','python',None),('torch',None,None),('torch','python','cpu')])
+    def test_layer_stream_rejects_unsupported_owner(backend,python,source):
+        spec=importlib.util.spec_from_file_location('stream_factory',Path(__file__).parents[1]/'examples/pd/serve_batched.py')
+        factory=importlib.util.module_from_spec(spec);spec.loader.exec_module(factory)
+        args=SimpleNamespace(pd_prefill_stream_layers=True,backend=backend,pd_prefill_python=python,pd_prefill_device=source)
+        with pytest.raises(ValueError,match='layer streaming'):
+>           factory.prefill_worker(args,1,None)
+
+tests/test_serving_prefill_cache.py:99:
+_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
+
+a = namespace(pd_prefill_stream_layers=True, backend='torch', pd_prefill_python='python', pd_prefill_device='cpu')
+slots = 1, config = None
+
+    def prefill_worker(a, slots, config):
+        """the PD prefill side from the flags, or None (aggregated serving)"""
+        overlap = getattr(a, "pd_prefill_copy_overlap", False)
+        if overlap and (not a.pd_prefill_device or a.backend != "torch" or
+                        torch.device(a.pd_prefill_device).type != "cuda" or torch.device(a.device).type != "cuda"):
+            raise ValueError("PD copy overlap requires a Torch CUDA producer and CUDA decoder")
+        if a.pd_prefill_device:
+            chunk = getattr(a, "pd_prefill_chunk", 2048)
+            if chunk <= 0:
+                raise ValueError("PD prefill chunk must be positive")
+            from opt_prime.accel.pd.pd_batching import PrefillWorker, TorchPrefillEngine
+>           m = AutoModelForCausalLM.from_pretrained(a.model, dtype=getattr(torch, a.dtype)).to(a.pd_prefill_device).eval()
+E           AttributeError: 'types.SimpleNamespace' object has no attribute 'model'
+
+examples/pd/serve_batched.py:41: AttributeError
+________________ test_layer_stream_reaches_subprocess_receiver _________________
+
+monkeypatch = <_pytest.monkeypatch.MonkeyPatch object at 0x7c82bbe14310>
+
+    def test_layer_stream_reaches_subprocess_receiver(monkeypatch):
+        import opt_prime.accel.pd.pd_batching as pd
+        spec=importlib.util.spec_from_file_location('stream_factory',Path(__file__).parents[1]/'examples/pd/serve_batched.py')
+        factory=importlib.util.module_from_spec(spec);spec.loader.exec_module(factory)
+        received={}
+        def worker(*args,**kw):received.update(kw);return object()
+        monkeypatch.setattr(pd,'SubprocessPrefillWorker',worker)
+        args=SimpleNamespace(pd_prefill_stream_layers=True,backend='torch',pd_prefill_python='python',pd_prefill_device=None,
+                             pd_prefill_pythonpath=None,pd_prefill_args='--engine rngd-full',model='local',max_context=16)
+        config=LlamaConfig(hidden_size=32,num_attention_heads=4,num_key_value_heads=2,num_hidden_layers=2)
+        factory.prefill_worker(args,1,config)
+>       assert received['stream_layers'] is True
+E       KeyError: 'stream_layers'
+
+tests/test_serving_prefill_cache.py:113: KeyError
+=========================== short test summary info ============================
+FAILED tests/test_serving_prefill_cache.py::test_layer_stream_rejects_unsupported_owner[atom-python-None]
+FAILED tests/test_serving_prefill_cache.py::test_layer_stream_rejects_unsupported_owner[rngd-python-None]
+FAILED tests/test_serving_prefill_cache.py::test_layer_stream_rejects_unsupported_owner[torch-None-None]
+FAILED tests/test_serving_prefill_cache.py::test_layer_stream_rejects_unsupported_owner[torch-python-cpu]
+FAILED tests/test_serving_prefill_cache.py::test_layer_stream_reaches_subprocess_receiver
+5 failed, 7 deselected in 2.49s
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-control-gpu.py b/opt_prime/examples/pd/http_matrix/evidence265/stream265-control-gpu.py
new file mode 100644
index 00000000..5f9d6d4a
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-control-gpu.py
@@ -0,0 +1,55 @@
+"""Alternating same-engine whole/stream test; all runs and warmups retained."""
+import argparse,hashlib,json,os,time,statistics
+from pathlib import Path
+import torch
+from transformers import AutoModelForCausalLM,AutoTokenizer
+from opt_prime.accel.pd import PDRequest
+from opt_prime.accel.pd.pd_batching import SubprocessPrefillWorker,PDBatcher
+class ObservedBatcher(PDBatcher):
+ def __init__(self,*args,**kw):
+  super().__init__(*args,**kw);self.import_trace=[]
+ def _import_progress(self,r):
+  count=self.stats['progress_spans'];done_before=r.pending.done();start=time.perf_counter()
+  super()._import_progress(r)
+  if self.stats['progress_spans']>count:
+   self.import_trace.append({'request':r.request.request_id,'spans':self.stats['progress_spans']-count,'start_s':start,'end_s':time.perf_counter(),'source_done_before':done_before,'source_done_after':r.pending.done()})
+p=argparse.ArgumentParser();p.add_argument('--model',required=True);p.add_argument('--family',required=True);p.add_argument('--label',required=True);p.add_argument('--width',type=int,default=8192);a=p.parse_args()
+root=Path('/home/gspark/workspace/npu_offload/analysis/serving-matrix-201');out=root/f'stream265-{a.label}-control-gpu.json'
+if out.exists():raise RuntimeError('refuse overwrite')
+print('GPU loading',a.label,flush=True)
+model=AutoModelForCausalLM.from_pretrained(a.model,dtype=torch.bfloat16).to('cuda:0').eval();c=model.config.get_text_config()
+base=AutoTokenizer.from_pretrained(a.model).encode('Seoul is the capital of South Korea. Count one two three.',add_special_tokens=True)
+prompts=[tuple((base*((n+len(base)-1)//len(base)))[:n]) for n in [a.width//4,a.width]]
+env=dict(os.environ,PYTHONPATH='/home/gspark/workspace/aicomp-rngd-stream-265/opt_prime:/home/gspark/workspace/aicomp-rngd-stream-265/opt_prime/examples/pd:/tmp/rngd-gpu201-src:/home/gspark/workspace/aicomp-live-pd-234/opt_prime/examples/pd/serving_matrix')
+modes=['whole','early','deferred','early','deferred','whole','deferred','whole','early','whole','early','deferred']
+env['STREAM265_MODES']=json.dumps(modes)
+result={'model':a.label,'width':a.width,'input_lengths':list(map(len,prompts)),'output_tokens':32,'concurrency':2,'runs':[],
+ 'scope':'test-only whole/early/deferred-notification control; identical source layer compute and SHM writes in early/deferred, three measured batches per mode after one warmup each; not device-DMA trace'}
+result['source_sha256']={name:hashlib.sha256(Path(path).read_bytes()).hexdigest() for name,path in [('prefill_server.py','/home/gspark/workspace/aicomp-rngd-stream-265/opt_prime/opt_prime/accel/pd/prefill_server.py'),('streaming_factories.py','/home/gspark/workspace/aicomp-rngd-stream-265/opt_prime/examples/pd/streaming_factories.py')]}
+result['control_sha256']={name:hashlib.sha256(Path('/tmp',name).read_bytes()).hexdigest() for name in ['stream265_control.py','stream265-controlled-server.py','stream265-control-python.sh']}
+worker=None
+try:
+ worker=SubprocessPrefillWorker('/tmp/stream265-control-python.sh',['--engine','factory','--engine-factory',f'streaming_factories:{a.family}','--model',a.model,'--width',str(a.width),'--chip','0'],slots=2,layers=c.num_hidden_layers,kv_heads=c.num_key_value_heads,head_dim=getattr(c,'head_dim',None) or c.hidden_size//c.num_attention_heads,max_context=a.width+128,env=env,stream_layers=True)
+ reference=None
+ for index,mode in enumerate(modes):
+  stream=mode!="whole"
+  # Handshake required capability at startup. Change request mode only with no outstanding futures.
+  with worker._lock:
+   assert not worker._futures, "request mode switch requires drained source"
+  worker.stream_layers=stream
+  batch=ObservedBatcher(model,worker,slots=2,max_context=a.width+128,page_size=64,prefix_cache=False)
+  arrivals=[[],[]];start=time.perf_counter()
+  futures=[batch.submit(PDRequest(f'{index}-{i}',ids,32),on_token=lambda token,i=i:arrivals[i].append(time.perf_counter()-start)) for i,ids in enumerate(prompts)]
+  batch.run_until_idle();tokens=[f.result(timeout=1) for f in futures];elapsed=time.perf_counter()-start
+  if reference is None:reference=tokens
+  row={'stream_layers':stream,'mode':mode,'warmup':index<3,'elapsed_s':elapsed,'ttft_s':[v[0] for v in arrivals],'token_times_s':arrivals,'tokens':tokens,'tokens_exact':tokens==reference,'progress_spans':batch.stats['progress_spans'],'imports':batch.import_trace,'idle':batch.idle}
+  result['runs'].append(row);out.write_text(json.dumps(result,indent=2)+'\n')
+  print(json.dumps({k:v for k,v in row.items() if k not in ['tokens','imports','token_times_s']}),flush=True)
+  assert batch.idle and row['tokens_exact']
+  del batch
+ result['stage']='complete'
+except Exception as error:
+ result['stage']='failed';result['error']=repr(error);raise
+finally:
+ if worker is not None:worker.close()
+ out.write_text(json.dumps(result,indent=2)+'\n')
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-control-python.sh b/opt_prime/examples/pd/http_matrix/evidence265/stream265-control-python.sh
new file mode 100755
index 00000000..1d25dc8f
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-control-python.sh
@@ -0,0 +1,3 @@
+#!/bin/bash
+set -eu
+exec env TORCH_DEVICE_BACKEND_AUTOLOAD=0 /tmp/codex-rngd-gpu-201/bin/python /tmp/stream265-controlled-server.py "$@"
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-control-queue.json b/opt_prime/examples/pd/http_matrix/evidence265/stream265-control-queue.json
new file mode 100644
index 00000000..ef57f7cc
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-control-queue.json
@@ -0,0 +1,18 @@
+[
+  {
+    "label": "qwen06b",
+    "exit_code": 0
+  },
+  {
+    "label": "qwen4b",
+    "exit_code": 0
+  },
+  {
+    "label": "gemma",
+    "exit_code": 0
+  },
+  {
+    "label": "koni",
+    "exit_code": 0
+  }
+]
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-control-queue.py b/opt_prime/examples/pd/http_matrix/evidence265/stream265-control-queue.py
new file mode 100644
index 00000000..292bbab5
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-control-queue.py
@@ -0,0 +1,15 @@
+import os,subprocess,json
+from pathlib import Path
+root=Path('/home/gspark/workspace/npu_offload/analysis/serving-matrix-201')
+env=dict(os.environ,OMP_NUM_THREADS='4',MKL_NUM_THREADS='4',TORCH_DEVICE_BACKEND_AUTOLOAD='0',PYTHONPATH='/home/gspark/workspace/aicomp-rngd-stream-265/opt_prime:/home/gspark/workspace/aicomp-rngd-stream-265/opt_prime/examples/pd:/tmp/rngd-gpu201-src:/home/gspark/workspace/aicomp-live-pd-234/opt_prime/examples/pd/serving_matrix')
+rows=[]
+for label,family,model,width in [('qwen06b','qwen','/home/gspark/.cache/huggingface/hub/models--Qwen--Qwen3-0.6B/snapshots/c1899de289a04d12100db370d81485cdf75e47ca',128),('qwen4b','qwen','/tmp/serving-matrix-models/Qwen3-4B',128),('gemma','gemma','/tmp/serving-matrix-models/Gemma3-1B',128),('koni','koni','/tmp/serving-matrix-models/KONI-8B',512)]:
+ log=root/f'stream265-{label}-control-gpu.log'
+ print('start',label,flush=True)
+ with log.open('x') as f:
+  p=subprocess.run(['/home/gspark/venvs/pt213/bin/python','/tmp/stream265-control-gpu.py','--label',label,'--family',family,'--model',model,'--width','8192'],env=env,stdout=f,stderr=subprocess.STDOUT)
+ rows.append({'label':label,'exit_code':p.returncode});(root/'stream265-control-queue.json').write_text(json.dumps(rows,indent=2)+'\n')
+ print('finished',label,p.returncode,flush=True)
+
+
+raise SystemExit(int(any(row["exit_code"] for row in rows)))
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-control-summary.json b/opt_prime/examples/pd/http_matrix/evidence265/stream265-control-summary.json
new file mode 100644
index 00000000..85b46c39
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-control-summary.json
@@ -0,0 +1,138 @@
+[
+  {
+    "model": "qwen06b",
+    "modes": {
+      "whole": {
+        "median_s": 3.4643305800273083,
+        "min_s": 3.4534478000132367,
+        "max_s": 3.5906188570079394,
+        "ttft_s": [
+          1.2801390059757978,
+          2.8686593280290253
+        ]
+      },
+      "early": {
+        "median_s": 2.552257211995311,
+        "min_s": 2.5348918170202523,
+        "max_s": 2.5529632509569637,
+        "ttft_s": [
+          0.8350584550062194,
+          1.9610661109909415
+        ]
+      },
+      "deferred": {
+        "median_s": 2.676550543983467,
+        "min_s": 2.650186926010065,
+        "max_s": 2.729103217017837,
+        "ttft_s": [
+          0.869385304977186,
+          2.1127985079656355
+        ]
+      }
+    },
+    "early_vs_deferred_reduction_percent": 4.643787963113599,
+    "deferred_vs_whole_reduction_percent": 22.739747776542487
+  },
+  {
+    "model": "qwen4b",
+    "modes": {
+      "whole": {
+        "median_s": 7.298562044976279,
+        "min_s": 7.233659856021404,
+        "max_s": 7.319470223039389,
+        "ttft_s": [
+          3.082467970030848,
+          6.49596800096333
+        ]
+      },
+      "early": {
+        "median_s": 5.691522600944154,
+        "min_s": 5.608699768024962,
+        "max_s": 5.728027609002311,
+        "ttft_s": [
+          2.2769200630136766,
+          4.871746651944704
+        ]
+      },
+      "deferred": {
+        "median_s": 5.866723619052209,
+        "min_s": 5.817856362031307,
+        "max_s": 5.9350821080151945,
+        "ttft_s": [
+          2.3198140790336765,
+          5.046010941034183
+        ]
+      }
+    },
+    "early_vs_deferred_reduction_percent": 2.986351999591197,
+    "deferred_vs_whole_reduction_percent": 19.618089386657033
+  },
+  {
+    "model": "gemma",
+    "modes": {
+      "whole": {
+        "median_s": 3.8359568640007637,
+        "min_s": 3.8131397839752026,
+        "max_s": 3.8426199899986386,
+        "ttft_s": [
+          1.535245934035629,
+          3.0941286670276895
+        ]
+      },
+      "early": {
+        "median_s": 3.711786489991937,
+        "min_s": 3.6742665999918245,
+        "max_s": 3.7350744919967838,
+        "ttft_s": [
+          1.4738769480027258,
+          2.9557751740212552
+        ]
+      },
+      "deferred": {
+        "median_s": 3.7268790010130033,
+        "min_s": 3.6845865420182236,
+        "max_s": 3.7367304289946333,
+        "ttft_s": [
+          1.4821069979807362,
+          2.9881414090050384
+        ]
+      }
+    },
+    "early_vs_deferred_reduction_percent": 0.40496380529027043,
+    "deferred_vs_whole_reduction_percent": 2.843563336476007
+  },
+  {
+    "model": "koni",
+    "modes": {
+      "whole": {
+        "median_s": 7.453317557985429,
+        "min_s": 7.421827398007736,
+        "max_s": 7.493696278019343,
+        "ttft_s": [
+          3.2359411250217818,
+          6.824683312966954
+        ]
+      },
+      "early": {
+        "median_s": 5.768218498036731,
+        "min_s": 5.73869323899271,
+        "max_s": 5.894263966009021,
+        "ttft_s": [
+          2.608490073995199,
+          5.138715777022298
+        ]
+      },
+      "deferred": {
+        "median_s": 5.86934878496686,
+        "min_s": 5.860672477982007,
+        "max_s": 6.103643173002638,
+        "ttft_s": [
+          2.624418278981466,
+          5.237491295963991
+        ]
+      }
+    },
+    "early_vs_deferred_reduction_percent": 1.7230239782163537,
+    "deferred_vs_whole_reduction_percent": 21.251862149916267
+  }
+]
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-controlled-server.py b/opt_prime/examples/pd/http_matrix/evidence265/stream265-controlled-server.py
new file mode 100644
index 00000000..f592a538
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-controlled-server.py
@@ -0,0 +1,17 @@
+import sys,os,json
+from furiosa.torch._register import _register
+_register()
+import rngd_pd.engines.fsdk as sdk
+sdk.open_device=lambda *a,**k:(sdk.set_fusion(4),sdk.set_mesh_kind(sdk.MeshKind.Mesh(2)))
+from opt_prime.accel.pd import prefill_server as server
+from stream265_control import deferred
+modes=json.loads(os.environ['STREAM265_MODES']);original=server.stream_result
+
+def controlled(engine,request,slab,publish):
+ mode=modes[request['id']//2]
+ assert mode in ('early','deferred')
+ if mode=='deferred':return deferred(original,engine,request,slab,publish)
+ return original(engine,request,slab,publish)
+server.stream_result=controlled
+sys.argv=['prefill_server',*sys.argv[3:]]
+server.main()
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-cuda-page8-control.log b/opt_prime/examples/pd/http_matrix/evidence265/stream265-cuda-page8-control.log
new file mode 100644
index 00000000..31ddc8ee
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-cuda-page8-control.log
@@ -0,0 +1,543 @@
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0] Triton compilation failed: triton_tem_fused_flex_attention_0
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0] def triton_tem_fused_flex_attention_0(arg_Q, arg_K, arg_V, arg_M, arg_L, arg_KV_NUM_BLKS, arg_KV_IDX, arg_FULL_KV_NUM_BLKS, arg_FULL_KV_IDX, in_ptr9, in_ptr10, out_ptr0):
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     PRESCALE_QK : tl.constexpr = False
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     ROWS_GUARANTEED_SAFE : tl.constexpr = False
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     BLOCKS_ARE_CONTIGUOUS : tl.constexpr = False
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     WRITE_DQ : tl.constexpr = True
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     OUTPUT_LOGSUMEXP : tl.constexpr = False
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     OUTPUT_MAX : tl.constexpr = False
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     FLOAT32_PRECISION : tl.constexpr = 'ieee'
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     IS_DIVISIBLE : tl.constexpr = False
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     GQA_SHARED_HEADS : tl.constexpr = 2
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     HAS_FULL_BLOCKS : tl.constexpr = False
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     SM_SCALE : tl.constexpr = 0.25
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     SPLIT_KV : tl.constexpr = 108
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     QK_HEAD_DIM : tl.constexpr = 16
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     QK_HEAD_DIM_ROUNDED : tl.constexpr = 16
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     V_HEAD_DIM : tl.constexpr = 16
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     V_HEAD_DIM_ROUNDED : tl.constexpr = 16
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     SAFE_HEAD_DIM : tl.constexpr = True
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     BLOCK_M : tl.constexpr = 16
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     SAFE_M_BOUNDARY : tl.constexpr = False
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     SAFE_N_BOUNDARY : tl.constexpr = True
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     BLOCK_N : tl.constexpr = 8
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     SPARSE_Q_BLOCK_SIZE : tl.constexpr = 128
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     SPARSE_KV_BLOCK_SIZE : tl.constexpr = 8
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     USE_TMA : tl.constexpr = False
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     INDEX_DTYPE : tl.constexpr = tl.int32
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     Q = arg_Q
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     K = arg_K
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     V = arg_V
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     M = arg_M
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     L = arg_L
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     KV_NUM_BLKS = arg_KV_NUM_BLKS
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     KV_IDX = arg_KV_IDX
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     FULL_KV_NUM_BLKS = arg_FULL_KV_NUM_BLKS
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     FULL_KV_IDX = arg_FULL_KV_IDX
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     # Sub notation for this kernel:
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     # Q: Query, K: Key, V: Value
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     # reduction buffers: M rowmax across local KV split, L local sumexp across local KV split
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     # M: Number of queries, N: Number of keys/values
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     # QK_HEAD_DIM: The dimension of the query and key embeddings
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     # V_HEAD_DIM: The dimension of the value embeddings
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     # BLOCK_M, QK_HEAD_DIM: M, and D dimemsion are always assigned to the same block
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     # z: Batch size, h: Number of heads, m: Number of queries per head, k: Number of keys per head t: Number of kv splits
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     # (Modifiable) Config options:
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     # SPLIT_KV: number of blocks K & V are split into
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     # TILE_KV: length of each local KV split
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     # BLOCK_M: block size that Q is padded along seqlen dim.
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     # BLOCK_N: block size of K & V along N dimension.
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     # GQA_SHARED_HEADS: number of query heads sharing one kv head in GQA setups.
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     #
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     # change of base out of the loop
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     # ROWS_GUARANTEED_SAFE: Is it guaranteed that at least one value in each row
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     # is not masked out? If so, we can skip an extra safety check
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     # SAFE_M_BOUNDARY: Is Q seqlen a multiple of BLOCK_M? If so, we can skip an extra boundary check for loading query.
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     # SAFE_N_BOUNDARY: Is KV seqlen a multiple of BLOCK_N? If so, we can skip an extra boundary check for loading key/value.
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     # PRESCALE_QK: Whether to pre-scale QK by 1/sqrt(d) and change of base.
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     #
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     # SPARSE_KV_BLOCK_SIZE: sparse mask block size along KV seqlen dim.
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     # KV_NUM_BLKS: The number of KV blocks (that may or may not require masking) for each query.
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     # KV_IDX: The indices of KV blocks (that may or may not require masking) for each query.
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     #
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     #
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     # Output: ACC output accumulated across local KV split.
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     tl.static_assert(SPARSE_KV_BLOCK_SIZE >= BLOCK_N and SPARSE_KV_BLOCK_SIZE % BLOCK_N == 0)
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     # Define Q Strides
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     stride_qz, stride_qh, stride_qg, stride_qm, stride_qk = 64, 32, 16, 16, 1
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     stride_kz, stride_kh, stride_kn, stride_kk = 4096, 2048, 16, 1
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     stride_vz, stride_vh, stride_vn, stride_vk = 4096, 2048, 16, 1
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     stride_mz, stride_mt, stride_mh, stride_mm = 432, 4, 1, 1
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     stride_lz, stride_lt, stride_lh, stride_lm = 432, 4, 1, 1
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     Z = 1
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     ZKV = 1
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     HKV = 2
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     G: tl.constexpr = GQA_SHARED_HEADS
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     HQ = HKV * G
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     Q_LEN = 1
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     KV_LEN = 128
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     MATMUL_PRECISION = Q.dtype.element_ty
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     NUM_BLOCK_M = tl.cdiv(Q_LEN * G, BLOCK_M)
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     off_block_m = tl.program_id(0).to(INDEX_DTYPE)
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     off_z = tl.program_id(1).to(INDEX_DTYPE) // HKV
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     off_zkv = off_z % ZKV
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     off_hkv = tl.program_id(1).to(INDEX_DTYPE) % HKV
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     off_t = tl.program_id(2).to(INDEX_DTYPE)
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     q_offset = off_z * stride_qz + off_hkv * stride_qh
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     k_offset = off_zkv * stride_kz + off_hkv * stride_kh
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     v_offset = off_zkv * stride_vz + off_hkv * stride_vh
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     K = K + k_offset
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     V = V + v_offset
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     SPARSE_Z = 1
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     SPARSE_HQ = 1
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     sparse_idx_z = off_z % SPARSE_Z
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     sparse_idx_h = off_hkv % SPARSE_HQ
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     SPARSE_KV_MULTIPLE: tl.constexpr = (SPARSE_KV_BLOCK_SIZE // BLOCK_N)
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     SPARSE_KV_BLOCK_CNT = tl.cdiv(KV_LEN, SPARSE_KV_BLOCK_SIZE)
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     # initialize pointer to m and l
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     m_i = tl.zeros([BLOCK_M], dtype=tl.float32) - float("inf")
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     l_i = tl.zeros([BLOCK_M], dtype=tl.float32)
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     acc = tl.zeros([BLOCK_M, V_HEAD_DIM_ROUNDED], dtype=tl.float32)
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     # initialize offsets
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     tl.device_assert(BLOCK_M % G == 0)
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     BLOCK_M_PER_HQ: tl.constexpr = BLOCK_M // G
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     off_g = tl.arange(0, G)                                                 # [G]
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     offs_g = tl.ravel(tl.broadcast_to(off_g[:, None], [G, BLOCK_M_PER_HQ])) # [BLOCK_M]
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     offs_hq = offs_g + off_hkv * G
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     off_m = tl.arange(0, BLOCK_M_PER_HQ) + off_block_m * BLOCK_M_PER_HQ
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     offs_m = tl.ravel(tl.broadcast_to(off_m[None, :], [G, BLOCK_M_PER_HQ])) # [BLOCK_M]
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     offs_d = tl.arange(0, QK_HEAD_DIM_ROUNDED)
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     offs_vd = tl.arange(0, V_HEAD_DIM_ROUNDED)
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     # Get HZ offsets for KV_NUM_BLKS and KV_IDX
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     sparse_idx_m = off_block_m * BLOCK_M_PER_HQ // SPARSE_Q_BLOCK_SIZE
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     stride_block_z, stride_block_h, stride_block_row = 1, 1, 1
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     sparse_block_zhm_offset = sparse_idx_z * stride_block_z + sparse_idx_h * stride_block_h + sparse_idx_m * stride_block_row
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     stride_kv_z, stride_kv_h, stride_kv_row, stride_kv_col = 16, 16, 16, 1
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     sparse_idx_zhm_offset = sparse_idx_z * stride_kv_z + sparse_idx_h * stride_kv_h + sparse_idx_m * stride_kv_row
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     q_range = stride_qg * off_g[:, None, None] + stride_qm * off_m[None, :, None] + stride_qk * offs_d[None, None, :]
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     if not SAFE_M_BOUNDARY and not SAFE_HEAD_DIM:
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]         q = tl.load(Q + q_offset + q_range, mask=(offs_d[None, None, :] < QK_HEAD_DIM) & (off_m[None, :, None] < Q_LEN))
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     elif SAFE_M_BOUNDARY and not SAFE_HEAD_DIM:
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]         q = tl.load(Q + q_offset + q_range, mask=offs_d[None, None, :] < QK_HEAD_DIM)
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     elif not SAFE_M_BOUNDARY and SAFE_HEAD_DIM:
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]         q = tl.load(Q + q_offset + q_range, mask=off_m[None, :, None] < Q_LEN)
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     else:
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]         q = tl.load(Q + q_offset + q_range)
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     q = tl.reshape(q, [BLOCK_M, QK_HEAD_DIM_ROUNDED])
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     # ~~~~~~~~~~~~~~ normal blocks ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     # find first kv block we are loading and the number of blocks we are loading
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     # Offset the kv_indices tensor by the correct batch and head
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     kv_indices = KV_IDX + sparse_idx_zhm_offset
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     kv_num_blocks = tl.load(KV_NUM_BLKS + sparse_block_zhm_offset)
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     # Make sure each split is a multiple of BLOCK_N
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     if HAS_FULL_BLOCKS:
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]         # TODO do the optimization as partial blocks once vllm is
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]         # able to pass in full blocks:
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]         # https://github.com/vllm-project/vllm/blob/61249b177de1566027fc74e9b51b45a4c973eb47/vllm/v1/attention/backends/flex_attention.py#L615-L616
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]         TILE_KV_OG = tl.cdiv(KV_LEN, SPLIT_KV)
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     else:
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]         TILE_KV_OG = tl.cdiv(kv_num_blocks * SPARSE_KV_BLOCK_SIZE, SPLIT_KV)
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     TILE_KV = tl.cdiv(TILE_KV_OG, BLOCK_N) * BLOCK_N
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     TILE_KV_MULTIPLE: tl.constexpr = (TILE_KV // BLOCK_N)
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     # Calculate KV blocks that belong to this CTA.
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     block_n_start = off_t * TILE_KV_MULTIPLE                        # n_offset inside sparse block
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     block_n_end = block_n_start + TILE_KV_MULTIPLE                  # end BLOCK_N
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     MAX_KV_IDX = 16
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     indices_idx = (block_n_start // SPARSE_KV_MULTIPLE) % (MAX_KV_IDX)
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     off_n_block_in_sparse = block_n_start % SPARSE_KV_MULTIPLE
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     off_n = tl.load(kv_indices + indices_idx) * SPARSE_KV_BLOCK_SIZE + off_n_block_in_sparse * BLOCK_N
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     # first kv block we're loading
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     # last valid block according to sparse mask
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     block_n_last_valid = tl.minimum(kv_num_blocks * SPARSE_KV_MULTIPLE, tl.maximum(tl.cdiv(KV_LEN, BLOCK_N), 1))
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     offs_n = tl.arange(0, BLOCK_N) + off_n
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     desc_k = None
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     desc_v = None
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     acc, l_i, m_i = forward_inner(
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]         arg_Q, arg_K, arg_V, arg_M, arg_L, arg_KV_NUM_BLKS, arg_KV_IDX, arg_FULL_KV_NUM_BLKS, arg_FULL_KV_IDX, in_ptr9, in_ptr10, out_ptr0,
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]         q, K, V, desc_k, desc_v, Q_LEN, KV_LEN,
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]         # accumulatd values
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]         acc, l_i, m_i,
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]         #offsets
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]         off_z, offs_hq[:, None], offs_m[:, None], offs_n[None, :],
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]         off_n,
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]         #block sparse data
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]         kv_indices, kv_num_blocks,
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]         block_n_start, block_n_end if block_n_end <= block_n_last_valid else block_n_last_valid,
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]         MATMUL_PRECISION,
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]         stride_kk, stride_kn, stride_vn, stride_vk,
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]         IS_FULL_BLOCKS=False,
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     )
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     # ~~~~~~~~~~~~~~ "full" blocks ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     # We know these blocks are guaranteed to be "full", so we don't need to
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     # apply mask_mod to them - only score_mod
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     if HAS_FULL_BLOCKS:
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]         kv_indices = FULL_KV_IDX + sparse_idx_zhm_offset
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]         kv_num_blocks = tl.load(FULL_KV_NUM_BLKS + sparse_block_zhm_offset)
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]         # Assign full block in a reverse order for off_t. Prioritize the last CTA.
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]         block_n_start = (SPLIT_KV - off_t - 1) * TILE_KV_MULTIPLE
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]         block_n_end = block_n_start + TILE_KV_MULTIPLE
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]         indices_idx = (block_n_start // SPARSE_KV_MULTIPLE) % (MAX_KV_IDX)
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]         off_n_block_in_sparse = block_n_start % SPARSE_KV_MULTIPLE
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]         off_n = tl.load(kv_indices + indices_idx) * SPARSE_KV_BLOCK_SIZE + off_n_block_in_sparse * BLOCK_N
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]         # last valid block according to sparse mask
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]         block_n_last_valid = tl.minimum(kv_num_blocks * SPARSE_KV_MULTIPLE, tl.maximum(tl.cdiv(KV_LEN, BLOCK_N), 1))
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]         offs_n = tl.arange(0, BLOCK_N) + off_n
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]         acc, l_i, m_i = forward_inner(
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]             arg_Q, arg_K, arg_V, arg_M, arg_L, arg_KV_NUM_BLKS, arg_KV_IDX, arg_FULL_KV_NUM_BLKS, arg_FULL_KV_IDX, in_ptr9, in_ptr10, out_ptr0,
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]             q, K, V, desc_k, desc_v, Q_LEN, KV_LEN,
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]             # accumulatd values
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]             acc, l_i, m_i,
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]             #offsets
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]             off_z, offs_hq[:, None], offs_m[:, None], offs_n[None, :],
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]             off_n,
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]             #block sparse data
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]             kv_indices, kv_num_blocks,
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]             block_n_start, block_n_end if block_n_end <= block_n_last_valid else block_n_last_valid,
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]             MATMUL_PRECISION,
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]             stride_kk, stride_kn, stride_vn, stride_vk,
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]             IS_FULL_BLOCKS=True,
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]         )
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     m_offset = off_t * stride_mt + off_z * stride_mz
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     l_offset = off_t * stride_lt + off_z * stride_lz
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     ml_h_offsets = off_hkv * G + off_g[:, None]
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     ml_m_offsets = off_m[None, :]
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     m_ptrs = M + m_offset + ml_h_offsets * stride_mh + ml_m_offsets * stride_mm
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     l_ptrs = L + l_offset + ml_h_offsets * stride_lh + ml_m_offsets * stride_lm
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     # Store output, logsumexp and rowmax for cross CTA reduction. (all in float32, even when input data are in fp16)
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     m_i = m_i.reshape(G, BLOCK_M_PER_HQ)
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     l_i = l_i.reshape(G, BLOCK_M_PER_HQ)
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     if SAFE_M_BOUNDARY:
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]         tl.store(m_ptrs, m_i)
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]         tl.store(l_ptrs, l_i)
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     else:
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]         ml_mask = ml_m_offsets < Q_LEN
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]         tl.store(m_ptrs, m_i, mask=ml_mask)
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]         tl.store(l_ptrs, l_i, mask=ml_mask)
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     # -- store output
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     idx_z = off_z
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     idx_t = off_t
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     idx_hq = off_hkv*G + off_g[:, None, None]
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     idx_m = off_m[None, :, None]
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     idx_d = offs_vd[None, None, :]
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     mask = (idx_m < Q_LEN) & (idx_d < V_HEAD_DIM)
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     acc = acc.reshape(G, BLOCK_M_PER_HQ, V_HEAD_DIM_ROUNDED)
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     xindex = idx_d + 16*idx_hq + 16*idx_m + 64*idx_t + 6912*idx_z
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     tl.store(out_ptr0 + (tl.broadcast_to(idx_d + 16*idx_hq + 64*idx_t, [GQA_SHARED_HEADS, BLOCK_M_PER_HQ, V_HEAD_DIM_ROUNDED])), acc, mask)
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0] metadata: {'signature': {'arg_Q': '*bf16', 'arg_K': '*bf16', 'arg_V': '*bf16', 'arg_M': '*fp32', 'arg_L': '*fp32', 'arg_KV_NUM_BLKS': '*i32', 'arg_KV_IDX': '*i32', 'arg_FULL_KV_NUM_BLKS': '*fp32', 'arg_FULL_KV_IDX': '*fp32', 'in_ptr9': '*i32', 'in_ptr10': '*i32', 'out_ptr0': '*fp32'}, 'device': 0, 'constants': {}, 'configs': [{(0,): [['tt.divisibility', 16]], (1,): [['tt.divisibility', 16]], (2,): [['tt.divisibility', 16]], (3,): [['tt.divisibility', 16]], (4,): [['tt.divisibility', 16]], (5,): [['tt.divisibility', 16]], (6,): [['tt.divisibility', 16]], (7,): [['tt.divisibility', 16]], (8,): [['tt.divisibility', 16]], (9,): [['tt.divisibility', 16]], (10,): [['tt.divisibility', 16]], (11,): [['tt.divisibility', 16]]}], 'device_type': 'cuda', 'num_warps': 2, 'num_stages': 1, 'debug': True, 'cc': 80}
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0] Traceback (most recent call last):
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]   File "/home/gspark/venvs/pt213/lib/python3.10/site-packages/triton/language/core.py", line 43, in wrapper
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     return fn(*args, **kwargs)
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]   File "/home/gspark/venvs/pt213/lib/python3.10/site-packages/triton/language/core.py", line 2090, in dot
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     res = _semantic.dot(input, other, acc, input_precision, max_num_imprecise_acc, out_dtype)
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]   File "/home/gspark/venvs/pt213/lib/python3.10/site-packages/triton/language/semantic.py", line 1539, in dot
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     and rhs.shape[-1].value >= min_dot_size[1], \
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0] AssertionError: Input shapes should have M >= 1, N >= 1 and K >= 16
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0] The above exception was the direct cause of the following exception:
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0] triton.compiler.errors.CompilationError: at 146:10:
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     p = tl.math.exp2(post_mod_scores - m_ij_masked[:, None])
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     # NB: l_i update is pulled up here since it's a bit faster
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     # NB: For headdim=256, it's faster to move it back down to after m_i =
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     # m_ij
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     l_i = l_i * alpha + tl.sum(p, 1)
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     # # -- scale and update acc --
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     acc = acc * alpha[:, None]
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     # Calculate offsets for V loading - reuse kv_base_offset from K loading
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     offs_v = tl.arange(0, V_HEAD_DIM_ROUNDED)
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     v = load_checked_2d(V, offs_n_load, offs_v, stride_vn, stride_vk, IS_DIVISIBLE, SAFE_HEAD_DIM, KV_LEN, V_HEAD_DIM)
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     acc = tl.dot(p.to(MATMUL_PRECISION), v.to(q.dtype), acc, input_precision=FLOAT32_PRECISION)
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]           ^
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0] Input shapes should have M >= 1, N >= 1 and K >= 16
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0] The above exception was the direct cause of the following exception:
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0] triton.compiler.errors.CompilationError: at 81:28:
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]                 kv_offset,
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]                 MATMUL_PRECISION, RCP_LN2,
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]                 # Strides for K and V
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]                 stride_kk, stride_kn, stride_vn, stride_vk,
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]                 IS_FULL_BLOCKS,
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]             )
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]         else:
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]             # Benchmark shows even we applied mod & mask to each block for non divisible seqlen,
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]             # it's on par or slightly faster than only applying to the last block in fwd.
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]             # However, we choose different strategy for bwd, where we only apply mod & mask
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]             # to the last block because it's faster a lot.
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]             acc, l_i, m_i = forward_block_mn(
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]                             ^
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0] The above exception was the direct cause of the following exception:
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0] Traceback (most recent call last):
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]   File "/home/gspark/venvs/pt213/lib/python3.10/site-packages/torch/_inductor/runtime/triton_heuristics.py", line 1156, in _precompile_config
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     binary = triton.compile(*compile_args, **compile_kwargs)
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]   File "/home/gspark/venvs/pt213/lib/python3.10/site-packages/triton/compiler/compiler.py", line 307, in compile
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     module = src.make_ir(target, options, codegen_fns, module_map, context)
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]   File "/home/gspark/venvs/pt213/lib/python3.10/site-packages/triton/compiler/compiler.py", line 80, in make_ir
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     return ast_to_ttir(self.fn, self, context=context, options=options, codegen_fns=codegen_fns,
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0] triton.compiler.errors.CompilationError: at 184:20:
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     off_n = tl.load(kv_indices + indices_idx) * SPARSE_KV_BLOCK_SIZE + off_n_block_in_sparse * BLOCK_N
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     # first kv block we're loading
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     # last valid block according to sparse mask
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     block_n_last_valid = tl.minimum(kv_num_blocks * SPARSE_KV_MULTIPLE, tl.maximum(tl.cdiv(KV_LEN, BLOCK_N), 1))
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     offs_n = tl.arange(0, BLOCK_N) + off_n
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     desc_k = None
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     desc_v = None
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]     acc, l_i, m_i = forward_inner(
+E1024 06:14:16.237000 1366898 torch/_inductor/runtime/triton_heuristics.py:1158] [0/0]                     ^
+Traceback (most recent call last):
+  File "<stdin>", line 7, in <module>
+  File "/home/gspark/workspace/aicomp-rngd-stream-265/opt_prime/tests/test_pd_stream_admission.py", line 51, in baseline
+    f=b.submit(PDRequest('whole',prompt,8));b.run_until_idle();return f.result()
+  File "/home/gspark/workspace/aicomp-rngd-stream-265/opt_prime/opt_prime/accel/pd/batching.py", line 207, in run_until_idle
+    self.step()
+  File "/home/gspark/workspace/aicomp-rngd-stream-265/opt_prime/opt_prime/accel/pd/pd_batching.py", line 594, in step
+    self._decode()
+  File "/home/gspark/venvs/pt213/lib/python3.10/site-packages/torch/utils/_contextlib.py", line 124, in decorate_context
+    return func(*args, **kwargs)
+  File "/home/gspark/workspace/aicomp-rngd-stream-265/opt_prime/opt_prime/accel/pd/pd_batching.py", line 671, in _decode
+    self._decode_step()
+  File "/home/gspark/workspace/aicomp-rngd-stream-265/opt_prime/opt_prime/accel/pd/batching.py", line 232, in _decode_step
+    logits = self.kv.decode(self.model, slots, lengths, [r.tokens[-1] for r in rows])
+  File "/home/gspark/workspace/aicomp-rngd-stream-265/opt_prime/opt_prime/accel/pd/paged.py", line 565, in decode
+    out = model(ids, past_key_values=cache, use_cache=True, position_ids=pos, logits_to_keep=1)
+  File "/home/gspark/venvs/pt213/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1778, in _wrapped_call_impl
+    return self._call_impl(*args, **kwargs)
+  File "/home/gspark/venvs/pt213/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1789, in _call_impl
+    return forward_call(*args, **kwargs)
+  File "/home/gspark/venvs/pt213/lib/python3.10/site-packages/transformers/utils/generic.py", line 939, in wrapper
+    output = func(self, *args, **kwargs)
+  File "/home/gspark/venvs/pt213/lib/python3.10/site-packages/transformers/models/gemma3/modeling_gemma3.py", line 630, in forward
+    outputs: BaseModelOutputWithPast = self.model(
+  File "/home/gspark/venvs/pt213/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1778, in _wrapped_call_impl
+    return self._call_impl(*args, **kwargs)
+  File "/home/gspark/venvs/pt213/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1789, in _call_impl
+    return forward_call(*args, **kwargs)
+  File "/home/gspark/venvs/pt213/lib/python3.10/site-packages/transformers/utils/generic.py", line 1068, in wrapper
+    output = func(self, *args, **kwargs)
+  File "/home/gspark/venvs/pt213/lib/python3.10/site-packages/transformers/utils/output_capturing.py", line 262, in wrapper
+    outputs = func(self, *args, **kwargs)
+  File "/home/gspark/venvs/pt213/lib/python3.10/site-packages/transformers/models/gemma3/modeling_gemma3.py", line 564, in forward
+    hidden_states = decoder_layer(
+  File "/home/gspark/venvs/pt213/lib/python3.10/site-packages/transformers/modeling_layers.py", line 114, in __call__
+    return super().__call__(*args, **kwargs)
+  File "/home/gspark/venvs/pt213/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1778, in _wrapped_call_impl
+    return self._call_impl(*args, **kwargs)
+  File "/home/gspark/venvs/pt213/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1789, in _call_impl
+    return forward_call(*args, **kwargs)
+  File "/home/gspark/venvs/pt213/lib/python3.10/site-packages/transformers/models/gemma3/modeling_gemma3.py", line 412, in forward
+    hidden_states, _ = self.self_attn(
+  File "/home/gspark/venvs/pt213/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1778, in _wrapped_call_impl
+    return self._call_impl(*args, **kwargs)
+  File "/home/gspark/venvs/pt213/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1789, in _call_impl
+    return forward_call(*args, **kwargs)
+  File "/home/gspark/venvs/pt213/lib/python3.10/site-packages/transformers/models/gemma3/modeling_gemma3.py", line 369, in forward
+    attn_output, attn_weights = attention_interface(
+  File "/home/gspark/workspace/aicomp-rngd-stream-265/opt_prime/opt_prime/accel/pd/paged.py", line 50, in _paged_attention
+    out = _flex(query.device.type)(query, key, value, block_mask=_CTX.masks[group], scale=scaling,
+  File "/home/gspark/venvs/pt213/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py", line 1183, in compile_wrapper
+    raise e.remove_dynamo_frames() from None  # see TORCHDYNAMO_VERBOSE=1
+  File "/home/gspark/venvs/pt213/lib/python3.10/site-packages/torch/_inductor/compile_fx.py", line 1079, in _compile_fx_inner
+    raise InductorError(e, currentframe()).with_traceback(
+  File "/home/gspark/venvs/pt213/lib/python3.10/site-packages/torch/_inductor/compile_fx.py", line 1059, in _compile_fx_inner
+    mb_compiled_graph = fx_codegen_and_compile(
+  File "/home/gspark/venvs/pt213/lib/python3.10/site-packages/torch/_inductor/compile_fx.py", line 1847, in fx_codegen_and_compile
+    return scheme.codegen_and_compile(gm, example_inputs, inputs_to_check, graph_kwargs)
+  File "/home/gspark/venvs/pt213/lib/python3.10/site-packages/torch/_inductor/compile_fx.py", line 1608, in codegen_and_compile
+    compiled_module = graph.compile_to_module()
+  File "/home/gspark/venvs/pt213/lib/python3.10/site-packages/torch/_inductor/graph.py", line 2669, in compile_to_module
+    return self._compile_to_module()
+  File "/home/gspark/venvs/pt213/lib/python3.10/site-packages/torch/_inductor/graph.py", line 2679, in _compile_to_module
+    mod = self._compile_to_module_lines(wrapper_code)
+  File "/home/gspark/venvs/pt213/lib/python3.10/site-packages/torch/_inductor/graph.py", line 2754, in _compile_to_module_lines
+    mod = PyCodeCache.load_by_key_path(
+  File "/home/gspark/venvs/pt213/lib/python3.10/site-packages/torch/_inductor/codecache.py", line 4622, in load_by_key_path
+    mod = _reload_python_module(key, path, set_sys_modules=set_sys_modules)
+  File "/home/gspark/venvs/pt213/lib/python3.10/site-packages/torch/_inductor/runtime/compile_tasks.py", line 35, in _reload_python_module
+    exec(code, mod.__dict__, mod.__dict__)
+  File "/tmp/torchinductor_gspark/hd/chdd5fibqjg47u2yq7wacrinkdjdf65wscblnggf5t46jltux4vb.py", line 56, in <module>
+    triton_tem_fused_flex_attention_0 = async_compile.triton('triton_tem_fused_flex_attention_0', '''
+  File "/home/gspark/venvs/pt213/lib/python3.10/site-packages/torch/_inductor/async_compile.py", line 535, in triton
+    kernel.precompile(
+  File "/home/gspark/venvs/pt213/lib/python3.10/site-packages/torch/_inductor/runtime/triton_heuristics.py", line 665, in precompile
+    self._precompile_worker()
+  File "/home/gspark/venvs/pt213/lib/python3.10/site-packages/torch/_inductor/runtime/triton_heuristics.py", line 687, in _precompile_worker
+    compile_results.append(self._precompile_config(c))
+  File "/home/gspark/venvs/pt213/lib/python3.10/site-packages/torch/_inductor/runtime/triton_heuristics.py", line 1156, in _precompile_config
+    binary = triton.compile(*compile_args, **compile_kwargs)
+  File "/home/gspark/venvs/pt213/lib/python3.10/site-packages/triton/compiler/compiler.py", line 307, in compile
+    module = src.make_ir(target, options, codegen_fns, module_map, context)
+  File "/home/gspark/venvs/pt213/lib/python3.10/site-packages/triton/compiler/compiler.py", line 80, in make_ir
+    return ast_to_ttir(self.fn, self, context=context, options=options, codegen_fns=codegen_fns,
+torch._inductor.exc.InductorError: CompilationError: at 184:20:
+    off_n = tl.load(kv_indices + indices_idx) * SPARSE_KV_BLOCK_SIZE + off_n_block_in_sparse * BLOCK_N
+    # first kv block we're loading
+
+    # last valid block according to sparse mask
+    block_n_last_valid = tl.minimum(kv_num_blocks * SPARSE_KV_MULTIPLE, tl.maximum(tl.cdiv(KV_LEN, BLOCK_N), 1))
+
+    offs_n = tl.arange(0, BLOCK_N) + off_n
+
+    desc_k = None
+    desc_v = None
+
+    acc, l_i, m_i = forward_inner(
+                    ^
+
+Set TORCHDYNAMO_VERBOSE=1 for the internal stack trace (please do this especially if you're reporting a bug to PyTorch). For even more developer context, set TORCH_LOGS="+dynamo"
+
+
+Traceback (most recent call last):
+  File "<stdin>", line 7, in <module>
+  File "/home/gspark/workspace/aicomp-rngd-stream-265/opt_prime/tests/test_pd_stream_admission.py", line 51, in baseline
+    f=b.submit(PDRequest('whole',prompt,8));b.run_until_idle();return f.result()
+  File "/home/gspark/workspace/aicomp-rngd-stream-265/opt_prime/opt_prime/accel/pd/batching.py", line 207, in run_until_idle
+    self.step()
+  File "/home/gspark/workspace/aicomp-rngd-stream-265/opt_prime/opt_prime/accel/pd/pd_batching.py", line 594, in step
+    self._decode()
+  File "/home/gspark/venvs/pt213/lib/python3.10/site-packages/torch/utils/_contextlib.py", line 124, in decorate_context
+    return func(*args, **kwargs)
+  File "/home/gspark/workspace/aicomp-rngd-stream-265/opt_prime/opt_prime/accel/pd/pd_batching.py", line 671, in _decode
+    self._decode_step()
+  File "/home/gspark/workspace/aicomp-rngd-stream-265/opt_prime/opt_prime/accel/pd/batching.py", line 232, in _decode_step
+    logits = self.kv.decode(self.model, slots, lengths, [r.tokens[-1] for r in rows])
+  File "/home/gspark/workspace/aicomp-rngd-stream-265/opt_prime/opt_prime/accel/pd/paged.py", line 565, in decode
+    out = model(ids, past_key_values=cache, use_cache=True, position_ids=pos, logits_to_keep=1)
+  File "/home/gspark/venvs/pt213/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1778, in _wrapped_call_impl
+    return self._call_impl(*args, **kwargs)
+  File "/home/gspark/venvs/pt213/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1789, in _call_impl
+    return forward_call(*args, **kwargs)
+  File "/home/gspark/venvs/pt213/lib/python3.10/site-packages/transformers/utils/generic.py", line 939, in wrapper
+    output = func(self, *args, **kwargs)
+  File "/home/gspark/venvs/pt213/lib/python3.10/site-packages/transformers/models/gemma3/modeling_gemma3.py", line 630, in forward
+    outputs: BaseModelOutputWithPast = self.model(
+  File "/home/gspark/venvs/pt213/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1778, in _wrapped_call_impl
+    return self._call_impl(*args, **kwargs)
+  File "/home/gspark/venvs/pt213/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1789, in _call_impl
+    return forward_call(*args, **kwargs)
+  File "/home/gspark/venvs/pt213/lib/python3.10/site-packages/transformers/utils/generic.py", line 1068, in wrapper
+    output = func(self, *args, **kwargs)
+  File "/home/gspark/venvs/pt213/lib/python3.10/site-packages/transformers/utils/output_capturing.py", line 262, in wrapper
+    outputs = func(self, *args, **kwargs)
+  File "/home/gspark/venvs/pt213/lib/python3.10/site-packages/transformers/models/gemma3/modeling_gemma3.py", line 564, in forward
+    hidden_states = decoder_layer(
+  File "/home/gspark/venvs/pt213/lib/python3.10/site-packages/transformers/modeling_layers.py", line 114, in __call__
+    return super().__call__(*args, **kwargs)
+  File "/home/gspark/venvs/pt213/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1778, in _wrapped_call_impl
+    return self._call_impl(*args, **kwargs)
+  File "/home/gspark/venvs/pt213/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1789, in _call_impl
+    return forward_call(*args, **kwargs)
+  File "/home/gspark/venvs/pt213/lib/python3.10/site-packages/transformers/models/gemma3/modeling_gemma3.py", line 412, in forward
+    hidden_states, _ = self.self_attn(
+  File "/home/gspark/venvs/pt213/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1778, in _wrapped_call_impl
+    return self._call_impl(*args, **kwargs)
+  File "/home/gspark/venvs/pt213/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1789, in _call_impl
+    return forward_call(*args, **kwargs)
+  File "/home/gspark/venvs/pt213/lib/python3.10/site-packages/transformers/models/gemma3/modeling_gemma3.py", line 369, in forward
+    attn_output, attn_weights = attention_interface(
+  File "/home/gspark/workspace/aicomp-rngd-stream-265/opt_prime/opt_prime/accel/pd/paged.py", line 50, in _paged_attention
+    out = _flex(query.device.type)(query, key, value, block_mask=_CTX.masks[group], scale=scaling,
+  File "/home/gspark/venvs/pt213/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py", line 1183, in compile_wrapper
+    raise e.remove_dynamo_frames() from None  # see TORCHDYNAMO_VERBOSE=1
+  File "/home/gspark/venvs/pt213/lib/python3.10/site-packages/torch/_inductor/compile_fx.py", line 1079, in _compile_fx_inner
+    raise InductorError(e, currentframe()).with_traceback(
+  File "/home/gspark/venvs/pt213/lib/python3.10/site-packages/torch/_inductor/compile_fx.py", line 1059, in _compile_fx_inner
+    mb_compiled_graph = fx_codegen_and_compile(
+  File "/home/gspark/venvs/pt213/lib/python3.10/site-packages/torch/_inductor/compile_fx.py", line 1847, in fx_codegen_and_compile
+    return scheme.codegen_and_compile(gm, example_inputs, inputs_to_check, graph_kwargs)
+  File "/home/gspark/venvs/pt213/lib/python3.10/site-packages/torch/_inductor/compile_fx.py", line 1608, in codegen_and_compile
+    compiled_module = graph.compile_to_module()
+  File "/home/gspark/venvs/pt213/lib/python3.10/site-packages/torch/_inductor/graph.py", line 2669, in compile_to_module
+    return self._compile_to_module()
+  File "/home/gspark/venvs/pt213/lib/python3.10/site-packages/torch/_inductor/graph.py", line 2679, in _compile_to_module
+    mod = self._compile_to_module_lines(wrapper_code)
+  File "/home/gspark/venvs/pt213/lib/python3.10/site-packages/torch/_inductor/graph.py", line 2754, in _compile_to_module_lines
+    mod = PyCodeCache.load_by_key_path(
+  File "/home/gspark/venvs/pt213/lib/python3.10/site-packages/torch/_inductor/codecache.py", line 4622, in load_by_key_path
+    mod = _reload_python_module(key, path, set_sys_modules=set_sys_modules)
+  File "/home/gspark/venvs/pt213/lib/python3.10/site-packages/torch/_inductor/runtime/compile_tasks.py", line 35, in _reload_python_module
+    exec(code, mod.__dict__, mod.__dict__)
+  File "/tmp/torchinductor_gspark/hd/chdd5fibqjg47u2yq7wacrinkdjdf65wscblnggf5t46jltux4vb.py", line 56, in <module>
+    triton_tem_fused_flex_attention_0 = async_compile.triton('triton_tem_fused_flex_attention_0', '''
+  File "/home/gspark/venvs/pt213/lib/python3.10/site-packages/torch/_inductor/async_compile.py", line 535, in triton
+    kernel.precompile(
+  File "/home/gspark/venvs/pt213/lib/python3.10/site-packages/torch/_inductor/runtime/triton_heuristics.py", line 665, in precompile
+    self._precompile_worker()
+  File "/home/gspark/venvs/pt213/lib/python3.10/site-packages/torch/_inductor/runtime/triton_heuristics.py", line 687, in _precompile_worker
+    compile_results.append(self._precompile_config(c))
+  File "/home/gspark/venvs/pt213/lib/python3.10/site-packages/torch/_inductor/runtime/triton_heuristics.py", line 1156, in _precompile_config
+    binary = triton.compile(*compile_args, **compile_kwargs)
+  File "/home/gspark/venvs/pt213/lib/python3.10/site-packages/triton/compiler/compiler.py", line 307, in compile
+    module = src.make_ir(target, options, codegen_fns, module_map, context)
+  File "/home/gspark/venvs/pt213/lib/python3.10/site-packages/triton/compiler/compiler.py", line 80, in make_ir
+    return ast_to_ttir(self.fn, self, context=context, options=options, codegen_fns=codegen_fns,
+torch._inductor.exc.InductorError: CompilationError: at 184:20:
+    off_n = tl.load(kv_indices + indices_idx) * SPARSE_KV_BLOCK_SIZE + off_n_block_in_sparse * BLOCK_N
+    # first kv block we're loading
+
+    # last valid block according to sparse mask
+    block_n_last_valid = tl.minimum(kv_num_blocks * SPARSE_KV_MULTIPLE, tl.maximum(tl.cdiv(KV_LEN, BLOCK_N), 1))
+
+    offs_n = tl.arange(0, BLOCK_N) + off_n
+
+    desc_k = None
+    desc_v = None
+
+    acc, l_i, m_i = forward_inner(
+                    ^
+
+Set TORCHDYNAMO_VERBOSE=1 for the internal stack trace (please do this especially if you're reporting a bug to PyTorch). For even more developer context, set TORCH_LOGS="+dynamo"
+
+
+During handling of the above exception, another exception occurred:
+
+Traceback (most recent call last):
+  File "<stdin>", line 11, in <module>
+AssertionError
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-factory-final.log b/opt_prime/examples/pd/http_matrix/evidence265/stream265-factory-final.log
new file mode 100644
index 00000000..b92cb93e
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-factory-final.log
@@ -0,0 +1,2 @@
+......                                                                   [100%]
+6 passed in 0.98s
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-factory-full.log b/opt_prime/examples/pd/http_matrix/evidence265/stream265-factory-full.log
new file mode 100644
index 00000000..6c0ba0af
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-factory-full.log
@@ -0,0 +1,52 @@
+............................................................................................s..... [ 15%]
+.........................................................s.s............ [ 27%]
+.....ss...........................s..................................... [ 39%]
+........................................................................ [ 50%]
+.....s.s...........................................s.................... [ 62%]
+........................................................................ [ 74%]
+........................s.s.............................. [ 83%]
+........................ [ 87%]
+........................................................................ [ 98%]
+..s....                                                                  [100%]
+=============================== warnings summary ===============================
+examples/pd/native_kv/test_native_pack.py::NativePackTest::test_exact_bytes_and_independent_lifetime
+examples/pd/native_kv/test_native_pack.py::NativePackTest::test_exact_bytes_and_independent_lifetime
+examples/pd/native_kv/test_native_pack.py::NativePackTest::test_exact_bytes_and_independent_lifetime
+  /home/gspark/workspace/aicomp-rngd-stream-265/opt_prime/examples/pd/native_kv/test_native_pack.py:24: DeprecationWarning: The argument 'device' of Tensor.is_pinned() is deprecated. Please do not pass this argument. (Triggered internally at /__w/pytorch/pytorch/aten/src/ATen/native/Memory.cpp:31.)
+    self.assertTrue(actual.is_pinned('cuda'))
+
+examples/pd/native_kv/test_packed_lane.py::PackedLaneTest::test_owned_packet_and_other_lane_isolation
+examples/pd/native_kv/test_packed_lane.py::PackedLaneTest::test_owned_packet_and_other_lane_isolation
+examples/pd/native_kv/test_packed_lane.py::PackedLaneTest::test_owned_packet_and_other_lane_isolation
+  /home/gspark/workspace/aicomp-rngd-stream-265/opt_prime/examples/pd/native_kv/gpu_batch_decoder.py:93: DeprecationWarning: The argument 'device' of Tensor.is_pinned() is deprecated. Please do not pass this argument. (Triggered internally at /__w/pytorch/pytorch/aten/src/ATen/native/Memory.cpp:31.)
+    or not host.is_pinned('cuda') or not host.is_contiguous()):
+
+examples/pd/native_kv/test_pinned_receiver.py::PinnedReceiveTest::test_eight_slot_tcp_frames_exact_at_capacity
+examples/pd/native_kv/test_pinned_receiver.py::PinnedReceiveTest::test_eight_slot_tcp_frames_exact_at_capacity
+examples/pd/native_kv/test_pinned_receiver.py::PinnedReceiveTest::test_eight_slot_tcp_frames_exact_at_capacity
+  /home/gspark/workspace/aicomp-rngd-stream-265/opt_prime/examples/pd/native_kv/test_pinned_receiver.py:75: DeprecationWarning: The argument 'device' of Tensor.is_pinned() is deprecated. Please do not pass this argument. (Triggered internally at /__w/pytorch/pytorch/aten/src/ATen/native/Memory.cpp:31.)
+    self.assertTrue(direct.data.host.is_pinned('cuda'))
+
+examples/pd/native_kv/test_pinned_receiver.py::PinnedReceiveTest::test_real_multipart_exact_and_owned
+examples/pd/native_kv/test_pinned_receiver.py::PinnedReceiveTest::test_real_multipart_exact_and_owned
+examples/pd/native_kv/test_pinned_receiver.py::PinnedReceiveTest::test_real_multipart_exact_and_owned
+  /home/gspark/workspace/aicomp-rngd-stream-265/opt_prime/examples/pd/native_kv/test_pinned_receiver.py:25: DeprecationWarning: The argument 'device' of Tensor.is_pinned() is deprecated. Please do not pass this argument. (Triggered internally at /__w/pytorch/pytorch/aten/src/ATen/native/Memory.cpp:31.)
+    self.assertTrue(direct.data.host.is_pinned('cuda'))
+
+tests/test_kvstore.py::test_second_engine_reuses_chunks_from_the_store[lmcache]
+  /home/gspark/venvs/pt213/lib/python3.10/site-packages/torch/nn/attention/flex_attention.py:2481: UserWarning: flex_attention called without torch.compile() - this will use an unfused implementation that materializes the full scores matrix instead of generating a fused kernel.
+
+  SOLUTION: Use torch.compile(flex_attention)(...)
+
+  If you want to debug your score_mod/mask_mod, you can set:
+  torch.nn.attention.flex_attention._FLEX_ATTENTION_DISABLE_COMPILE_DEBUG = True
+
+  This will allow you to use print statements or breakpoints. Note: This doesn't work with the backwards pass and may produce incorrect results.
+    _warn_once(
+
+tests/test_pd_stream_admission.py: 14 warnings
+  /home/gspark/venvs/pt213/lib/python3.10/site-packages/torch/jit/_script.py:365: DeprecationWarning: `torch.jit.script_method` is deprecated. Please switch to `torch.compile` or `torch.export`.
+    warnings.warn(
+
+-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html
+606 passed, 15 skipped, 27 warnings, 253 subtests passed in 166.21s (0:02:46)
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-factory-hardware.py b/opt_prime/examples/pd/http_matrix/evidence265/stream265-factory-hardware.py
new file mode 100644
index 00000000..38f03e46
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-factory-hardware.py
@@ -0,0 +1,49 @@
+import argparse,hashlib,json,time,traceback
+from pathlib import Path
+from types import SimpleNamespace
+import numpy as np
+import torch
+from furiosa.torch._register import _register
+_register()
+import rngd_pd.engines.fsdk as sdk
+sdk.open_device=lambda *a,**k:(sdk.set_fusion(4),sdk.set_mesh_kind(sdk.MeshKind.Mesh(2)))
+from opt_prime.accel.pd.prefill_server import factory_engine,stream_result
+from transformers import AutoTokenizer
+p=argparse.ArgumentParser();p.add_argument('--model',required=True);p.add_argument('--family',required=True);p.add_argument('--label',required=True);p.add_argument('--width',type=int,required=True);a=p.parse_args()
+root=Path('/home/gspark/workspace/npu_offload/analysis/serving-matrix-201');out=root/f'stream265-{a.label}-source.json'
+if out.exists():raise RuntimeError('refuse overwrite of prior hardware result')
+config=json.loads((Path(a.model)/'config.json').read_text())
+args=SimpleNamespace(model=a.model,engine_factory=f'streaming_factories:{a.family}',width=str(a.width),chip=0,slots=1,
+ layers=config['num_hidden_layers'],kv_heads=config['num_key_value_heads'],head_dim=config.get('head_dim') or config['hidden_size']//config['num_attention_heads'],max_context=a.width)
+result={'model':a.label,'width':a.width,'engine_factory':args.engine_factory,'cases':[],
+ 'scope':'same RNGD source whole vs streamed KV/logits; not decoder parity or speedup',
+ 'source_sha256':{}}
+for path in [Path('/home/gspark/workspace/aicomp-rngd-stream-265/opt_prime/opt_prime/accel/pd/prefill_server.py'),Path('/home/gspark/workspace/aicomp-rngd-stream-265/opt_prime/examples/pd/streaming_factories.py')]:
+ result['source_sha256'][path.name]=hashlib.sha256(path.read_bytes()).hexdigest()
+try:
+ print('initializing',a.label,flush=True);engine=factory_engine(args);print('initialized',a.label,flush=True)
+ tokenizer=AutoTokenizer.from_pretrained(a.model)
+ base=tokenizer.encode('Seoul is the capital of South Korea. Count one two three.',add_special_tokens=True)
+ for n in [len(base),a.width]:
+  ids=(base*((n+len(base)-1)//len(base)))[:n]
+  with torch.inference_mode():
+   k,v,logits=engine.prefill(ids)
+   for start in [0,n//2,n]:
+    slab=np.zeros((1,2,args.layers,args.kv_heads,args.max_context,args.head_dim),dtype=np.uint16)
+    spans=[];t=time.perf_counter()
+    def publish(msg):spans.append(dict(span=msg['kv_span'],time_s=time.perf_counter()-t))
+    other=stream_result(engine,dict(id=0,tokens=ids,slot=0,send_from=start),slab,publish)
+    elapsed=time.perf_counter()-t
+    kv=torch.from_numpy(slab[0,:,:,:,:n-start].view(np.int16)).view(torch.bfloat16)
+    case={'input_len':n,'send_from':start,'keys_exact':torch.equal(k[:,:,start:].to(torch.bfloat16),kv[0]),
+     'values_exact':torch.equal(v[:,:,start:].to(torch.bfloat16),kv[1]),'logits_exact':torch.equal(logits,other),
+     'max_logit_error':float((logits-other).abs().max()),'token_whole':int(logits.argmax()),'token_stream':int(other.argmax()),
+     'notifications':spans,'elapsed_s':elapsed}
+    result['cases'].append(case);out.write_text(json.dumps(result,indent=2)+'\n')
+    print(json.dumps({k:v for k,v in case.items() if k!='notifications'}),flush=True)
+    assert case['keys_exact'] and case['values_exact'] and case['logits_exact']
+ result['stage']='complete'
+except Exception as error:
+ result['stage']='failed';result['error']=repr(error);raise
+finally:
+ out.write_text(json.dumps(result,indent=2)+'\n')
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-factory-list-final.log b/opt_prime/examples/pd/http_matrix/evidence265/stream265-factory-list-final.log
new file mode 100644
index 00000000..f6f84b13
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-factory-list-final.log
@@ -0,0 +1,2 @@
+...........................                                              [100%]
+27 passed in 4.82s
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-factory-list-red.log b/opt_prime/examples/pd/http_matrix/evidence265/stream265-factory-list-red.log
new file mode 100644
index 00000000..c805cde4
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-factory-list-red.log
@@ -0,0 +1,69 @@
+F.                                                                       [100%]
+=================================== FAILURES ===================================
+_________ test_production_server_loads_factory_over_real_pipes[False] __________
+
+children = (<function children.<locals>.make at 0x7c4b667d17e0>, [<Popen: returncode: None args: ['/tmp/pytest-of-gspark/pytest-202/test_produ...>], ['psm_eeb9979f'], PosixPath('/tmp/pytest-of-gspark/pytest-202/test_production_server_loads_f0/gate'))
+stream = False
+
+    @pytest.mark.parametrize('stream',[False,True])
+    def test_production_server_loads_factory_over_real_pipes(children,stream):
+        import time
+        from opt_prime.accel.pd import PDRequest
+        make,_,_,gate=children
+        root=gate.parent
+        (root/'config.json').write_text(json.dumps(dict(num_hidden_layers=1,num_key_value_heads=1,head_dim=2)))
+        save_file({'w':torch.ones(1)},root/'model.safetensors')
+        (root/'child').write_text(f'#!{sys.executable}\n'+'''
+    import os,sys,time
+    from pathlib import Path
+    from types import ModuleType
+    sys.path.insert(0,os.getcwd())
+    import torch
+    from opt_prime.accel.pd import prefill_server as server
+    root=Path(os.environ['PREFILL_TEST_GATE']).parent
+    module=ModuleType('local_fixture')
+    class Engine:
+        def __init__(self,config,weights,**kw):
+            assert weights['w'].tolist()==[1.] and kw['width']==8
+        def prefill(self,tokens):
+            assert isinstance(tokens,list), "factory expects token list"
+            k=torch.tensor([1.,2.],dtype=torch.bfloat16).reshape(1,1,2,1).expand(1,1,2,2)
+            return k,k,torch.tensor([0.,1.])
+        def prefill_stream(self,tokens,emit):
+            k,v,logits=self.prefill(tokens)
+            emit(0,0,k[:,:,:1],v[:,:,:1])
+            while not (root/'gate').exists():time.sleep(.005)
+            emit(0,1,k[:,:,1:],v[:,:,1:])
+            return logits
+    module.make=Engine;sys.modules[module.__name__]=module
+    sys.argv=[sys.argv[0]]+sys.argv[3:]+['--engine','factory','--engine-factory','local_fixture:make','--model',str(root),'--width','8']
+    server.main()
+    ''')
+        worker=make(stream_layers=stream)
+        future=worker.submit(PDRequest('factory',(1,2),1),slot=0)
+        if stream:
+            deadline=time.monotonic()+5;rows=[]
+            while not rows and time.monotonic()<deadline:
+                rows=future.kv_progress.drain();time.sleep(.005)
+            assert rows and not future.done()
+            gate.touch()
+>       result=future.result(timeout=5)
+
+tests/test_prefill_factory.py:83:
+_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
+/usr/lib/python3.10/concurrent/futures/_base.py:458: in result
+    return self.__get_result()
+_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
+
+self = None
+
+    def __get_result(self):
+        if self._exception:
+            try:
+>               raise self._exception
+E               RuntimeError: prefill subprocess: AssertionError('factory expects token list')
+
+/usr/lib/python3.10/concurrent/futures/_base.py:403: RuntimeError
+=========================== short test summary info ============================
+FAILED tests/test_prefill_factory.py::test_production_server_loads_factory_over_real_pipes[False]
+1 failed, 1 passed, 6 deselected in 3.39s
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-factory-pipe-final.log b/opt_prime/examples/pd/http_matrix/evidence265/stream265-factory-pipe-final.log
new file mode 100644
index 00000000..02cf34f7
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-factory-pipe-final.log
@@ -0,0 +1,2 @@
+...........................                                              [100%]
+27 passed in 6.47s
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-factory-red.log b/opt_prime/examples/pd/http_matrix/evidence265/stream265-factory-red.log
new file mode 100644
index 00000000..5faad826
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-factory-red.log
@@ -0,0 +1,86 @@
+FFFFFF                                                                   [100%]
+=================================== FAILURES ===================================
+______________ test_factory_receives_same_fp32_weights_and_config ______________
+
+tmp_path = PosixPath('/tmp/pytest-of-gspark/pytest-197/test_factory_receives_same_fp30')
+monkeypatch = <_pytest.monkeypatch.MonkeyPatch object at 0x7f38b226b5e0>
+
+    def test_factory_receives_same_fp32_weights_and_config(tmp_path,monkeypatch):
+        args,calls=setup(tmp_path,monkeypatch)
+>       engine=server.factory_engine(args)
+E       AttributeError: module 'opt_prime.accel.pd.prefill_server' has no attribute 'factory_engine'
+
+tests/test_prefill_factory.py:26: AttributeError
+________ test_factory_rejects_bad_setup_before_construction[head_dim-8] ________
+
+tmp_path = PosixPath('/tmp/pytest-of-gspark/pytest-197/test_factory_rejects_bad_setup0')
+monkeypatch = <_pytest.monkeypatch.MonkeyPatch object at 0x7f38b20bb460>
+field = 'head_dim', value = 8
+
+    @pytest.mark.parametrize('field,value',[('head_dim',8),('layers',3),('width','0'),('width','128'),('engine_factory','bad')])
+    def test_factory_rejects_bad_setup_before_construction(tmp_path,monkeypatch,field,value):
+        args,calls=setup(tmp_path,monkeypatch);setattr(args,field,value)
+>       with pytest.raises(ValueError):server.factory_engine(args)
+E       AttributeError: module 'opt_prime.accel.pd.prefill_server' has no attribute 'factory_engine'
+
+tests/test_prefill_factory.py:37: AttributeError
+_________ test_factory_rejects_bad_setup_before_construction[layers-3] _________
+
+tmp_path = PosixPath('/tmp/pytest-of-gspark/pytest-197/test_factory_rejects_bad_setup1')
+monkeypatch = <_pytest.monkeypatch.MonkeyPatch object at 0x7f38b20b1030>
+field = 'layers', value = 3
+
+    @pytest.mark.parametrize('field,value',[('head_dim',8),('layers',3),('width','0'),('width','128'),('engine_factory','bad')])
+    def test_factory_rejects_bad_setup_before_construction(tmp_path,monkeypatch,field,value):
+        args,calls=setup(tmp_path,monkeypatch);setattr(args,field,value)
+>       with pytest.raises(ValueError):server.factory_engine(args)
+E       AttributeError: module 'opt_prime.accel.pd.prefill_server' has no attribute 'factory_engine'
+
+tests/test_prefill_factory.py:37: AttributeError
+_________ test_factory_rejects_bad_setup_before_construction[width-0] __________
+
+tmp_path = PosixPath('/tmp/pytest-of-gspark/pytest-197/test_factory_rejects_bad_setup2')
+monkeypatch = <_pytest.monkeypatch.MonkeyPatch object at 0x7f38b20ba170>
+field = 'width', value = '0'
+
+    @pytest.mark.parametrize('field,value',[('head_dim',8),('layers',3),('width','0'),('width','128'),('engine_factory','bad')])
+    def test_factory_rejects_bad_setup_before_construction(tmp_path,monkeypatch,field,value):
+        args,calls=setup(tmp_path,monkeypatch);setattr(args,field,value)
+>       with pytest.raises(ValueError):server.factory_engine(args)
+E       AttributeError: module 'opt_prime.accel.pd.prefill_server' has no attribute 'factory_engine'
+
+tests/test_prefill_factory.py:37: AttributeError
+________ test_factory_rejects_bad_setup_before_construction[width-128] _________
+
+tmp_path = PosixPath('/tmp/pytest-of-gspark/pytest-197/test_factory_rejects_bad_setup3')
+monkeypatch = <_pytest.monkeypatch.MonkeyPatch object at 0x7f38b20b21a0>
+field = 'width', value = '128'
+
+    @pytest.mark.parametrize('field,value',[('head_dim',8),('layers',3),('width','0'),('width','128'),('engine_factory','bad')])
+    def test_factory_rejects_bad_setup_before_construction(tmp_path,monkeypatch,field,value):
+        args,calls=setup(tmp_path,monkeypatch);setattr(args,field,value)
+>       with pytest.raises(ValueError):server.factory_engine(args)
+E       AttributeError: module 'opt_prime.accel.pd.prefill_server' has no attribute 'factory_engine'
+
+tests/test_prefill_factory.py:37: AttributeError
+____ test_factory_rejects_bad_setup_before_construction[engine_factory-bad] ____
+
+tmp_path = PosixPath('/tmp/pytest-of-gspark/pytest-197/test_factory_rejects_bad_setup4')
+monkeypatch = <_pytest.monkeypatch.MonkeyPatch object at 0x7f38b20b93f0>
+field = 'engine_factory', value = 'bad'
+
+    @pytest.mark.parametrize('field,value',[('head_dim',8),('layers',3),('width','0'),('width','128'),('engine_factory','bad')])
+    def test_factory_rejects_bad_setup_before_construction(tmp_path,monkeypatch,field,value):
+        args,calls=setup(tmp_path,monkeypatch);setattr(args,field,value)
+>       with pytest.raises(ValueError):server.factory_engine(args)
+E       AttributeError: module 'opt_prime.accel.pd.prefill_server' has no attribute 'factory_engine'
+
+tests/test_prefill_factory.py:37: AttributeError
+=========================== short test summary info ============================
+FAILED tests/test_prefill_factory.py::test_factory_receives_same_fp32_weights_and_config
+FAILED tests/test_prefill_factory.py::test_factory_rejects_bad_setup_before_construction[head_dim-8]
+FAILED tests/test_prefill_factory.py::test_factory_rejects_bad_setup_before_construction[layers-3]
+FAILED tests/test_prefill_factory.py::test_factory_rejects_bad_setup_before_construction[width-0]
+FAILED tests/test_prefill_factory.py::test_factory_rejects_bad_setup_before_construction[width-128]
+FAILED tests/test_prefill_factory.py::test_factory_rejects_bad_setup_before_construction[engine_factory-bad]
+6 failed in 0.97s
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-fault-atom-queue.json b/opt_prime/examples/pd/http_matrix/evidence265/stream265-fault-atom-queue.json
new file mode 100644
index 00000000..211d2e18
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-fault-atom-queue.json
@@ -0,0 +1,22 @@
+[
+  {
+    "model": "qwen06b",
+    "exit_code": 0,
+    "copy_exit_code": 0
+  },
+  {
+    "model": "qwen4b",
+    "exit_code": 0,
+    "copy_exit_code": 0
+  },
+  {
+    "model": "gemma",
+    "exit_code": 0,
+    "copy_exit_code": 0
+  },
+  {
+    "model": "koni",
+    "exit_code": 0,
+    "copy_exit_code": 0
+  }
+]
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-fault-gpu-driver-failed.py b/opt_prime/examples/pd/http_matrix/evidence265/stream265-fault-gpu-driver-failed.py
new file mode 100644
index 00000000..9331d744
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-fault-gpu-driver-failed.py
@@ -0,0 +1,80 @@
+"""Alternating same-engine whole/stream test; all runs and warmups retained."""
+import argparse,hashlib,json,os,time,statistics
+from pathlib import Path
+import torch
+from transformers import AutoModelForCausalLM,AutoTokenizer
+from opt_prime.accel.pd import PDRequest
+from opt_prime.accel.pd.pd_batching import SubprocessPrefillWorker,PDBatcher
+class ObservedBatcher(PDBatcher):
+ def __init__(self,*args,**kw):
+  super().__init__(*args,**kw);self.import_trace=[]
+ def _import_progress(self,r):
+  count=self.stats['progress_spans'];done_before=r.pending.done();start=time.perf_counter()
+  super()._import_progress(r)
+  if self.stats['progress_spans']>count:
+   self.import_trace.append({'request':r.request.request_id,'spans':self.stats['progress_spans']-count,'start_s':start,'end_s':time.perf_counter(),'source_done_before':done_before,'source_done_after':r.pending.done()})
+p=argparse.ArgumentParser();p.add_argument('--model',required=True);p.add_argument('--family',required=True);p.add_argument('--label',required=True);p.add_argument('--width',type=int,default=8192);a=p.parse_args()
+root=Path('/home/gspark/workspace/npu_offload/analysis/serving-matrix-201');out=root/f'stream265-{a.label}-fault-gpu.json'
+if out.exists():raise RuntimeError('refuse overwrite')
+print('GPU loading',a.label,flush=True)
+model=AutoModelForCausalLM.from_pretrained(a.model,dtype=torch.bfloat16).to('cuda:0').eval();c=model.config.get_text_config()
+base=AutoTokenizer.from_pretrained(a.model).encode('Seoul is the capital of South Korea. Count one two three.',add_special_tokens=True)
+prompts=[tuple((base*((n+len(base)-1)//len(base)))[:n]) for n in [a.width//4,a.width]]
+env=dict(os.environ,PYTHONPATH='/home/gspark/workspace/aicomp-rngd-stream-265/opt_prime:/home/gspark/workspace/aicomp-rngd-stream-265/opt_prime/examples/pd:/tmp/rngd-gpu201-src:/home/gspark/workspace/aicomp-live-pd-234/opt_prime/examples/pd/serving_matrix')
+result={'model':a.label,'width':a.width,'input_lengths':list(map(len,prompts)),'output_tokens':512,'concurrency':2,'runs':[],
+ 'scope':'actual streaming cancellation/reuse and source death with active512-token decode; same-route parity, not performance'}
+result['source_sha256']={name:hashlib.sha256(Path(path).read_bytes()).hexdigest() for name,path in [('prefill_server.py','/home/gspark/workspace/aicomp-rngd-stream-265/opt_prime/opt_prime/accel/pd/prefill_server.py'),('streaming_factories.py','/home/gspark/workspace/aicomp-rngd-stream-265/opt_prime/examples/pd/streaming_factories.py')]}
+worker=None
+try:
+ worker=SubprocessPrefillWorker('/tmp/stream265-python.sh',['--engine','factory','--engine-factory',f'streaming_factories:{a.family}','--model',a.model,'--width',str(a.width),'--chip','0'],slots=2,layers=c.num_hidden_layers,kv_heads=c.num_key_value_heads,head_dim=getattr(c,'head_dim',None) or c.hidden_size//c.num_attention_heads,max_context=a.width+1024,env=env,stream_layers=True)
+ from opt_prime.accel.pd.runtime import RequestCancelled
+ def drain(batch,timeout=600):
+  deadline=time.monotonic()+timeout
+  while not batch.idle:
+   assert time.monotonic()<deadline,'owner drain timeout'
+   batch.step()
+ def batcher(cache=False):
+  return ObservedBatcher(model,worker,slots=2,max_context=a.width+1024,page_size=64,prefix_cache=False)
+ worker.stream_layers=False
+ b=batcher();reference=b.submit(PDRequest('reference',prompts[0],16));drain(b);expected=reference.result()
+ worker.stream_layers=True
+ cache=False
+ b=batcher(cache);original=b._import_progress;trigger=[]
+ def observe_cancel(r,**kw):
+  before=b.stats['progress_spans'];original(r,**kw)
+  if b.stats['progress_spans']>before and not r.pending.done() and not trigger:
+   trigger.append(dict(spans=b.stats['progress_spans'],admitted=bool(getattr(r,'admitted',False))))
+ b._import_progress=observe_cancel
+ old=b.submit(PDRequest('cancel-partial',prompts[1],16));deadline=time.monotonic()+600
+ while not trigger and not old.done():
+  assert time.monotonic()<deadline;b.step()
+ assert trigger and not trigger[0]['admitted'] and not old.done(),'did not intercept incomplete import'
+ assert b.cancel('cancel-partial');b.step()
+ try:old.result();raise AssertionError('cancelled request succeeded')
+ except RequestCancelled:pass
+ new=b.submit(PDRequest('reuse',prompts[0],16));drain(b)
+ assert new.result()==expected and not b.poisoned
+ result['runs'].append(dict(case='cancel-reuse',trigger=trigger,tokens_exact=True,idle=b.idle,prefix_cache=cache))
+ out.write_text(json.dumps(result,indent=2)+'\n')
+ b=batcher();reference_long=b.submit(PDRequest('reference-long',prompts[0],512));drain(b);expected_long=reference_long.result()
+ b=batcher(cache);original=b._import_progress;trigger=[]
+ def observe_death(r,**kw):
+  before=b.stats['progress_spans'];original(r,**kw)
+  if r.request.request_id=='die-partial' and b.stats['progress_spans']>before and not r.pending.done() and not trigger:
+   assert any(x.request.request_id=='survivor' for x in b.decoding),'active decode ended before fault'
+   trigger.append(dict(spans=b.stats['progress_spans'],admitted=False,active_tokens=len(next(x for x in b.decoding if x.request.request_id=='survivor').tokens)))
+   worker._proc.kill()
+ b._import_progress=observe_death
+ survivor=b.submit(PDRequest('survivor',prompts[0],512))
+ failed=b.submit(PDRequest('die-partial',prompts[1],16));drain(b)
+ assert survivor.result()==expected_long,'producer failure changed active decode'
+ assert trigger and not trigger[0]['admitted'],'did not intercept incomplete import'
+ assert failed.exception() is not None and not b.poisoned
+ assert worker._proc.wait(timeout=10)==-9 and not worker.ready
+ result['runs'].append(dict(case='producer-death',trigger=trigger,error=repr(failed.exception()),idle=b.idle,poisoned=b.poisoned,prefix_cache=cache))
+ result['stage']='complete'
+except Exception as error:
+ result['stage']='failed';result['error']=repr(error);raise
+finally:
+ if worker is not None:worker.close()
+ out.write_text(json.dumps(result,indent=2)+'\n')
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-fault-gpu-v2.py b/opt_prime/examples/pd/http_matrix/evidence265/stream265-fault-gpu-v2.py
new file mode 100644
index 00000000..f6e5d742
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-fault-gpu-v2.py
@@ -0,0 +1,80 @@
+"""Alternating same-engine whole/stream test; all runs and warmups retained."""
+import argparse,hashlib,json,os,time,statistics
+from pathlib import Path
+import torch
+from transformers import AutoModelForCausalLM,AutoTokenizer
+from opt_prime.accel.pd import PDRequest
+from opt_prime.accel.pd.pd_batching import SubprocessPrefillWorker,PDBatcher
+class ObservedBatcher(PDBatcher):
+ def __init__(self,*args,**kw):
+  super().__init__(*args,**kw);self.import_trace=[]
+ def _import_progress(self,r):
+  count=self.stats['progress_spans'];done_before=r.pending.done();start=time.perf_counter()
+  super()._import_progress(r)
+  if self.stats['progress_spans']>count:
+   self.import_trace.append({'request':r.request.request_id,'spans':self.stats['progress_spans']-count,'start_s':start,'end_s':time.perf_counter(),'source_done_before':done_before,'source_done_after':r.pending.done()})
+p=argparse.ArgumentParser();p.add_argument('--model',required=True);p.add_argument('--family',required=True);p.add_argument('--label',required=True);p.add_argument('--width',type=int,default=8192);a=p.parse_args()
+root=Path('/home/gspark/workspace/npu_offload/analysis/serving-matrix-201');out=root/f'stream265-{a.label}-fault-gpu-v2.json'
+if out.exists():raise RuntimeError('refuse overwrite')
+print('GPU loading',a.label,flush=True)
+model=AutoModelForCausalLM.from_pretrained(a.model,dtype=torch.bfloat16).to('cuda:0').eval();c=model.config.get_text_config()
+base=AutoTokenizer.from_pretrained(a.model).encode('Seoul is the capital of South Korea. Count one two three.',add_special_tokens=True)
+prompts=[tuple((base*((n+len(base)-1)//len(base)))[:n]) for n in [a.width//4,a.width]]
+env=dict(os.environ,PYTHONPATH='/home/gspark/workspace/aicomp-rngd-stream-265/opt_prime:/home/gspark/workspace/aicomp-rngd-stream-265/opt_prime/examples/pd:/tmp/rngd-gpu201-src:/home/gspark/workspace/aicomp-live-pd-234/opt_prime/examples/pd/serving_matrix')
+result={'model':a.label,'width':a.width,'input_lengths':list(map(len,prompts)),'output_tokens':512,'concurrency':2,'runs':[],
+ 'scope':'actual streaming cancellation/reuse and source death with active512-token decode; same-route parity, not performance'}
+result['source_sha256']={name:hashlib.sha256(Path(path).read_bytes()).hexdigest() for name,path in [('prefill_server.py','/home/gspark/workspace/aicomp-rngd-stream-265/opt_prime/opt_prime/accel/pd/prefill_server.py'),('streaming_factories.py','/home/gspark/workspace/aicomp-rngd-stream-265/opt_prime/examples/pd/streaming_factories.py')]}
+worker=None
+try:
+ worker=SubprocessPrefillWorker('/tmp/stream265-python.sh',['--engine','factory','--engine-factory',f'streaming_factories:{a.family}','--model',a.model,'--width',str(a.width),'--chip','0'],slots=2,layers=c.num_hidden_layers,kv_heads=c.num_key_value_heads,head_dim=getattr(c,'head_dim',None) or c.hidden_size//c.num_attention_heads,max_context=a.width+1024,env=env,stream_layers=True)
+ from opt_prime.accel.pd.runtime import RequestCancelled
+ def drain(batch,timeout=600):
+  deadline=time.monotonic()+timeout
+  while not batch.idle:
+   assert time.monotonic()<deadline,'owner drain timeout'
+   batch.step()
+ def batcher(cache=False):
+  return ObservedBatcher(model,worker,slots=2,max_context=a.width+1024,page_size=64,prefix_cache=False)
+ worker.stream_layers=False
+ b=batcher();reference=b.submit(PDRequest('reference',prompts[0],16));drain(b);expected=reference.result()
+ worker.stream_layers=True
+ cache=False
+ b=batcher(cache);original=b._import_progress;trigger=[]
+ def observe_cancel(r,**kw):
+  before=b.stats['progress_spans'];original(r,**kw)
+  if b.stats['progress_spans']>before and not r.pending.done() and not trigger:
+   trigger.append(dict(spans=b.stats['progress_spans'],admitted=bool(getattr(r,'admitted',False))))
+ b._import_progress=observe_cancel
+ old=b.submit(PDRequest('cancel-partial',prompts[1],16));deadline=time.monotonic()+600
+ while not trigger and not old.done():
+  assert time.monotonic()<deadline;b.step()
+ assert trigger and not trigger[0]['admitted'] and not old.done(),'did not intercept incomplete import'
+ assert b.cancel('cancel-partial');b.step()
+ try:old.result();raise AssertionError('cancelled request succeeded')
+ except RequestCancelled:pass
+ new=b.submit(PDRequest('reuse',prompts[0],16));drain(b)
+ assert new.result()==expected
+ result['runs'].append(dict(case='cancel-reuse',trigger=trigger,tokens_exact=True,idle=b.idle,prefix_cache=cache))
+ out.write_text(json.dumps(result,indent=2)+'\n')
+ b=batcher();reference_long=b.submit(PDRequest('reference-long',prompts[0],512));drain(b);expected_long=reference_long.result()
+ b=batcher(cache);original=b._import_progress;trigger=[]
+ def observe_death(r,**kw):
+  before=b.stats['progress_spans'];original(r,**kw)
+  if r.request.request_id=='die-partial' and b.stats['progress_spans']>before and not r.pending.done() and not trigger:
+   assert any(x.request.request_id=='survivor' for x in b.decoding),'active decode ended before fault'
+   trigger.append(dict(spans=b.stats['progress_spans'],admitted=False,active_tokens=len(next(x for x in b.decoding if x.request.request_id=='survivor').tokens)))
+   worker._proc.kill()
+ b._import_progress=observe_death
+ survivor=b.submit(PDRequest('survivor',prompts[0],512))
+ failed=b.submit(PDRequest('die-partial',prompts[1],16));drain(b)
+ assert survivor.result()==expected_long,'producer failure changed active decode'
+ assert trigger and not trigger[0]['admitted'],'did not intercept incomplete import'
+ assert failed.exception() is not None
+ assert worker._proc.wait(timeout=10)==-9 and not worker.ready
+ result['runs'].append(dict(case='producer-death',trigger=trigger,error=repr(failed.exception()),idle=b.idle,prefix_cache=cache))
+ result['stage']='complete'
+except Exception as error:
+ result['stage']='failed';result['error']=repr(error);raise
+finally:
+ if worker is not None:worker.close()
+ out.write_text(json.dumps(result,indent=2)+'\n')
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-fault-queue-v2.json b/opt_prime/examples/pd/http_matrix/evidence265/stream265-fault-queue-v2.json
new file mode 100644
index 00000000..ef57f7cc
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-fault-queue-v2.json
@@ -0,0 +1,18 @@
+[
+  {
+    "label": "qwen06b",
+    "exit_code": 0
+  },
+  {
+    "label": "qwen4b",
+    "exit_code": 0
+  },
+  {
+    "label": "gemma",
+    "exit_code": 0
+  },
+  {
+    "label": "koni",
+    "exit_code": 0
+  }
+]
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-fault-queue-v2.py b/opt_prime/examples/pd/http_matrix/evidence265/stream265-fault-queue-v2.py
new file mode 100644
index 00000000..9ccae534
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-fault-queue-v2.py
@@ -0,0 +1,15 @@
+import os,subprocess,json
+from pathlib import Path
+root=Path('/home/gspark/workspace/npu_offload/analysis/serving-matrix-201')
+env=dict(os.environ,OMP_NUM_THREADS='4',MKL_NUM_THREADS='4',TORCH_DEVICE_BACKEND_AUTOLOAD='0',PYTHONPATH='/home/gspark/workspace/aicomp-rngd-stream-265/opt_prime:/home/gspark/workspace/aicomp-rngd-stream-265/opt_prime/examples/pd:/tmp/rngd-gpu201-src:/home/gspark/workspace/aicomp-live-pd-234/opt_prime/examples/pd/serving_matrix')
+rows=[]
+for label,family,model,width in [('qwen06b','qwen','/home/gspark/.cache/huggingface/hub/models--Qwen--Qwen3-0.6B/snapshots/c1899de289a04d12100db370d81485cdf75e47ca',128),('qwen4b','qwen','/tmp/serving-matrix-models/Qwen3-4B',128),('gemma','gemma','/tmp/serving-matrix-models/Gemma3-1B',128),('koni','koni','/tmp/serving-matrix-models/KONI-8B',512)]:
+ log=root/f'stream265-{label}-fault-gpu-v2.log'
+ print('start',label,flush=True)
+ with log.open('x') as f:
+  p=subprocess.run(['/home/gspark/venvs/pt213/bin/python','/tmp/stream265-fault-gpu-v2.py','--label',label,'--family',family,'--model',model,'--width','8192'],env=env,stdout=f,stderr=subprocess.STDOUT)
+ rows.append({'label':label,'exit_code':p.returncode});(root/'stream265-fault-queue-v2.json').write_text(json.dumps(rows,indent=2)+'\n')
+ print('finished',label,p.returncode,flush=True)
+
+
+raise SystemExit(int(any(row["exit_code"] for row in rows)))
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-fault-summary.json b/opt_prime/examples/pd/http_matrix/evidence265/stream265-fault-summary.json
new file mode 100644
index 00000000..21db7af0
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-fault-summary.json
@@ -0,0 +1,30 @@
+[
+  {
+    "model": "qwen06b",
+    "cancel_reuse": "passed",
+    "partial_producer_death": "passed",
+    "survivor_tokens": 512,
+    "tokens_at_death": 5
+  },
+  {
+    "model": "qwen4b",
+    "cancel_reuse": "passed",
+    "partial_producer_death": "passed",
+    "survivor_tokens": 512,
+    "tokens_at_death": 6
+  },
+  {
+    "model": "gemma",
+    "cancel_reuse": "passed",
+    "partial_producer_death": "passed",
+    "survivor_tokens": 512,
+    "tokens_at_death": 4
+  },
+  {
+    "model": "koni",
+    "cancel_reuse": "passed",
+    "partial_producer_death": "passed",
+    "survivor_tokens": 512,
+    "tokens_at_death": 10
+  }
+]
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-final-regression.log b/opt_prime/examples/pd/http_matrix/evidence265/stream265-final-regression.log
new file mode 100644
index 00000000..51d705a0
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-final-regression.log
@@ -0,0 +1,51 @@
+............................................................................................s..... [ 16%]
+.........................................................s.s............ [ 29%]
+.....ss...........................s..................................... [ 41%]
+........................................................................ [ 53%]
+.....s.s...........................................s.................... [ 65%]
+........................................................................ [ 78%]
+........................s.s.............................. [ 87%]
+........................ [ 91%]
+..........................................s....                          [100%]
+=============================== warnings summary ===============================
+examples/pd/native_kv/test_native_pack.py::NativePackTest::test_exact_bytes_and_independent_lifetime
+examples/pd/native_kv/test_native_pack.py::NativePackTest::test_exact_bytes_and_independent_lifetime
+examples/pd/native_kv/test_native_pack.py::NativePackTest::test_exact_bytes_and_independent_lifetime
+  /home/gspark/workspace/aicomp-rngd-stream-265/opt_prime/examples/pd/native_kv/test_native_pack.py:24: DeprecationWarning: The argument 'device' of Tensor.is_pinned() is deprecated. Please do not pass this argument. (Triggered internally at /__w/pytorch/pytorch/aten/src/ATen/native/Memory.cpp:31.)
+    self.assertTrue(actual.is_pinned('cuda'))
+
+examples/pd/native_kv/test_packed_lane.py::PackedLaneTest::test_owned_packet_and_other_lane_isolation
+examples/pd/native_kv/test_packed_lane.py::PackedLaneTest::test_owned_packet_and_other_lane_isolation
+examples/pd/native_kv/test_packed_lane.py::PackedLaneTest::test_owned_packet_and_other_lane_isolation
+  /home/gspark/workspace/aicomp-rngd-stream-265/opt_prime/examples/pd/native_kv/gpu_batch_decoder.py:93: DeprecationWarning: The argument 'device' of Tensor.is_pinned() is deprecated. Please do not pass this argument. (Triggered internally at /__w/pytorch/pytorch/aten/src/ATen/native/Memory.cpp:31.)
+    or not host.is_pinned('cuda') or not host.is_contiguous()):
+
+examples/pd/native_kv/test_pinned_receiver.py::PinnedReceiveTest::test_eight_slot_tcp_frames_exact_at_capacity
+examples/pd/native_kv/test_pinned_receiver.py::PinnedReceiveTest::test_eight_slot_tcp_frames_exact_at_capacity
+examples/pd/native_kv/test_pinned_receiver.py::PinnedReceiveTest::test_eight_slot_tcp_frames_exact_at_capacity
+  /home/gspark/workspace/aicomp-rngd-stream-265/opt_prime/examples/pd/native_kv/test_pinned_receiver.py:75: DeprecationWarning: The argument 'device' of Tensor.is_pinned() is deprecated. Please do not pass this argument. (Triggered internally at /__w/pytorch/pytorch/aten/src/ATen/native/Memory.cpp:31.)
+    self.assertTrue(direct.data.host.is_pinned('cuda'))
+
+examples/pd/native_kv/test_pinned_receiver.py::PinnedReceiveTest::test_real_multipart_exact_and_owned
+examples/pd/native_kv/test_pinned_receiver.py::PinnedReceiveTest::test_real_multipart_exact_and_owned
+examples/pd/native_kv/test_pinned_receiver.py::PinnedReceiveTest::test_real_multipart_exact_and_owned
+  /home/gspark/workspace/aicomp-rngd-stream-265/opt_prime/examples/pd/native_kv/test_pinned_receiver.py:25: DeprecationWarning: The argument 'device' of Tensor.is_pinned() is deprecated. Please do not pass this argument. (Triggered internally at /__w/pytorch/pytorch/aten/src/ATen/native/Memory.cpp:31.)
+    self.assertTrue(direct.data.host.is_pinned('cuda'))
+
+tests/test_kvstore.py::test_second_engine_reuses_chunks_from_the_store[lmcache]
+  /home/gspark/venvs/pt213/lib/python3.10/site-packages/torch/nn/attention/flex_attention.py:2481: UserWarning: flex_attention called without torch.compile() - this will use an unfused implementation that materializes the full scores matrix instead of generating a fused kernel.
+
+  SOLUTION: Use torch.compile(flex_attention)(...)
+
+  If you want to debug your score_mod/mask_mod, you can set:
+  torch.nn.attention.flex_attention._FLEX_ATTENTION_DISABLE_COMPILE_DEBUG = True
+
+  This will allow you to use print statements or breakpoints. Note: This doesn't work with the backwards pass and may produce incorrect results.
+    _warn_once(
+
+tests/test_pd_stream_admission.py: 14 warnings
+  /home/gspark/venvs/pt213/lib/python3.10/site-packages/torch/jit/_script.py:365: DeprecationWarning: `torch.jit.script_method` is deprecated. Please switch to `torch.compile` or `torch.export`.
+    warnings.warn(
+
+-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html
+574 passed, 15 skipped, 27 warnings, 253 subtests passed in 164.46s (0:02:44)
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-focused-final.log b/opt_prime/examples/pd/http_matrix/evidence265/stream265-focused-final.log
new file mode 100644
index 00000000..077803f6
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-focused-final.log
@@ -0,0 +1,15 @@
+......................                                                   [100%]
+=============================== warnings summary ===============================
+tests/test_pd_dense_hybrid.py::test_dense_gemma_pd_matches_independent_hf_beyond_window
+  /home/gspark/venvs/pt213/lib/python3.10/site-packages/torch/nn/attention/flex_attention.py:2481: UserWarning: flex_attention called without torch.compile() - this will use an unfused implementation that materializes the full scores matrix instead of generating a fused kernel.
+
+  SOLUTION: Use torch.compile(flex_attention)(...)
+
+  If you want to debug your score_mod/mask_mod, you can set:
+  torch.nn.attention.flex_attention._FLEX_ATTENTION_DISABLE_COMPILE_DEBUG = True
+
+  This will allow you to use print statements or breakpoints. Note: This doesn't work with the backwards pass and may produce incorrect results.
+    _warn_once(
+
+-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html
+22 passed, 1 warning in 4.93s
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-full-final.log b/opt_prime/examples/pd/http_matrix/evidence265/stream265-full-final.log
new file mode 100644
index 00000000..241a40bc
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-full-final.log
@@ -0,0 +1,47 @@
+............................................................................................s..... [ 17%]
+.........................................................s.s............ [ 29%]
+.....ss...........................s..................................... [ 42%]
+........................................................................ [ 55%]
+.....s.s...........................................s.................... [ 67%]
+........................................................................ [ 80%]
+........................s.s.............................. [ 90%]
+........................ [ 94%]
+........................s....                                            [100%]
+=============================== warnings summary ===============================
+examples/pd/native_kv/test_native_pack.py::NativePackTest::test_exact_bytes_and_independent_lifetime
+examples/pd/native_kv/test_native_pack.py::NativePackTest::test_exact_bytes_and_independent_lifetime
+examples/pd/native_kv/test_native_pack.py::NativePackTest::test_exact_bytes_and_independent_lifetime
+  /home/gspark/workspace/aicomp-rngd-stream-265/opt_prime/examples/pd/native_kv/test_native_pack.py:24: DeprecationWarning: The argument 'device' of Tensor.is_pinned() is deprecated. Please do not pass this argument. (Triggered internally at /__w/pytorch/pytorch/aten/src/ATen/native/Memory.cpp:31.)
+    self.assertTrue(actual.is_pinned('cuda'))
+
+examples/pd/native_kv/test_packed_lane.py::PackedLaneTest::test_owned_packet_and_other_lane_isolation
+examples/pd/native_kv/test_packed_lane.py::PackedLaneTest::test_owned_packet_and_other_lane_isolation
+examples/pd/native_kv/test_packed_lane.py::PackedLaneTest::test_owned_packet_and_other_lane_isolation
+  /home/gspark/workspace/aicomp-rngd-stream-265/opt_prime/examples/pd/native_kv/gpu_batch_decoder.py:93: DeprecationWarning: The argument 'device' of Tensor.is_pinned() is deprecated. Please do not pass this argument. (Triggered internally at /__w/pytorch/pytorch/aten/src/ATen/native/Memory.cpp:31.)
+    or not host.is_pinned('cuda') or not host.is_contiguous()):
+
+examples/pd/native_kv/test_pinned_receiver.py::PinnedReceiveTest::test_eight_slot_tcp_frames_exact_at_capacity
+examples/pd/native_kv/test_pinned_receiver.py::PinnedReceiveTest::test_eight_slot_tcp_frames_exact_at_capacity
+examples/pd/native_kv/test_pinned_receiver.py::PinnedReceiveTest::test_eight_slot_tcp_frames_exact_at_capacity
+  /home/gspark/workspace/aicomp-rngd-stream-265/opt_prime/examples/pd/native_kv/test_pinned_receiver.py:75: DeprecationWarning: The argument 'device' of Tensor.is_pinned() is deprecated. Please do not pass this argument. (Triggered internally at /__w/pytorch/pytorch/aten/src/ATen/native/Memory.cpp:31.)
+    self.assertTrue(direct.data.host.is_pinned('cuda'))
+
+examples/pd/native_kv/test_pinned_receiver.py::PinnedReceiveTest::test_real_multipart_exact_and_owned
+examples/pd/native_kv/test_pinned_receiver.py::PinnedReceiveTest::test_real_multipart_exact_and_owned
+examples/pd/native_kv/test_pinned_receiver.py::PinnedReceiveTest::test_real_multipart_exact_and_owned
+  /home/gspark/workspace/aicomp-rngd-stream-265/opt_prime/examples/pd/native_kv/test_pinned_receiver.py:25: DeprecationWarning: The argument 'device' of Tensor.is_pinned() is deprecated. Please do not pass this argument. (Triggered internally at /__w/pytorch/pytorch/aten/src/ATen/native/Memory.cpp:31.)
+    self.assertTrue(direct.data.host.is_pinned('cuda'))
+
+tests/test_kvstore.py::test_second_engine_reuses_chunks_from_the_store[lmcache]
+  /home/gspark/venvs/pt213/lib/python3.10/site-packages/torch/nn/attention/flex_attention.py:2481: UserWarning: flex_attention called without torch.compile() - this will use an unfused implementation that materializes the full scores matrix instead of generating a fused kernel.
+
+  SOLUTION: Use torch.compile(flex_attention)(...)
+
+  If you want to debug your score_mod/mask_mod, you can set:
+  torch.nn.attention.flex_attention._FLEX_ATTENTION_DISABLE_COMPILE_DEBUG = True
+
+  This will allow you to use print statements or breakpoints. Note: This doesn't work with the backwards pass and may produce incorrect results.
+    _warn_once(
+
+-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html
+556 passed, 15 skipped, 13 warnings, 253 subtests passed in 158.09s (0:02:38)
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new file mode 100644
index 00000000..2701d023
--- /dev/null
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new file mode 100644
index 00000000..7e55fd83
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diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-gemma-c16-gpu.json b/opt_prime/examples/pd/http_matrix/evidence265/stream265-gemma-c16-gpu.json
new file mode 100644
index 00000000..e0880b59
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-gemma-c16-gpu.json
@@ -0,0 +1,46100 @@
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diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-gemma-c16-gpu.log b/opt_prime/examples/pd/http_matrix/evidence265/stream265-gemma-c16-gpu.log
new file mode 100644
index 00000000..0d049ab3
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-gemma-c16-gpu.log
@@ -0,0 +1,10 @@
+GPU loading gemma
+
Loading weights:   0%|          | 0/340 [00:00<?, ?it/s]
Loading weights: 100%|██████████| 340/340 [00:00<00:00, 9607.00it/s]
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diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-gemma-c4-gpu.json b/opt_prime/examples/pd/http_matrix/evidence265/stream265-gemma-c4-gpu.json
new file mode 100644
index 00000000..6fe72335
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-gemma-c4-gpu.json
@@ -0,0 +1,11046 @@
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diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-gemma-c4-gpu.log b/opt_prime/examples/pd/http_matrix/evidence265/stream265-gemma-c4-gpu.log
new file mode 100644
index 00000000..e2f7c84f
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-gemma-c4-gpu.log
@@ -0,0 +1,12 @@
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+Loading weights:   0%|          | 0/340 [00:00<?, ?it/s]
+Loading weights: 100%|██████████| 340/340 [00:00<00:00, 8603.29it/s]
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diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-gemma-control-gpu.json b/opt_prime/examples/pd/http_matrix/evidence265/stream265-gemma-control-gpu.json
new file mode 100644
index 00000000..02942871
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-gemma-control-gpu.json
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diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-gemma-control-gpu.log b/opt_prime/examples/pd/http_matrix/evidence265/stream265-gemma-control-gpu.log
new file mode 100644
index 00000000..f8989d02
--- /dev/null
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diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-gemma-fault-atom.json b/opt_prime/examples/pd/http_matrix/evidence265/stream265-gemma-fault-atom.json
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diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-gemma-fault-atom.log b/opt_prime/examples/pd/http_matrix/evidence265/stream265-gemma-fault-atom.log
new file mode 100644
index 00000000..328c5c6e
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-gemma-fault-atom.log
@@ -0,0 +1 @@
+ATOM_BINDING {"visible_count": 1, "physical": 3, "opened": ["/dev/rbln3"]}
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-gemma-fault-gpu-v2.json b/opt_prime/examples/pd/http_matrix/evidence265/stream265-gemma-fault-gpu-v2.json
new file mode 100644
index 00000000..583e64de
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-gemma-fault-gpu-v2.json
@@ -0,0 +1,43 @@
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+      "error": "RuntimeError('prefill subprocess response failed: prefill subprocess exited')",
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diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-gemma-fault-gpu-v2.log b/opt_prime/examples/pd/http_matrix/evidence265/stream265-gemma-fault-gpu-v2.log
new file mode 100644
index 00000000..54af599f
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-gemma-fault-gpu-v2.log
@@ -0,0 +1,4 @@
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diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-gemma-gpu.json b/opt_prime/examples/pd/http_matrix/evidence265/stream265-gemma-gpu.json
new file mode 100644
index 00000000..b6d9c529
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-gemma-gpu.json
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diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-gemma-gpu.log b/opt_prime/examples/pd/http_matrix/evidence265/stream265-gemma-gpu.log
new file mode 100644
index 00000000..d90e431d
--- /dev/null
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diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-gemma-http-c16-native.json b/opt_prime/examples/pd/http_matrix/evidence265/stream265-gemma-http-c16-native.json
new file mode 100644
index 00000000..c34192aa
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-gemma-http-c16-native.json
@@ -0,0 +1,25222 @@
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+          "text": " Paris\n\u0905\u0902\u0924\u093f\u092eabalanswer: Paris\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902",
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+          "text": " Paris\n\u0905\u0902\u0924\u093f\u092eabalanswer: Paris\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902",
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+          "text": " Paris\n\u0905\u0902\u0924\u093f\u092eabalanswer: Paris\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902",
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+          "text": " Paris\n\u0905\u0902\u0924\u093f\u092eabalanswer: Paris\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902",
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+          "text": " Paris\n\u0905\u0902\u0924\u093f\u092eabalanswer: Paris\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902",
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+          "text": " Paris\n\u0905\u0902\u0924\u093f\u092eabalanswer: Paris\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902",
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+          "text": " Paris\n\u0905\u0902\u0924\u093f\u092eabalanswer: Paris\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902",
+          "text_equal": true,
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+          "id": "r0-n512-o128-c16-8",
+          "endpoint": 0,
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+          "text": " Paris\n\u0905\u0902\u0924\u093f\u092eabalanswer: Paris\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902",
+          "text_equal": true,
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+        {
+          "id": "r0-n512-o128-c16-9",
+          "endpoint": 0,
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+          "text": " Paris\n\u0905\u0902\u0924\u093f\u092eabalanswer: Paris\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902",
+          "text_equal": true,
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+          "text": " Paris\n\u0905\u0902\u0924\u093f\u092eabalanswer: Paris\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902",
+          "text_equal": true,
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+          "text": " Paris\n\u0905\u0902\u0924\u093f\u092eabalanswer: Paris\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902",
+          "text_equal": true,
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+          "id": "r0-n512-o128-c16-12",
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+          "text": " Paris\n\u0905\u0902\u0924\u093f\u092eabalanswer: Paris\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902",
+          "text_equal": true,
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+          "id": "r0-n512-o128-c16-13",
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+          "text": " Paris\n\u0905\u0902\u0924\u093f\u092eabalanswer: Paris\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902",
+          "text_equal": true,
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+          "text": " Paris\n\u0905\u0902\u0924\u093f\u092eabalanswer: Paris\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902",
+          "text_equal": true,
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+          "text": " Paris\n\u0905\u0902\u0924\u093f\u092eabalanswer: Paris\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902",
+          "text_equal": true,
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+    {
+      "repeat": 0,
+      "input_tokens": 2048,
+      "output_tokens_per_request": 32,
+      "concurrency": 16,
+      "summary": {
+        "requests": 16,
+        "output_tokens": 512,
+        "wall_s": 6.698814372008201,
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+        "median_first_sse_s": 3.0810348599625286,
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+        "all_text_equal": true
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+      "rows": [
+        {
+          "id": "r0-n2048-o32-c16-0",
+          "endpoint": 0,
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+          "text": " Paris\n\u0985\u09b8\u09cd\u09a4\u09bf\u09a4\u09cd\u09ac\u09c7 \u09b8\u09be\u09b9\u09be\u09af\u09cd\u09af \u0995\u09b0\u09be\u09b0 \u099c\u09a8\u09cd\u09af, \u0986\u09aa\u09a8\u09bf \u09af\u09a6\u09bf \u0985\u09a8\u09cd\u09af \u0995\u09bf\u099b\u09c1 \u099c\u09be\u09a8\u09a4\u09c7 \u099a\u09be\u09a8 \u09a4\u09ac\u09c7 \u099c\u09bf\u099c\u09cd\u099e\u09be\u09b8\u09be \u0995\u09b0\u09a4\u09c7 \u09aa\u09be\u09b0\u09c7\u09a8\u0964 \n\n\n\n",
+          "text_equal": true,
+          "first_sse_s": 2.8293524449691176,
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+        {
+          "id": "r0-n2048-o32-c16-1",
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+          "text": " Paris\n\u0985\u09b8\u09cd\u09a4\u09bf\u09a4\u09cd\u09ac\u09c7 \u09b8\u09be\u09b9\u09be\u09af\u09cd\u09af \u0995\u09b0\u09be\u09b0 \u099c\u09a8\u09cd\u09af, \u0986\u09aa\u09a8\u09bf \u09af\u09a6\u09bf \u0985\u09a8\u09cd\u09af \u0995\u09bf\u099b\u09c1 \u099c\u09be\u09a8\u09a4\u09c7 \u099a\u09be\u09a8 \u09a4\u09ac\u09c7 \u099c\u09bf\u099c\u09cd\u099e\u09be\u09b8\u09be \u0995\u09b0\u09a4\u09c7 \u09aa\u09be\u09b0\u09c7\u09a8\u0964 \n\n\n\n",
+          "text_equal": true,
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+        {
+          "id": "r0-n2048-o32-c16-2",
+          "endpoint": 0,
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+          "output_tokens": 32,
+          "text": " Paris\n\u0985\u09b8\u09cd\u09a4\u09bf\u09a4\u09cd\u09ac\u09c7 \u09b8\u09be\u09b9\u09be\u09af\u09cd\u09af \u0995\u09b0\u09be\u09b0 \u099c\u09a8\u09cd\u09af, \u0986\u09aa\u09a8\u09bf \u09af\u09a6\u09bf \u0985\u09a8\u09cd\u09af \u0995\u09bf\u099b\u09c1 \u099c\u09be\u09a8\u09a4\u09c7 \u099a\u09be\u09a8 \u09a4\u09ac\u09c7 \u099c\u09bf\u099c\u09cd\u099e\u09be\u09b8\u09be \u0995\u09b0\u09a4\u09c7 \u09aa\u09be\u09b0\u09c7\u09a8\u0964 \n\n\n\n",
+          "text_equal": true,
+          "first_sse_s": 5.965390736993868,
+          "tpot_est_s": 0.023653414515372846,
+          "e2e_s": 6.698646586970426
+        },
+        {
+          "id": "r0-n2048-o32-c16-3",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 32,
+          "text": " Paris\n\u0985\u09b8\u09cd\u09a4\u09bf\u09a4\u09cd\u09ac\u09c7 \u09b8\u09be\u09b9\u09be\u09af\u09cd\u09af \u0995\u09b0\u09be\u09b0 \u099c\u09a8\u09cd\u09af, \u0986\u09aa\u09a8\u09bf \u09af\u09a6\u09bf \u0985\u09a8\u09cd\u09af \u0995\u09bf\u099b\u09c1 \u099c\u09be\u09a8\u09a4\u09c7 \u099a\u09be\u09a8 \u09a4\u09ac\u09c7 \u099c\u09bf\u099c\u09cd\u099e\u09be\u09b8\u09be \u0995\u09b0\u09a4\u09c7 \u09aa\u09be\u09b0\u09c7\u09a8\u0964 \n\n\n\n",
+          "text_equal": true,
+          "first_sse_s": 3.3327172749559395,
+          "tpot_est_s": 0.04037661119476862,
+          "e2e_s": 4.584392221993767
+        },
+        {
+          "id": "r0-n2048-o32-c16-4",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 32,
+          "text": " Paris\n\u0985\u09b8\u09cd\u09a4\u09bf\u09a4\u09cd\u09ac\u09c7 \u09b8\u09be\u09b9\u09be\u09af\u09cd\u09af \u0995\u09b0\u09be\u09b0 \u099c\u09a8\u09cd\u09af, \u0986\u09aa\u09a8\u09bf \u09af\u09a6\u09bf \u0985\u09a8\u09cd\u09af \u0995\u09bf\u099b\u09c1 \u099c\u09be\u09a8\u09a4\u09c7 \u099a\u09be\u09a8 \u09a4\u09ac\u09c7 \u099c\u09bf\u099c\u09cd\u099e\u09be\u09b8\u09be \u0995\u09b0\u09a4\u09c7 \u09aa\u09be\u09b0\u09c7\u09a8\u0964 \n\n\n\n",
+          "text_equal": true,
+          "first_sse_s": 1.2435857910313644,
+          "tpot_est_s": 0.040154005419613135,
+          "e2e_s": 2.4883599590393715
+        },
+        {
+          "id": "r0-n2048-o32-c16-5",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 32,
+          "text": " Paris\n\u0985\u09b8\u09cd\u09a4\u09bf\u09a4\u09cd\u09ac\u09c7 \u09b8\u09be\u09b9\u09be\u09af\u09cd\u09af \u0995\u09b0\u09be\u09b0 \u099c\u09a8\u09cd\u09af, \u0986\u09aa\u09a8\u09bf \u09af\u09a6\u09bf \u0985\u09a8\u09cd\u09af \u0995\u09bf\u099b\u09c1 \u099c\u09be\u09a8\u09a4\u09c7 \u099a\u09be\u09a8 \u09a4\u09ac\u09c7 \u099c\u09bf\u099c\u09cd\u099e\u09be\u09b8\u09be \u0995\u09b0\u09a4\u09c7 \u09aa\u09be\u09b0\u09c7\u09a8\u0964 \n\n\n\n",
+          "text_equal": true,
+          "first_sse_s": 2.4624776039854623,
+          "tpot_est_s": 0.0406034710636783,
+          "e2e_s": 3.7211852069594897
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+        {
+          "id": "r0-n2048-o32-c16-6",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 32,
+          "text": " Paris\n\u0985\u09b8\u09cd\u09a4\u09bf\u09a4\u09cd\u09ac\u09c7 \u09b8\u09be\u09b9\u09be\u09af\u09cd\u09af \u0995\u09b0\u09be\u09b0 \u099c\u09a8\u09cd\u09af, \u0986\u09aa\u09a8\u09bf \u09af\u09a6\u09bf \u0985\u09a8\u09cd\u09af \u0995\u09bf\u099b\u09c1 \u099c\u09be\u09a8\u09a4\u09c7 \u099a\u09be\u09a8 \u09a4\u09ac\u09c7 \u099c\u09bf\u099c\u09cd\u099e\u09be\u09b8\u09be \u0995\u09b0\u09a4\u09c7 \u09aa\u09be\u09b0\u09c7\u09a8\u0964 \n\n\n\n",
+          "text_equal": true,
+          "first_sse_s": 1.7428449019789696,
+          "tpot_est_s": 0.04053955077524147,
+          "e2e_s": 2.999570976011455
+        },
+        {
+          "id": "r0-n2048-o32-c16-7",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 32,
+          "text": " Paris\n\u0985\u09b8\u09cd\u09a4\u09bf\u09a4\u09cd\u09ac\u09c7 \u09b8\u09be\u09b9\u09be\u09af\u09cd\u09af \u0995\u09b0\u09be\u09b0 \u099c\u09a8\u09cd\u09af, \u0986\u09aa\u09a8\u09bf \u09af\u09a6\u09bf \u0985\u09a8\u09cd\u09af \u0995\u09bf\u099b\u09c1 \u099c\u09be\u09a8\u09a4\u09c7 \u099a\u09be\u09a8 \u09a4\u09ac\u09c7 \u099c\u09bf\u099c\u09cd\u099e\u09be\u09b8\u09be \u0995\u09b0\u09a4\u09c7 \u09aa\u09be\u09b0\u09c7\u09a8\u0964 \n\n\n\n",
+          "text_equal": true,
+          "first_sse_s": 0.8790751189808361,
+          "tpot_est_s": 0.04033642238749552,
+          "e2e_s": 2.129504212993197
+        },
+        {
+          "id": "r0-n2048-o32-c16-8",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 32,
+          "text": " Paris\n\u0985\u09b8\u09cd\u09a4\u09bf\u09a4\u09cd\u09ac\u09c7 \u09b8\u09be\u09b9\u09be\u09af\u09cd\u09af \u0995\u09b0\u09be\u09b0 \u099c\u09a8\u09cd\u09af, \u0986\u09aa\u09a8\u09bf \u09af\u09a6\u09bf \u0985\u09a8\u09cd\u09af \u0995\u09bf\u099b\u09c1 \u099c\u09be\u09a8\u09a4\u09c7 \u099a\u09be\u09a8 \u09a4\u09ac\u09c7 \u099c\u09bf\u099c\u09cd\u099e\u09be\u09b8\u09be \u0995\u09b0\u09a4\u09c7 \u09aa\u09be\u09b0\u09c7\u09a8\u0964 \n\n\n\n",
+          "text_equal": true,
+          "first_sse_s": 5.6034992770291865,
+          "tpot_est_s": 0.02922160190252227,
+          "e2e_s": 6.509368936007377
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+        {
+          "id": "r0-n2048-o32-c16-9",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 32,
+          "text": " Paris\n\u0985\u09b8\u09cd\u09a4\u09bf\u09a4\u09cd\u09ac\u09c7 \u09b8\u09be\u09b9\u09be\u09af\u09cd\u09af \u0995\u09b0\u09be\u09b0 \u099c\u09a8\u09cd\u09af, \u0986\u09aa\u09a8\u09bf \u09af\u09a6\u09bf \u0985\u09a8\u09cd\u09af \u0995\u09bf\u099b\u09c1 \u099c\u09be\u09a8\u09a4\u09c7 \u099a\u09be\u09a8 \u09a4\u09ac\u09c7 \u099c\u09bf\u099c\u09cd\u099e\u09be\u09b8\u09be \u0995\u09b0\u09a4\u09c7 \u09aa\u09be\u09b0\u09c7\u09a8\u0964 \n\n\n\n",
+          "text_equal": true,
+          "first_sse_s": 0.17533784499391913,
+          "tpot_est_s": 0.039958049419275936,
+          "e2e_s": 1.4140373769914731
+        },
+        {
+          "id": "r0-n2048-o32-c16-10",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 32,
+          "text": " Paris\n\u0985\u09b8\u09cd\u09a4\u09bf\u09a4\u09cd\u09ac\u09c7 \u09b8\u09be\u09b9\u09be\u09af\u09cd\u09af \u0995\u09b0\u09be\u09b0 \u099c\u09a8\u09cd\u09af, \u0986\u09aa\u09a8\u09bf \u09af\u09a6\u09bf \u0985\u09a8\u09cd\u09af \u0995\u09bf\u099b\u09c1 \u099c\u09be\u09a8\u09a4\u09c7 \u099a\u09be\u09a8 \u09a4\u09ac\u09c7 \u099c\u09bf\u099c\u09cd\u099e\u09be\u09b8\u09be \u0995\u09b0\u09a4\u09c7 \u09aa\u09be\u09b0\u09c7\u09a8\u0964 \n\n\n\n",
+          "text_equal": true,
+          "first_sse_s": 4.912838381016627,
+          "tpot_est_s": 0.03936234632349243,
+          "e2e_s": 6.133071117044892
+        },
+        {
+          "id": "r0-n2048-o32-c16-11",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 32,
+          "text": " Paris\n\u0985\u09b8\u09cd\u09a4\u09bf\u09a4\u09cd\u09ac\u09c7 \u09b8\u09be\u09b9\u09be\u09af\u09cd\u09af \u0995\u09b0\u09be\u09b0 \u099c\u09a8\u09cd\u09af, \u0986\u09aa\u09a8\u09bf \u09af\u09a6\u09bf \u0985\u09a8\u09cd\u09af \u0995\u09bf\u099b\u09c1 \u099c\u09be\u09a8\u09a4\u09c7 \u099a\u09be\u09a8 \u09a4\u09ac\u09c7 \u099c\u09bf\u099c\u09cd\u099e\u09be\u09b8\u09be \u0995\u09b0\u09a4\u09c7 \u09aa\u09be\u09b0\u09c7\u09a8\u0964 \n\n\n\n",
+          "text_equal": true,
+          "first_sse_s": 5.260292185004801,
+          "tpot_est_s": 0.034364268484134826,
+          "e2e_s": 6.32558450801298
+        },
+        {
+          "id": "r0-n2048-o32-c16-12",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 32,
+          "text": " Paris\n\u0985\u09b8\u09cd\u09a4\u09bf\u09a4\u09cd\u09ac\u09c7 \u09b8\u09be\u09b9\u09be\u09af\u09cd\u09af \u0995\u09b0\u09be\u09b0 \u099c\u09a8\u09cd\u09af, \u0986\u09aa\u09a8\u09bf \u09af\u09a6\u09bf \u0985\u09a8\u09cd\u09af \u0995\u09bf\u099b\u09c1 \u099c\u09be\u09a8\u09a4\u09c7 \u099a\u09be\u09a8 \u09a4\u09ac\u09c7 \u099c\u09bf\u099c\u09cd\u099e\u09be\u09b8\u09be \u0995\u09b0\u09a4\u09c7 \u09aa\u09be\u09b0\u09c7\u09a8\u0964 \n\n\n\n",
+          "text_equal": true,
+          "first_sse_s": 0.5216506369761191,
+          "tpot_est_s": 0.040123447580563444,
+          "e2e_s": 1.765477511973586
+        },
+        {
+          "id": "r0-n2048-o32-c16-13",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 32,
+          "text": " Paris\n\u0985\u09b8\u09cd\u09a4\u09bf\u09a4\u09cd\u09ac\u09c7 \u09b8\u09be\u09b9\u09be\u09af\u09cd\u09af \u0995\u09b0\u09be\u09b0 \u099c\u09a8\u09cd\u09af, \u0986\u09aa\u09a8\u09bf \u09af\u09a6\u09bf \u0985\u09a8\u09cd\u09af \u0995\u09bf\u099b\u09c1 \u099c\u09be\u09a8\u09a4\u09c7 \u099a\u09be\u09a8 \u09a4\u09ac\u09c7 \u099c\u09bf\u099c\u09cd\u099e\u09be\u09b8\u09be \u0995\u09b0\u09a4\u09c7 \u09aa\u09be\u09b0\u09c7\u09a8\u0964 \n\n\n\n",
+          "text_equal": true,
+          "first_sse_s": 4.418821814004332,
+          "tpot_est_s": 0.03901140925834977,
+          "e2e_s": 5.628175501013175
+        },
+        {
+          "id": "r0-n2048-o32-c16-14",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 32,
+          "text": " Paris\n\u0985\u09b8\u09cd\u09a4\u09bf\u09a4\u09cd\u09ac\u09c7 \u09b8\u09be\u09b9\u09be\u09af\u09cd\u09af \u0995\u09b0\u09be\u09b0 \u099c\u09a8\u09cd\u09af, \u0986\u09aa\u09a8\u09bf \u09af\u09a6\u09bf \u0985\u09a8\u09cd\u09af \u0995\u09bf\u099b\u09c1 \u099c\u09be\u09a8\u09a4\u09c7 \u099a\u09be\u09a8 \u09a4\u09ac\u09c7 \u099c\u09bf\u099c\u09cd\u099e\u09be\u09b8\u09be \u0995\u09b0\u09a4\u09c7 \u09aa\u09be\u09b0\u09c7\u09a8\u0964 \n\n\n\n",
+          "text_equal": true,
+          "first_sse_s": 3.697180886985734,
+          "tpot_est_s": 0.039919888258971754,
+          "e2e_s": 4.9346974230138585
+        },
+        {
+          "id": "r0-n2048-o32-c16-15",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 32,
+          "text": " Paris\n\u0985\u09b8\u09cd\u09a4\u09bf\u09a4\u09cd\u09ac\u09c7 \u09b8\u09be\u09b9\u09be\u09af\u09cd\u09af \u0995\u09b0\u09be\u09b0 \u099c\u09a8\u09cd\u09af, \u0986\u09aa\u09a8\u09bf \u09af\u09a6\u09bf \u0985\u09a8\u09cd\u09af \u0995\u09bf\u099b\u09c1 \u099c\u09be\u09a8\u09a4\u09c7 \u099a\u09be\u09a8 \u09a4\u09ac\u09c7 \u099c\u09bf\u099c\u09cd\u099e\u09be\u09b8\u09be \u0995\u09b0\u09a4\u09c7 \u09aa\u09be\u09b0\u09c7\u09a8\u0964 \n\n\n\n",
+          "text_equal": true,
+          "first_sse_s": 2.1053979440475814,
+          "tpot_est_s": 0.04035491374088451,
+          "e2e_s": 3.3564002700150013
+        }
+      ]
+    },
+    {
+      "repeat": 0,
+      "input_tokens": 2048,
+      "output_tokens_per_request": 128,
+      "concurrency": 16,
+      "summary": {
+        "requests": 16,
+        "output_tokens": 2048,
+        "wall_s": 16.039330890984274,
+        "output_tok_s": 127.68612443497771,
+        "median_first_sse_s": 6.680608969996683,
+        "median_tpot_est_s": 0.028113771870037482,
+        "median_e2e_s": 10.267041852494003,
+        "all_text_equal": true
+      },
+      "rows": [
+        {
+          "id": "r0-n2048-o128-c16-0",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 128,
+          "text": " Paris\n\u0985\u09b8\u09cd\u09a4\u09bf\u09a4\u09cd\u09ac\u09c7 \u09b8\u09be\u09b9\u09be\u09af\u09cd\u09af \u0995\u09b0\u09be\u09b0 \u099c\u09a8\u09cd\u09af, \u0986\u09aa\u09a8\u09bf \u09af\u09a6\u09bf \u0985\u09a8\u09cd\u09af \u0995\u09bf\u099b\u09c1 \u099c\u09be\u09a8\u09a4\u09c7 \u099a\u09be\u09a8 \u09a4\u09ac\u09c7 \u099c\u09bf\u099c\u09cd\u099e\u09be\u09b8\u09be \u0995\u09b0\u09a4\u09c7 \u09aa\u09be\u09b0\u09c7\u09a8\u0964 \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n",
+          "text_equal": true,
+          "first_sse_s": 11.987091800023336,
+          "tpot_est_s": 0.027160257212432672,
+          "e2e_s": 15.436444466002285
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+        {
+          "id": "r0-n2048-o128-c16-1",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 128,
+          "text": " Paris\n\u0985\u09b8\u09cd\u09a4\u09bf\u09a4\u09cd\u09ac\u09c7 \u09b8\u09be\u09b9\u09be\u09af\u09cd\u09af \u0995\u09b0\u09be\u09b0 \u099c\u09a8\u09cd\u09af, \u0986\u09aa\u09a8\u09bf \u09af\u09a6\u09bf \u0985\u09a8\u09cd\u09af \u0995\u09bf\u099b\u09c1 \u099c\u09be\u09a8\u09a4\u09c7 \u099a\u09be\u09a8 \u09a4\u09ac\u09c7 \u099c\u09bf\u099c\u09cd\u099e\u09be\u09b8\u09be \u0995\u09b0\u09a4\u09c7 \u09aa\u09be\u09b0\u09c7\u09a8\u0964 \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n",
+          "text_equal": true,
+          "first_sse_s": 13.013562459032983,
+          "tpot_est_s": 0.023805015983965658,
+          "e2e_s": 16.03679948899662
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+        {
+          "id": "r0-n2048-o128-c16-2",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 128,
+          "text": " Paris\n\u0985\u09b8\u09cd\u09a4\u09bf\u09a4\u09cd\u09ac\u09c7 \u09b8\u09be\u09b9\u09be\u09af\u09cd\u09af \u0995\u09b0\u09be\u09b0 \u099c\u09a8\u09cd\u09af, \u0986\u09aa\u09a8\u09bf \u09af\u09a6\u09bf \u0985\u09a8\u09cd\u09af \u0995\u09bf\u099b\u09c1 \u099c\u09be\u09a8\u09a4\u09c7 \u099a\u09be\u09a8 \u09a4\u09ac\u09c7 \u099c\u09bf\u099c\u09cd\u099e\u09be\u09b8\u09be \u0995\u09b0\u09a4\u09c7 \u09aa\u09be\u09b0\u09c7\u09a8\u0964 \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n",
+          "text_equal": true,
+          "first_sse_s": 4.871158912952524,
+          "tpot_est_s": 0.028691922370381068,
+          "e2e_s": 8.51503305399092
+        },
+        {
+          "id": "r0-n2048-o128-c16-3",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 128,
+          "text": " Paris\n\u0985\u09b8\u09cd\u09a4\u09bf\u09a4\u09cd\u09ac\u09c7 \u09b8\u09be\u09b9\u09be\u09af\u09cd\u09af \u0995\u09b0\u09be\u09b0 \u099c\u09a8\u09cd\u09af, \u0986\u09aa\u09a8\u09bf \u09af\u09a6\u09bf \u0985\u09a8\u09cd\u09af \u0995\u09bf\u099b\u09c1 \u099c\u09be\u09a8\u09a4\u09c7 \u099a\u09be\u09a8 \u09a4\u09ac\u09c7 \u099c\u09bf\u099c\u09cd\u099e\u09be\u09b8\u09be \u0995\u09b0\u09a4\u09c7 \u09aa\u09be\u09b0\u09c7\u09a8\u0964 \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n",
+          "text_equal": true,
+          "first_sse_s": 9.197774193016812,
+          "tpot_est_s": 0.02753241939376673,
+          "e2e_s": 12.694391456025187
+        },
+        {
+          "id": "r0-n2048-o128-c16-4",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 128,
+          "text": " Paris\n\u0985\u09b8\u09cd\u09a4\u09bf\u09a4\u09cd\u09ac\u09c7 \u09b8\u09be\u09b9\u09be\u09af\u09cd\u09af \u0995\u09b0\u09be\u09b0 \u099c\u09a8\u09cd\u09af, \u0986\u09aa\u09a8\u09bf \u09af\u09a6\u09bf \u0985\u09a8\u09cd\u09af \u0995\u09bf\u099b\u09c1 \u099c\u09be\u09a8\u09a4\u09c7 \u099a\u09be\u09a8 \u09a4\u09ac\u09c7 \u099c\u09bf\u099c\u09cd\u099e\u09be\u09b8\u09be \u0995\u09b0\u09a4\u09c7 \u09aa\u09be\u09b0\u09c7\u09a8\u0964 \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n",
+          "text_equal": true,
+          "first_sse_s": 8.845966605003923,
+          "tpot_est_s": 0.027646840424881882,
+          "e2e_s": 12.357115338963922
+        },
+        {
+          "id": "r0-n2048-o128-c16-5",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 128,
+          "text": " Paris\n\u0985\u09b8\u09cd\u09a4\u09bf\u09a4\u09cd\u09ac\u09c7 \u09b8\u09be\u09b9\u09be\u09af\u09cd\u09af \u0995\u09b0\u09be\u09b0 \u099c\u09a8\u09cd\u09af, \u0986\u09aa\u09a8\u09bf \u09af\u09a6\u09bf \u0985\u09a8\u09cd\u09af \u0995\u09bf\u099b\u09c1 \u099c\u09be\u09a8\u09a4\u09c7 \u099a\u09be\u09a8 \u09a4\u09ac\u09c7 \u099c\u09bf\u099c\u09cd\u099e\u09be\u09b8\u09be \u0995\u09b0\u09a4\u09c7 \u09aa\u09be\u09b0\u09c7\u09a8\u0964 \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n",
+          "text_equal": true,
+          "first_sse_s": 1.2839322320069186,
+          "tpot_est_s": 0.028423178354351538,
+          "e2e_s": 4.893675883009564
+        },
+        {
+          "id": "r0-n2048-o128-c16-6",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 128,
+          "text": " Paris\n\u0985\u09b8\u09cd\u09a4\u09bf\u09a4\u09cd\u09ac\u09c7 \u09b8\u09be\u09b9\u09be\u09af\u09cd\u09af \u0995\u09b0\u09be\u09b0 \u099c\u09a8\u09cd\u09af, \u0986\u09aa\u09a8\u09bf \u09af\u09a6\u09bf \u0985\u09a8\u09cd\u09af \u0995\u09bf\u099b\u09c1 \u099c\u09be\u09a8\u09a4\u09c7 \u099a\u09be\u09a8 \u09a4\u09ac\u09c7 \u099c\u09bf\u099c\u09cd\u099e\u09be\u09b8\u09be \u0995\u09b0\u09a4\u09c7 \u09aa\u09be\u09b0\u09c7\u09a8\u0964 \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n",
+          "text_equal": true,
+          "first_sse_s": 0.9151753319893032,
+          "tpot_est_s": 0.02850253959818868,
+          "e2e_s": 4.534997860959265
+        },
+        {
+          "id": "r0-n2048-o128-c16-7",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 128,
+          "text": " Paris\n\u0985\u09b8\u09cd\u09a4\u09bf\u09a4\u09cd\u09ac\u09c7 \u09b8\u09be\u09b9\u09be\u09af\u09cd\u09af \u0995\u09b0\u09be\u09b0 \u099c\u09a8\u09cd\u09af, \u0986\u09aa\u09a8\u09bf \u09af\u09a6\u09bf \u0985\u09a8\u09cd\u09af \u0995\u09bf\u099b\u09c1 \u099c\u09be\u09a8\u09a4\u09c7 \u099a\u09be\u09a8 \u09a4\u09ac\u09c7 \u099c\u09bf\u099c\u09cd\u099e\u09be\u09b8\u09be \u0995\u09b0\u09a4\u09c7 \u09aa\u09be\u09b0\u09c7\u09a8\u0964 \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n",
+          "text_equal": true,
+          "first_sse_s": 12.333680796029512,
+          "tpot_est_s": 0.025913711456677282,
+          "e2e_s": 15.624722151027527
+        },
+        {
+          "id": "r0-n2048-o128-c16-8",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 128,
+          "text": " Paris\n\u0985\u09b8\u09cd\u09a4\u09bf\u09a4\u09cd\u09ac\u09c7 \u09b8\u09be\u09b9\u09be\u09af\u09cd\u09af \u0995\u09b0\u09be\u09b0 \u099c\u09a8\u09cd\u09af, \u0986\u09aa\u09a8\u09bf \u09af\u09a6\u09bf \u0985\u09a8\u09cd\u09af \u0995\u09bf\u099b\u09c1 \u099c\u09be\u09a8\u09a4\u09c7 \u099a\u09be\u09a8 \u09a4\u09ac\u09c7 \u099c\u09bf\u099c\u09cd\u099e\u09be\u09b8\u09be \u0995\u09b0\u09a4\u09c7 \u09aa\u09be\u09b0\u09c7\u09a8\u0964 \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n",
+          "text_equal": true,
+          "first_sse_s": 0.17687697900692,
+          "tpot_est_s": 0.028578650181119398,
+          "e2e_s": 3.8063655520090833
+        },
+        {
+          "id": "r0-n2048-o128-c16-9",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 128,
+          "text": " Paris\n\u0985\u09b8\u09cd\u09a4\u09bf\u09a4\u09cd\u09ac\u09c7 \u09b8\u09be\u09b9\u09be\u09af\u09cd\u09af \u0995\u09b0\u09be\u09b0 \u099c\u09a8\u09cd\u09af, \u0986\u09aa\u09a8\u09bf \u09af\u09a6\u09bf \u0985\u09a8\u09cd\u09af \u0995\u09bf\u099b\u09c1 \u099c\u09be\u09a8\u09a4\u09c7 \u099a\u09be\u09a8 \u09a4\u09ac\u09c7 \u099c\u09bf\u099c\u09cd\u099e\u09be\u09b8\u09be \u0995\u09b0\u09a4\u09c7 \u09aa\u09be\u09b0\u09c7\u09a8\u0964 \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n",
+          "text_equal": true,
+          "first_sse_s": 8.489697591983713,
+          "tpot_est_s": 0.02773416505514197,
+          "e2e_s": 12.011936553986743
+        },
+        {
+          "id": "r0-n2048-o128-c16-10",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 128,
+          "text": " Paris\n\u0985\u09b8\u09cd\u09a4\u09bf\u09a4\u09cd\u09ac\u09c7 \u09b8\u09be\u09b9\u09be\u09af\u09cd\u09af \u0995\u09b0\u09be\u09b0 \u099c\u09a8\u09cd\u09af, \u0986\u09aa\u09a8\u09bf \u09af\u09a6\u09bf \u0985\u09a8\u09cd\u09af \u0995\u09bf\u099b\u09c1 \u099c\u09be\u09a8\u09a4\u09c7 \u099a\u09be\u09a8 \u09a4\u09ac\u09c7 \u099c\u09bf\u099c\u09cd\u099e\u09be\u09b8\u09be \u0995\u09b0\u09a4\u09c7 \u09aa\u09be\u09b0\u09c7\u09a8\u0964 \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n",
+          "text_equal": true,
+          "first_sse_s": 5.226061203982681,
+          "tpot_est_s": 0.028674892606360355,
+          "e2e_s": 8.867772564990446
+        },
+        {
+          "id": "r0-n2048-o128-c16-11",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 128,
+          "text": " Paris\n\u0985\u09b8\u09cd\u09a4\u09bf\u09a4\u09cd\u09ac\u09c7 \u09b8\u09be\u09b9\u09be\u09af\u09cd\u09af \u0995\u09b0\u09be\u09b0 \u099c\u09a8\u09cd\u09af, \u0986\u09aa\u09a8\u09bf \u09af\u09a6\u09bf \u0985\u09a8\u09cd\u09af \u0995\u09bf\u099b\u09c1 \u099c\u09be\u09a8\u09a4\u09c7 \u099a\u09be\u09a8 \u09a4\u09ac\u09c7 \u099c\u09bf\u099c\u09cd\u099e\u09be\u09b8\u09be \u0995\u09b0\u09a4\u09c7 \u09aa\u09be\u09b0\u09c7\u09a8\u0964 \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n",
+          "text_equal": true,
+          "first_sse_s": 4.147898643976077,
+          "tpot_est_s": 0.02869272374824848,
+          "e2e_s": 7.7918745600036345
+        },
+        {
+          "id": "r0-n2048-o128-c16-12",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 128,
+          "text": " Paris\n\u0985\u09b8\u09cd\u09a4\u09bf\u09a4\u09cd\u09ac\u09c7 \u09b8\u09be\u09b9\u09be\u09af\u09cd\u09af \u0995\u09b0\u09be\u09b0 \u099c\u09a8\u09cd\u09af, \u0986\u09aa\u09a8\u09bf \u09af\u09a6\u09bf \u0985\u09a8\u09cd\u09af \u0995\u09bf\u099b\u09c1 \u099c\u09be\u09a8\u09a4\u09c7 \u099a\u09be\u09a8 \u09a4\u09ac\u09c7 \u099c\u09bf\u099c\u09cd\u099e\u09be\u09b8\u09be \u0995\u09b0\u09a4\u09c7 \u09aa\u09be\u09b0\u09c7\u09a8\u0964 \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n",
+          "text_equal": true,
+          "first_sse_s": 0.5517799399676733,
+          "tpot_est_s": 0.02853207150382525,
+          "e2e_s": 4.17535302095348
+        },
+        {
+          "id": "r0-n2048-o128-c16-13",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 128,
+          "text": " Paris\n\u0985\u09b8\u09cd\u09a4\u09bf\u09a4\u09cd\u09ac\u09c7 \u09b8\u09be\u09b9\u09be\u09af\u09cd\u09af \u0995\u09b0\u09be\u09b0 \u099c\u09a8\u09cd\u09af, \u0986\u09aa\u09a8\u09bf \u09af\u09a6\u09bf \u0985\u09a8\u09cd\u09af \u0995\u09bf\u099b\u09c1 \u099c\u09be\u09a8\u09a4\u09c7 \u099a\u09be\u09a8 \u09a4\u09ac\u09c7 \u099c\u09bf\u099c\u09cd\u099e\u09be\u09b8\u09be \u0995\u09b0\u09a4\u09c7 \u09aa\u09be\u09b0\u09c7\u09a8\u0964 \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n",
+          "text_equal": true,
+          "first_sse_s": 8.135156736010686,
+          "tpot_est_s": 0.027804365385723426,
+          "e2e_s": 11.66631113999756
+        },
+        {
+          "id": "r0-n2048-o128-c16-14",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 128,
+          "text": " Paris\n\u0985\u09b8\u09cd\u09a4\u09bf\u09a4\u09cd\u09ac\u09c7 \u09b8\u09be\u09b9\u09be\u09af\u09cd\u09af \u0995\u09b0\u09be\u09b0 \u099c\u09a8\u09cd\u09af, \u0986\u09aa\u09a8\u09bf \u09af\u09a6\u09bf \u0985\u09a8\u09cd\u09af \u0995\u09bf\u099b\u09c1 \u099c\u09be\u09a8\u09a4\u09c7 \u099a\u09be\u09a8 \u09a4\u09ac\u09c7 \u099c\u09bf\u099c\u09cd\u099e\u09be\u09b8\u09be \u0995\u09b0\u09a4\u09c7 \u09aa\u09be\u09b0\u09c7\u09a8\u0964 \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n",
+          "text_equal": true,
+          "first_sse_s": 12.672126130957622,
+          "tpot_est_s": 0.024893097708727725,
+          "e2e_s": 15.833549539966043
+        },
+        {
+          "id": "r0-n2048-o128-c16-15",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 128,
+          "text": " Paris\n\u0985\u09b8\u09cd\u09a4\u09bf\u09a4\u09cd\u09ac\u09c7 \u09b8\u09be\u09b9\u09be\u09af\u09cd\u09af \u0995\u09b0\u09be\u09b0 \u099c\u09a8\u09cd\u09af, \u0986\u09aa\u09a8\u09bf \u09af\u09a6\u09bf \u0985\u09a8\u09cd\u09af \u0995\u09bf\u099b\u09c1 \u099c\u09be\u09a8\u09a4\u09c7 \u099a\u09be\u09a8 \u09a4\u09ac\u09c7 \u099c\u09bf\u099c\u09cd\u099e\u09be\u09b8\u09be \u0995\u09b0\u09a4\u09c7 \u09aa\u09be\u09b0\u09c7\u09a8\u0964 \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n",
+          "text_equal": true,
+          "first_sse_s": 4.511710115999449,
+          "tpot_est_s": 0.028719228661186466,
+          "e2e_s": 8.15905215597013
+        }
+      ]
+    },
+    {
+      "repeat": 0,
+      "input_tokens": 8192,
+      "output_tokens_per_request": 32,
+      "concurrency": 16,
+      "summary": {
+        "requests": 16,
+        "output_tokens": 512,
+        "wall_s": 25.08320806903066,
+        "output_tok_s": 20.412062069211476,
+        "median_first_sse_s": 12.652028317505028,
+        "median_tpot_est_s": 0.04802835259714254,
+        "median_e2e_s": 14.101079530490097,
+        "all_text_equal": true
+      },
+      "rows": [
+        {
+          "id": "r0-n8192-o32-c16-0",
+          "endpoint": 0,
+          "input_tokens": 8192,
+          "output_tokens": 32,
+          "text": " Paris.\nThis is background context. The red box contains apples. The blue box contains books.\nThis is background context. The red box contains apples.",
+          "text_equal": true,
+          "first_sse_s": 24.360000838001724,
+          "tpot_est_s": 0.023282631935041036,
+          "e2e_s": 25.081762427987996
+        },
+        {
+          "id": "r0-n8192-o32-c16-1",
+          "endpoint": 0,
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+      "text": " Paris\n\n nonlocal\n\n\nThis is background context. The red box contains apples. The blue box contains books.\nThis is background"
+    },
+    "128:128": {
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+      "text": " Paris\n\n nonlocal\n\n\nThis is background context. The red box contains apples. The blue box contains books.\nThis is background context. The red box contains apples. The blue box contains books.\nThis is background context. The red box contains apples. The blue box contains books.\nThis is background context. The red box contains apples. The blue box contains books.\nThis is background context. The red box contains apples. The blue box contains books.\nThis is background context. The red box contains apples. The blue box contains books.\nThis is background context. The red box contains"
+    },
+    "512:32": {
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+      "text": " Paris\n\u0905\u0902\u0924\u093f\u092eabalanswer: Paris\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902"
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+      "text": " Paris\n\u0905\u0902\u0924\u093f\u092eabalanswer: Paris\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902\u0924\u093f\u092e\u0905\u0902"
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+      "text": " Paris\n\u0985\u09b8\u09cd\u09a4\u09bf\u09a4\u09cd\u09ac\u09c7 \u09b8\u09be\u09b9\u09be\u09af\u09cd\u09af \u0995\u09b0\u09be\u09b0 \u099c\u09a8\u09cd\u09af, \u0986\u09aa\u09a8\u09bf \u09af\u09a6\u09bf \u0985\u09a8\u09cd\u09af \u0995\u09bf\u099b\u09c1 \u099c\u09be\u09a8\u09a4\u09c7 \u099a\u09be\u09a8 \u09a4\u09ac\u09c7 \u099c\u09bf\u099c\u09cd\u099e\u09be\u09b8\u09be \u0995\u09b0\u09a4\u09c7 \u09aa\u09be\u09b0\u09c7\u09a8\u0964 \n\n\n\n"
+    },
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+      "text": " Paris\n\u0985\u09b8\u09cd\u09a4\u09bf\u09a4\u09cd\u09ac\u09c7 \u09b8\u09be\u09b9\u09be\u09af\u09cd\u09af \u0995\u09b0\u09be\u09b0 \u099c\u09a8\u09cd\u09af, \u0986\u09aa\u09a8\u09bf \u09af\u09a6\u09bf \u0985\u09a8\u09cd\u09af \u0995\u09bf\u099b\u09c1 \u099c\u09be\u09a8\u09a4\u09c7 \u099a\u09be\u09a8 \u09a4\u09ac\u09c7 \u099c\u09bf\u099c\u09cd\u099e\u09be\u09b8\u09be \u0995\u09b0\u09a4\u09c7 \u09aa\u09be\u09b0\u09c7\u09a8\u0964 \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n"
+    },
+    "8192:32": {
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+      "text": " Paris.\nThis is background context. The red box contains apples. The blue box contains books.\nThis is background context. The red box contains apples."
+    },
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+      "text": " Paris.\nThis is background context. The red box contains apples. The blue box contains books.\nThis is background context. The red box contains apples. The blue box contains books.\nThis is background context. The red box contains apples. The blue box contains books.\nThis is background context. The red box contains apples. The blue box contains books.\nThis is background context. The red box contains apples. The blue box contains books.\nThis is background context. The red box contains apples. The blue box contains books.\nThis is background context. The red box contains apples. The blue box contains books."
+    }
+  },
+  "warmup_and_checks_s": 127.59834061097354,
+  "metrics": [
+    "# HELP vllm:num_requests_running Requests executing on the stage.\n# TYPE vllm:num_requests_running gauge\nvllm:num_requests_running{model_name=\"Gemma3-1B\"} 0\n# HELP vllm:num_requests_waiting Requests queued for the stage owner.\n# TYPE vllm:num_requests_waiting gauge\nvllm:num_requests_waiting{model_name=\"Gemma3-1B\"} 0\n# HELP vllm:kv_cache_usage_perc Occupied fraction of the stage's KV lanes (1 = full).\n# TYPE vllm:kv_cache_usage_perc gauge\nvllm:kv_cache_usage_perc{model_name=\"Gemma3-1B\"} 0.0\n# HELP vllm:gpu_cache_usage_perc Occupied fraction of the stage's KV lanes (1 = full).\n# TYPE vllm:gpu_cache_usage_perc gauge\nvllm:gpu_cache_usage_perc{model_name=\"Gemma3-1B\"} 0.0\n# HELP vllm:request_success_total Finished requests by finish reason.\n# TYPE vllm:request_success_total counter\nvllm:request_success_total{model_name=\"Gemma3-1B\",finished_reason=\"length\"} 264\n# HELP airm_stage_request_failures_total Requests that ended with an engine error.\n# TYPE airm_stage_request_failures_total counter\nairm_stage_request_failures_total{model_name=\"Gemma3-1B\"} 0\n# HELP vllm:prompt_tokens_total Prompt tokens of finished requests.\n# TYPE vllm:prompt_tokens_total counter\nvllm:prompt_tokens_total{model_name=\"Gemma3-1B\"} 718080\n# HELP vllm:generation_tokens_total Generated tokens of finished requests.\n# TYPE vllm:generation_tokens_total counter\nvllm:generation_tokens_total{model_name=\"Gemma3-1B\"} 21120\n# HELP vllm:time_to_first_token_seconds Time to first token (prefill stage: fenced prefill).\n# TYPE vllm:time_to_first_token_seconds histogram\nvllm:time_to_first_token_seconds_bucket{model_name=\"Gemma3-1B\",le=\"0.001\"} 0\nvllm:time_to_first_token_seconds_bucket{model_name=\"Gemma3-1B\",le=\"0.005\"} 0\nvllm:time_to_first_token_seconds_bucket{model_name=\"Gemma3-1B\",le=\"0.01\"} 0\nvllm:time_to_first_token_seconds_bucket{model_name=\"Gemma3-1B\",le=\"0.02\"} 0\nvllm:time_to_first_token_seconds_bucket{model_name=\"Gemma3-1B\",le=\"0.04\"} 5\nvllm:time_to_first_token_seconds_bucket{model_name=\"Gemma3-1B\",le=\"0.06\"} 11\nvllm:time_to_first_token_seconds_bucket{model_name=\"Gemma3-1B\",le=\"0.08\"} 15\nvllm:time_to_first_token_seconds_bucket{model_name=\"Gemma3-1B\",le=\"0.1\"} 15\nvllm:time_to_first_token_seconds_bucket{model_name=\"Gemma3-1B\",le=\"0.25\"} 37\nvllm:time_to_first_token_seconds_bucket{model_name=\"Gemma3-1B\",le=\"0.5\"} 41\nvllm:time_to_first_token_seconds_bucket{model_name=\"Gemma3-1B\",le=\"0.75\"} 47\nvllm:time_to_first_token_seconds_bucket{model_name=\"Gemma3-1B\",le=\"1.0\"} 64\nvllm:time_to_first_token_seconds_bucket{model_name=\"Gemma3-1B\",le=\"2.5\"} 117\nvllm:time_to_first_token_seconds_bucket{model_name=\"Gemma3-1B\",le=\"5.0\"} 160\nvllm:time_to_first_token_seconds_bucket{model_name=\"Gemma3-1B\",le=\"7.5\"} 186\nvllm:time_to_first_token_seconds_bucket{model_name=\"Gemma3-1B\",le=\"10.0\"} 214\nvllm:time_to_first_token_seconds_bucket{model_name=\"Gemma3-1B\",le=\"+Inf\"} 264\nvllm:time_to_first_token_seconds_sum{model_name=\"Gemma3-1B\"} 1505.1111895471695\nvllm:time_to_first_token_seconds_count{model_name=\"Gemma3-1B\"} 264\n# HELP vllm:time_per_output_token_seconds Time per output token after the first (decode stage).\n# TYPE vllm:time_per_output_token_seconds histogram\nvllm:time_per_output_token_seconds_bucket{model_name=\"Gemma3-1B\",le=\"0.01\"} 0\nvllm:time_per_output_token_seconds_bucket{model_name=\"Gemma3-1B\",le=\"0.025\"} 70\nvllm:time_per_output_token_seconds_bucket{model_name=\"Gemma3-1B\",le=\"0.05\"} 262\nvllm:time_per_output_token_seconds_bucket{model_name=\"Gemma3-1B\",le=\"0.075\"} 264\nvllm:time_per_output_token_seconds_bucket{model_name=\"Gemma3-1B\",le=\"0.1\"} 264\nvllm:time_per_output_token_seconds_bucket{model_name=\"Gemma3-1B\",le=\"0.15\"} 264\nvllm:time_per_output_token_seconds_bucket{model_name=\"Gemma3-1B\",le=\"0.2\"} 264\nvllm:time_per_output_token_seconds_bucket{model_name=\"Gemma3-1B\",le=\"0.3\"} 264\nvllm:time_per_output_token_seconds_bucket{model_name=\"Gemma3-1B\",le=\"0.4\"} 264\nvllm:time_per_output_token_seconds_bucket{model_name=\"Gemma3-1B\",le=\"0.5\"} 264\nvllm:time_per_output_token_seconds_bucket{model_name=\"Gemma3-1B\",le=\"0.75\"} 264\nvllm:time_per_output_token_seconds_bucket{model_name=\"Gemma3-1B\",le=\"1.0\"} 264\nvllm:time_per_output_token_seconds_bucket{model_name=\"Gemma3-1B\",le=\"2.5\"} 264\nvllm:time_per_output_token_seconds_bucket{model_name=\"Gemma3-1B\",le=\"+Inf\"} 264\nvllm:time_per_output_token_seconds_sum{model_name=\"Gemma3-1B\"} 8.397689850651556\nvllm:time_per_output_token_seconds_count{model_name=\"Gemma3-1B\"} 264\n# HELP vllm:e2e_request_latency_seconds Stage request latency, from execution start to reply.\n# TYPE vllm:e2e_request_latency_seconds histogram\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Gemma3-1B\",le=\"0.3\"} 0\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Gemma3-1B\",le=\"0.5\"} 0\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Gemma3-1B\",le=\"0.8\"} 1\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Gemma3-1B\",le=\"1.0\"} 11\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Gemma3-1B\",le=\"1.5\"} 21\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Gemma3-1B\",le=\"2.0\"} 35\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Gemma3-1B\",le=\"2.5\"} 54\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Gemma3-1B\",le=\"5.0\"} 120\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Gemma3-1B\",le=\"10.0\"} 181\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Gemma3-1B\",le=\"15.0\"} 228\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Gemma3-1B\",le=\"20.0\"} 240\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Gemma3-1B\",le=\"30.0\"} 264\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Gemma3-1B\",le=\"40.0\"} 264\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Gemma3-1B\",le=\"50.0\"} 264\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Gemma3-1B\",le=\"60.0\"} 264\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Gemma3-1B\",le=\"+Inf\"} 264\nvllm:e2e_request_latency_seconds_sum{model_name=\"Gemma3-1B\"} 2134.2233280055225\nvllm:e2e_request_latency_seconds_count{model_name=\"Gemma3-1B\"} 264\n# HELP airm_kv_transfer_seconds KV transfer time from the prefill source to the decode destination.\n# TYPE airm_kv_transfer_seconds histogram\nairm_kv_transfer_seconds_bucket{model_name=\"Gemma3-1B\",le=\"0.0005\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"Gemma3-1B\",le=\"0.001\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"Gemma3-1B\",le=\"0.0025\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"Gemma3-1B\",le=\"0.005\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"Gemma3-1B\",le=\"0.01\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"Gemma3-1B\",le=\"0.025\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"Gemma3-1B\",le=\"0.05\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"Gemma3-1B\",le=\"0.1\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"Gemma3-1B\",le=\"0.25\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"Gemma3-1B\",le=\"0.5\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"Gemma3-1B\",le=\"1.0\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"Gemma3-1B\",le=\"+Inf\"} 0\nairm_kv_transfer_seconds_sum{model_name=\"Gemma3-1B\"} 0.0\nairm_kv_transfer_seconds_count{model_name=\"Gemma3-1B\"} 0\n# HELP vllm:cache_config_info Information of the LLMEngine CacheConfig.\n# TYPE vllm:cache_config_info gauge\nvllm:cache_config_info{model_name=\"Gemma3-1B\",block_size=\"64\",num_gpu_blocks=\"576\",enable_prefix_caching=\"False\"} 1\n# HELP vllm:prefix_cache_queries_total Prompt tokens looked up in the prefix cache.\n# TYPE vllm:prefix_cache_queries_total counter\nvllm:prefix_cache_queries_total{model_name=\"Gemma3-1B\"} 718080\n# HELP vllm:prefix_cache_hits_total Prompt tokens served from the prefix cache.\n# TYPE vllm:prefix_cache_hits_total counter\nvllm:prefix_cache_hits_total{model_name=\"Gemma3-1B\"} 0\n"
+  ],
+  "parity_passed": true
+}
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-gemma-http-c16-stream.json b/opt_prime/examples/pd/http_matrix/evidence265/stream265-gemma-http-c16-stream.json
new file mode 100644
index 00000000..622e4881
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-gemma-http-c16-stream.json
@@ -0,0 +1,25227 @@
+{
+  "passed": true,
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+  "transformers": "5.15.0",
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+        {
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+          "text": " Paris\n\u0985\u09b8\u09cd\u09a4\u09bf\u09a4\u09cd\u09ac\u09c7 \u09b8\u09be\u09b9\u09be\u09af\u09cd\u09af \u0995\u09b0\u09be\u09b0 \u099c\u09a8\u09cd\u09af, \u0986\u09aa\u09a8\u09bf \u09af\u09a6\u09bf \u0985\u09a8\u09cd\u09af \u0995\u09bf\u099b\u09c1 \u099c\u09be\u09a8\u09a4\u09c7 \u099a\u09be\u09a8 \u09a4\u09ac\u09c7 \u099c\u09bf\u099c\u09cd\u099e\u09be\u09b8\u09be \u0995\u09b0\u09a4\u09c7 \u09aa\u09be\u09b0\u09c7\u09a8\u0964 \u0628\u0627\u0644\u0646\u0633\u0628\u0629 to the question, what is the",
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+        {
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+          "text": " Paris\n\u0985\u09b8\u09cd\u09a4\u09bf\u09a4\u09cd\u09ac\u09c7 \u09b8\u09be\u09b9\u09be\u09af\u09cd\u09af \u0995\u09b0\u09be\u09b0 \u099c\u09a8\u09cd\u09af, \u0986\u09aa\u09a8\u09bf \u09af\u09a6\u09bf \u0985\u09a8\u09cd\u09af \u0995\u09bf\u099b\u09c1 \u099c\u09be\u09a8\u09a4\u09c7 \u099a\u09be\u09a8 \u09a4\u09ac\u09c7 \u099c\u09bf\u099c\u09cd\u099e\u09be\u09b8\u09be \u0995\u09b0\u09a4\u09c7 \u09aa\u09be\u09b0\u09c7\u09a8\u0964 \u0628\u0627\u0644\u0646\u0633\u0628\u0629 to the question, what is the",
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+          "text": " Paris\n\u0985\u09b8\u09cd\u09a4\u09bf\u09a4\u09cd\u09ac\u09c7 \u09b8\u09be\u09b9\u09be\u09af\u09cd\u09af \u0995\u09b0\u09be\u09b0 \u099c\u09a8\u09cd\u09af, \u0986\u09aa\u09a8\u09bf \u09af\u09a6\u09bf \u0985\u09a8\u09cd\u09af \u0995\u09bf\u099b\u09c1 \u099c\u09be\u09a8\u09a4\u09c7 \u099a\u09be\u09a8 \u09a4\u09ac\u09c7 \u099c\u09bf\u099c\u09cd\u099e\u09be\u09b8\u09be \u0995\u09b0\u09a4\u09c7 \u09aa\u09be\u09b0\u09c7\u09a8\u0964 \u0628\u0627\u0644\u0646\u0633\u0628\u0629 to the question, what is the",
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+        {
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+          "text": " Paris\n\u0985\u09b8\u09cd\u09a4\u09bf\u09a4\u09cd\u09ac\u09c7 \u09b8\u09be\u09b9\u09be\u09af\u09cd\u09af \u0995\u09b0\u09be\u09b0 \u099c\u09a8\u09cd\u09af, \u0986\u09aa\u09a8\u09bf \u09af\u09a6\u09bf \u0985\u09a8\u09cd\u09af \u0995\u09bf\u099b\u09c1 \u099c\u09be\u09a8\u09a4\u09c7 \u099a\u09be\u09a8 \u09a4\u09ac\u09c7 \u099c\u09bf\u099c\u09cd\u099e\u09be\u09b8\u09be \u0995\u09b0\u09a4\u09c7 \u09aa\u09be\u09b0\u09c7\u09a8\u0964 \u0628\u0627\u0644\u0646\u0633\u0628\u0629 to the question, what is the",
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+          "text": " Paris\n\u0985\u09b8\u09cd\u09a4\u09bf\u09a4\u09cd\u09ac\u09c7 \u09b8\u09be\u09b9\u09be\u09af\u09cd\u09af \u0995\u09b0\u09be\u09b0 \u099c\u09a8\u09cd\u09af, \u0986\u09aa\u09a8\u09bf \u09af\u09a6\u09bf \u0985\u09a8\u09cd\u09af \u0995\u09bf\u099b\u09c1 \u099c\u09be\u09a8\u09a4\u09c7 \u099a\u09be\u09a8 \u09a4\u09ac\u09c7 \u099c\u09bf\u099c\u09cd\u099e\u09be\u09b8\u09be \u0995\u09b0\u09a4\u09c7 \u09aa\u09be\u09b0\u09c7\u09a8\u0964 \u0628\u0627\u0644\u0646\u0633\u0628\u0629 to the question, what is the",
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+          "text": " Paris\n\u0985\u09b8\u09cd\u09a4\u09bf\u09a4\u09cd\u09ac\u09c7 \u09b8\u09be\u09b9\u09be\u09af\u09cd\u09af \u0995\u09b0\u09be\u09b0 \u099c\u09a8\u09cd\u09af, \u0986\u09aa\u09a8\u09bf \u09af\u09a6\u09bf \u0985\u09a8\u09cd\u09af \u0995\u09bf\u099b\u09c1 \u099c\u09be\u09a8\u09a4\u09c7 \u099a\u09be\u09a8 \u09a4\u09ac\u09c7 \u099c\u09bf\u099c\u09cd\u099e\u09be\u09b8\u09be \u0995\u09b0\u09a4\u09c7 \u09aa\u09be\u09b0\u09c7\u09a8\u0964 \u0628\u0627\u0644\u0646\u0633\u0628\u0629 to the question, what is the",
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+          "text": " Paris\n\u0985\u09b8\u09cd\u09a4\u09bf\u09a4\u09cd\u09ac\u09c7 \u09b8\u09be\u09b9\u09be\u09af\u09cd\u09af \u0995\u09b0\u09be\u09b0 \u099c\u09a8\u09cd\u09af, \u0986\u09aa\u09a8\u09bf \u09af\u09a6\u09bf \u0985\u09a8\u09cd\u09af \u0995\u09bf\u099b\u09c1 \u099c\u09be\u09a8\u09a4\u09c7 \u099a\u09be\u09a8 \u09a4\u09ac\u09c7 \u099c\u09bf\u099c\u09cd\u099e\u09be\u09b8\u09be \u0995\u09b0\u09a4\u09c7 \u09aa\u09be\u09b0\u09c7\u09a8\u0964 \u0628\u0627\u0644\u0646\u0633\u0628\u0629 to the question, what is the",
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+          "text": " Paris\n\u0985\u09b8\u09cd\u09a4\u09bf\u09a4\u09cd\u09ac\u09c7 \u09b8\u09be\u09b9\u09be\u09af\u09cd\u09af \u0995\u09b0\u09be\u09b0 \u099c\u09a8\u09cd\u09af, \u0986\u09aa\u09a8\u09bf \u09af\u09a6\u09bf \u0985\u09a8\u09cd\u09af \u0995\u09bf\u099b\u09c1 \u099c\u09be\u09a8\u09a4\u09c7 \u099a\u09be\u09a8 \u09a4\u09ac\u09c7 \u099c\u09bf\u099c\u09cd\u099e\u09be\u09b8\u09be \u0995\u09b0\u09a4\u09c7 \u09aa\u09be\u09b0\u09c7\u09a8\u0964 \u0628\u0627\u0644\u0646\u0633\u0628\u0629 to the question, what is the",
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+    },
+    {
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+      "concurrency": 16,
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+          "text": " Paris\n\u0905\u0902\u0924\u093f\u092e\u0941\u0915\u093f\u0932\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e",
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+          "text": " Paris\n\u0905\u0902\u0924\u093f\u092e\u0941\u0915\u093f\u0932\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e",
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+          "text": " Paris\n\u0905\u0902\u0924\u093f\u092e\u0941\u0915\u093f\u0932\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e",
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+          "text": " Paris\n\u0905\u0902\u0924\u093f\u092e\u0941\u0915\u093f\u0932\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e",
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+    {
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+      "output_tokens_per_request": 32,
+      "concurrency": 16,
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+          "input_tokens": 2048,
+          "output_tokens": 32,
+          "text": " Paris\n\u0985\u09b8\u09cd\u09a4\u09bf\u09a4\u09cd\u09ac\u09c7 \u09b8\u09be\u09b9\u09be\u09af\u09cd\u09af \u0995\u09b0\u09be\u09b0 \u099c\u09a8\u09cd\u09af, \u0986\u09aa\u09a8\u09bf \u09af\u09a6\u09bf \u0985\u09a8\u09cd\u09af \u0995\u09bf\u099b\u09c1 \u099c\u09be\u09a8\u09a4\u09c7 \u099a\u09be\u09a8 \u09a4\u09ac\u09c7 \u099c\u09bf\u099c\u09cd\u099e\u09be\u09b8\u09be \u0995\u09b0\u09a4\u09c7 \u09aa\u09be\u09b0\u09c7\u09a8\u0964 \u0628\u0627\u0644\u0646\u0633\u0628\u0629 to the question, what is the",
+          "text_equal": true,
+          "first_sse_s": 4.418598190008197,
+          "tpot_est_s": 0.025584910645134625,
+          "e2e_s": 5.21173042000737
+        },
+        {
+          "id": "r0-n2048-o32-c16-1",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 32,
+          "text": " Paris\n\u0985\u09b8\u09cd\u09a4\u09bf\u09a4\u09cd\u09ac\u09c7 \u09b8\u09be\u09b9\u09be\u09af\u09cd\u09af \u0995\u09b0\u09be\u09b0 \u099c\u09a8\u09cd\u09af, \u0986\u09aa\u09a8\u09bf \u09af\u09a6\u09bf \u0985\u09a8\u09cd\u09af \u0995\u09bf\u099b\u09c1 \u099c\u09be\u09a8\u09a4\u09c7 \u099a\u09be\u09a8 \u09a4\u09ac\u09c7 \u099c\u09bf\u099c\u09cd\u099e\u09be\u09b8\u09be \u0995\u09b0\u09a4\u09c7 \u09aa\u09be\u09b0\u09c7\u09a8\u0964 \u0628\u0627\u0644\u0646\u0633\u0628\u0629 to the question, what is the",
+          "text_equal": true,
+          "first_sse_s": 13.247787161031738,
+          "tpot_est_s": 0.026519567095449254,
+          "e2e_s": 14.069893740990665
+        },
+        {
+          "id": "r0-n2048-o32-c16-2",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 32,
+          "text": " Paris\n\u0985\u09b8\u09cd\u09a4\u09bf\u09a4\u09cd\u09ac\u09c7 \u09b8\u09be\u09b9\u09be\u09af\u09cd\u09af \u0995\u09b0\u09be\u09b0 \u099c\u09a8\u09cd\u09af, \u0986\u09aa\u09a8\u09bf \u09af\u09a6\u09bf \u0985\u09a8\u09cd\u09af \u0995\u09bf\u099b\u09c1 \u099c\u09be\u09a8\u09a4\u09c7 \u099a\u09be\u09a8 \u09a4\u09ac\u09c7 \u099c\u09bf\u099c\u09cd\u099e\u09be\u09b8\u09be \u0995\u09b0\u09a4\u09c7 \u09aa\u09be\u09b0\u09c7\u09a8\u0964 \u0628\u0627\u0644\u0646\u0633\u0628\u0629 to the question, what is the",
+          "text_equal": true,
+          "first_sse_s": 10.301562602980994,
+          "tpot_est_s": 0.02577255232327227,
+          "e2e_s": 11.100511725002434
+        },
+        {
+          "id": "r0-n2048-o32-c16-3",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 32,
+          "text": " Paris\n\u0985\u09b8\u09cd\u09a4\u09bf\u09a4\u09cd\u09ac\u09c7 \u09b8\u09be\u09b9\u09be\u09af\u09cd\u09af \u0995\u09b0\u09be\u09b0 \u099c\u09a8\u09cd\u09af, \u0986\u09aa\u09a8\u09bf \u09af\u09a6\u09bf \u0985\u09a8\u09cd\u09af \u0995\u09bf\u099b\u09c1 \u099c\u09be\u09a8\u09a4\u09c7 \u099a\u09be\u09a8 \u09a4\u09ac\u09c7 \u099c\u09bf\u099c\u09cd\u099e\u09be\u09b8\u09be \u0995\u09b0\u09a4\u09c7 \u09aa\u09be\u09b0\u09c7\u09a8\u0964 \u0628\u0627\u0644\u0646\u0633\u0628\u0629 to the question, what is the",
+          "text_equal": true,
+          "first_sse_s": 20.611714339000173,
+          "tpot_est_s": 0.025341278482829372,
+          "e2e_s": 21.397293971967883
+        },
+        {
+          "id": "r0-n2048-o32-c16-4",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 32,
+          "text": " Paris\n\u0985\u09b8\u09cd\u09a4\u09bf\u09a4\u09cd\u09ac\u09c7 \u09b8\u09be\u09b9\u09be\u09af\u09cd\u09af \u0995\u09b0\u09be\u09b0 \u099c\u09a8\u09cd\u09af, \u0986\u09aa\u09a8\u09bf \u09af\u09a6\u09bf \u0985\u09a8\u09cd\u09af \u0995\u09bf\u099b\u09c1 \u099c\u09be\u09a8\u09a4\u09c7 \u099a\u09be\u09a8 \u09a4\u09ac\u09c7 \u099c\u09bf\u099c\u09cd\u099e\u09be\u09b8\u09be \u0995\u09b0\u09a4\u09c7 \u09aa\u09be\u09b0\u09c7\u09a8\u0964 \u0628\u0627\u0644\u0646\u0633\u0628\u0629 to the question, what is the",
+          "text_equal": true,
+          "first_sse_s": 11.7731762319454,
+          "tpot_est_s": 0.02593839838875518,
+          "e2e_s": 12.57726658199681
+        },
+        {
+          "id": "r0-n2048-o32-c16-5",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 32,
+          "text": " Paris\n\u0985\u09b8\u09cd\u09a4\u09bf\u09a4\u09cd\u09ac\u09c7 \u09b8\u09be\u09b9\u09be\u09af\u09cd\u09af \u0995\u09b0\u09be\u09b0 \u099c\u09a8\u09cd\u09af, \u0986\u09aa\u09a8\u09bf \u09af\u09a6\u09bf \u0985\u09a8\u09cd\u09af \u0995\u09bf\u099b\u09c1 \u099c\u09be\u09a8\u09a4\u09c7 \u099a\u09be\u09a8 \u09a4\u09ac\u09c7 \u099c\u09bf\u099c\u09cd\u099e\u09be\u09b8\u09be \u0995\u09b0\u09a4\u09c7 \u09aa\u09be\u09b0\u09c7\u09a8\u0964 \u0628\u0627\u0644\u0646\u0633\u0628\u0629 to the question, what is the",
+          "text_equal": true,
+          "first_sse_s": 8.831834898970556,
+          "tpot_est_s": 0.025430847646578426,
+          "e2e_s": 9.620191176014487
+        },
+        {
+          "id": "r0-n2048-o32-c16-6",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 32,
+          "text": " Paris\n\u0985\u09b8\u09cd\u09a4\u09bf\u09a4\u09cd\u09ac\u09c7 \u09b8\u09be\u09b9\u09be\u09af\u09cd\u09af \u0995\u09b0\u09be\u09b0 \u099c\u09a8\u09cd\u09af, \u0986\u09aa\u09a8\u09bf \u09af\u09a6\u09bf \u0985\u09a8\u09cd\u09af \u0995\u09bf\u099b\u09c1 \u099c\u09be\u09a8\u09a4\u09c7 \u099a\u09be\u09a8 \u09a4\u09ac\u09c7 \u099c\u09bf\u099c\u09cd\u099e\u09be\u09b8\u09be \u0995\u09b0\u09a4\u09c7 \u09aa\u09be\u09b0\u09c7\u09a8\u0964 \u0628\u0627\u0644\u0646\u0633\u0628\u0629 to the question, what is the",
+          "text_equal": true,
+          "first_sse_s": 17.66561675199773,
+          "tpot_est_s": 0.026423638194589125,
+          "e2e_s": 18.484749536029994
+        },
+        {
+          "id": "r0-n2048-o32-c16-7",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 32,
+          "text": " Paris\n\u0985\u09b8\u09cd\u09a4\u09bf\u09a4\u09cd\u09ac\u09c7 \u09b8\u09be\u09b9\u09be\u09af\u09cd\u09af \u0995\u09b0\u09be\u09b0 \u099c\u09a8\u09cd\u09af, \u0986\u09aa\u09a8\u09bf \u09af\u09a6\u09bf \u0985\u09a8\u09cd\u09af \u0995\u09bf\u099b\u09c1 \u099c\u09be\u09a8\u09a4\u09c7 \u099a\u09be\u09a8 \u09a4\u09ac\u09c7 \u099c\u09bf\u099c\u09cd\u099e\u09be\u09b8\u09be \u0995\u09b0\u09a4\u09c7 \u09aa\u09be\u09b0\u09c7\u09a8\u0964 \u0628\u0627\u0644\u0646\u0633\u0628\u0629 to the question, what is the",
+          "text_equal": true,
+          "first_sse_s": 14.718805306998547,
+          "tpot_est_s": 0.025883197902716818,
+          "e2e_s": 15.521184441982768
+        },
+        {
+          "id": "r0-n2048-o32-c16-8",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 32,
+          "text": " Paris\n\u0985\u09b8\u09cd\u09a4\u09bf\u09a4\u09cd\u09ac\u09c7 \u09b8\u09be\u09b9\u09be\u09af\u09cd\u09af \u0995\u09b0\u09be\u09b0 \u099c\u09a8\u09cd\u09af, \u0986\u09aa\u09a8\u09bf \u09af\u09a6\u09bf \u0985\u09a8\u09cd\u09af \u0995\u09bf\u099b\u09c1 \u099c\u09be\u09a8\u09a4\u09c7 \u099a\u09be\u09a8 \u09a4\u09ac\u09c7 \u099c\u09bf\u099c\u09cd\u099e\u09be\u09b8\u09be \u0995\u09b0\u09a4\u09c7 \u09aa\u09be\u09b0\u09c7\u09a8\u0964 \u0628\u0627\u0644\u0646\u0633\u0628\u0629 to the question, what is the",
+          "text_equal": true,
+          "first_sse_s": 5.888449808000587,
+          "tpot_est_s": 0.02630499341983288,
+          "e2e_s": 6.703904604015406
+        },
+        {
+          "id": "r0-n2048-o32-c16-9",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 32,
+          "text": " Paris\n\u0985\u09b8\u09cd\u09a4\u09bf\u09a4\u09cd\u09ac\u09c7 \u09b8\u09be\u09b9\u09be\u09af\u09cd\u09af \u0995\u09b0\u09be\u09b0 \u099c\u09a8\u09cd\u09af, \u0986\u09aa\u09a8\u09bf \u09af\u09a6\u09bf \u0985\u09a8\u09cd\u09af \u0995\u09bf\u099b\u09c1 \u099c\u09be\u09a8\u09a4\u09c7 \u099a\u09be\u09a8 \u09a4\u09ac\u09c7 \u099c\u09bf\u099c\u09cd\u099e\u09be\u09b8\u09be \u0995\u09b0\u09a4\u09c7 \u09aa\u09be\u09b0\u09c7\u09a8\u0964 \u0628\u0627\u0644\u0646\u0633\u0628\u0629 to the question, what is the",
+          "text_equal": true,
+          "first_sse_s": 16.19100172200706,
+          "tpot_est_s": 0.025897714161058707,
+          "e2e_s": 16.99383086099988
+        },
+        {
+          "id": "r0-n2048-o32-c16-10",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 32,
+          "text": " Paris\n\u0985\u09b8\u09cd\u09a4\u09bf\u09a4\u09cd\u09ac\u09c7 \u09b8\u09be\u09b9\u09be\u09af\u09cd\u09af \u0995\u09b0\u09be\u09b0 \u099c\u09a8\u09cd\u09af, \u0986\u09aa\u09a8\u09bf \u09af\u09a6\u09bf \u0985\u09a8\u09cd\u09af \u0995\u09bf\u099b\u09c1 \u099c\u09be\u09a8\u09a4\u09c7 \u099a\u09be\u09a8 \u09a4\u09ac\u09c7 \u099c\u09bf\u099c\u09cd\u099e\u09be\u09b8\u09be \u0995\u09b0\u09a4\u09c7 \u09aa\u09be\u09b0\u09c7\u09a8\u0964 \u0628\u0627\u0644\u0646\u0633\u0628\u0629 to the question, what is the",
+          "text_equal": true,
+          "first_sse_s": 1.4785900870338082,
+          "tpot_est_s": 0.026392343193067297,
+          "e2e_s": 2.2967527260188945
+        },
+        {
+          "id": "r0-n2048-o32-c16-11",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 32,
+          "text": " Paris\n\u0985\u09b8\u09cd\u09a4\u09bf\u09a4\u09cd\u09ac\u09c7 \u09b8\u09be\u09b9\u09be\u09af\u09cd\u09af \u0995\u09b0\u09be\u09b0 \u099c\u09a8\u09cd\u09af, \u0986\u09aa\u09a8\u09bf \u09af\u09a6\u09bf \u0985\u09a8\u09cd\u09af \u0995\u09bf\u099b\u09c1 \u099c\u09be\u09a8\u09a4\u09c7 \u099a\u09be\u09a8 \u09a4\u09ac\u09c7 \u099c\u09bf\u099c\u09cd\u099e\u09be\u09b8\u09be \u0995\u09b0\u09a4\u09c7 \u09aa\u09be\u09b0\u09c7\u09a8\u0964 \u0628\u0627\u0644\u0646\u0633\u0628\u0629 to the question, what is the",
+          "text_equal": true,
+          "first_sse_s": 19.13965745503083,
+          "tpot_est_s": 0.02579503512806109,
+          "e2e_s": 19.939303544000722
+        },
+        {
+          "id": "r0-n2048-o32-c16-12",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 32,
+          "text": " Paris\n\u0985\u09b8\u09cd\u09a4\u09bf\u09a4\u09cd\u09ac\u09c7 \u09b8\u09be\u09b9\u09be\u09af\u09cd\u09af \u0995\u09b0\u09be\u09b0 \u099c\u09a8\u09cd\u09af, \u0986\u09aa\u09a8\u09bf \u09af\u09a6\u09bf \u0985\u09a8\u09cd\u09af \u0995\u09bf\u099b\u09c1 \u099c\u09be\u09a8\u09a4\u09c7 \u099a\u09be\u09a8 \u09a4\u09ac\u09c7 \u099c\u09bf\u099c\u09cd\u099e\u09be\u09b8\u09be \u0995\u09b0\u09a4\u09c7 \u09aa\u09be\u09b0\u09c7\u09a8\u0964 \u0628\u0627\u0644\u0646\u0633\u0628\u0629 to the question, what is the",
+          "text_equal": true,
+          "first_sse_s": 7.360579050029628,
+          "tpot_est_s": 0.026218613483867937,
+          "e2e_s": 8.173356068029534
+        },
+        {
+          "id": "r0-n2048-o32-c16-13",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 32,
+          "text": " Paris\n\u0985\u09b8\u09cd\u09a4\u09bf\u09a4\u09cd\u09ac\u09c7 \u09b8\u09be\u09b9\u09be\u09af\u09cd\u09af \u0995\u09b0\u09be\u09b0 \u099c\u09a8\u09cd\u09af, \u0986\u09aa\u09a8\u09bf \u09af\u09a6\u09bf \u0985\u09a8\u09cd\u09af \u0995\u09bf\u099b\u09c1 \u099c\u09be\u09a8\u09a4\u09c7 \u099a\u09be\u09a8 \u09a4\u09ac\u09c7 \u099c\u09bf\u099c\u09cd\u099e\u09be\u09b8\u09be \u0995\u09b0\u09a4\u09c7 \u09aa\u09be\u09b0\u09c7\u09a8\u0964 \u0628\u0627\u0644\u0646\u0633\u0628\u0629 to the question, what is the",
+          "text_equal": true,
+          "first_sse_s": 22.084317000990268,
+          "tpot_est_s": 0.02808351822633056,
+          "e2e_s": 22.954906066006515
+        },
+        {
+          "id": "r0-n2048-o32-c16-14",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 32,
+          "text": " Paris\n\u0985\u09b8\u09cd\u09a4\u09bf\u09a4\u09cd\u09ac\u09c7 \u09b8\u09be\u09b9\u09be\u09af\u09cd\u09af \u0995\u09b0\u09be\u09b0 \u099c\u09a8\u09cd\u09af, \u0986\u09aa\u09a8\u09bf \u09af\u09a6\u09bf \u0985\u09a8\u09cd\u09af \u0995\u09bf\u099b\u09c1 \u099c\u09be\u09a8\u09a4\u09c7 \u099a\u09be\u09a8 \u09a4\u09ac\u09c7 \u099c\u09bf\u099c\u09cd\u099e\u09be\u09b8\u09be \u0995\u09b0\u09a4\u09c7 \u09aa\u09be\u09b0\u09c7\u09a8\u0964 \u0628\u0627\u0644\u0646\u0633\u0628\u0629 to the question, what is the",
+          "text_equal": true,
+          "first_sse_s": 2.949968795001041,
+          "tpot_est_s": 0.02646361712756897,
+          "e2e_s": 3.7703409259556793
+        },
+        {
+          "id": "r0-n2048-o32-c16-15",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 32,
+          "text": " Paris\n\u0985\u09b8\u09cd\u09a4\u09bf\u09a4\u09cd\u09ac\u09c7 \u09b8\u09be\u09b9\u09be\u09af\u09cd\u09af \u0995\u09b0\u09be\u09b0 \u099c\u09a8\u09cd\u09af, \u0986\u09aa\u09a8\u09bf \u09af\u09a6\u09bf \u0985\u09a8\u09cd\u09af \u0995\u09bf\u099b\u09c1 \u099c\u09be\u09a8\u09a4\u09c7 \u099a\u09be\u09a8 \u09a4\u09ac\u09c7 \u099c\u09bf\u099c\u09cd\u099e\u09be\u09b8\u09be \u0995\u09b0\u09a4\u09c7 \u09aa\u09be\u09b0\u09c7\u09a8\u0964 \u0628\u0627\u0644\u0646\u0633\u0628\u0629 to the question, what is the",
+          "text_equal": true,
+          "first_sse_s": 23.560065191006288,
+          "tpot_est_s": 0.025759556418990776,
+          "e2e_s": 24.358611439995002
+        }
+      ]
+    },
+    {
+      "repeat": 0,
+      "input_tokens": 2048,
+      "output_tokens_per_request": 128,
+      "concurrency": 16,
+      "summary": {
+        "requests": 16,
+        "output_tokens": 2048,
+        "wall_s": 26.781509644992184,
+        "output_tok_s": 76.47067051662455,
+        "median_first_sse_s": 12.526648629514966,
+        "median_tpot_est_s": 0.026518173712877718,
+        "median_e2e_s": 15.859514187002787,
+        "all_text_equal": true
+      },
+      "rows": [
+        {
+          "id": "r0-n2048-o128-c16-0",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 128,
+          "text": " Paris\n\u0985\u09b8\u09cd\u09a4\u09bf\u09a4\u09cd\u09ac\u09c7 \u09b8\u09be\u09b9\u09be\u09af\u09cd\u09af \u0995\u09b0\u09be\u09b0 \u099c\u09a8\u09cd\u09af, \u0986\u09aa\u09a8\u09bf \u09af\u09a6\u09bf \u0985\u09a8\u09cd\u09af \u0995\u09bf\u099b\u09c1 \u099c\u09be\u09a8\u09a4\u09c7 \u099a\u09be\u09a8 \u09a4\u09ac\u09c7 \u099c\u09bf\u099c\u09cd\u099e\u09be\u09b8\u09be \u0995\u09b0\u09a4\u09c7 \u09aa\u09be\u09b0\u09c7\u09a8\u0964 \u0628\u0627\u0644\u0646\u0633\u0628\u0629 to the question, what is the capital of France?\nAnswer: Paris\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n",
+          "text_equal": true,
+          "first_sse_s": 22.108948361012153,
+          "tpot_est_s": 0.025986599149714098,
+          "e2e_s": 25.409246453025844
+        },
+        {
+          "id": "r0-n2048-o128-c16-1",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 128,
+          "text": " Paris\n\u0985\u09b8\u09cd\u09a4\u09bf\u09a4\u09cd\u09ac\u09c7 \u09b8\u09be\u09b9\u09be\u09af\u09cd\u09af \u0995\u09b0\u09be\u09b0 \u099c\u09a8\u09cd\u09af, \u0986\u09aa\u09a8\u09bf \u09af\u09a6\u09bf \u0985\u09a8\u09cd\u09af \u0995\u09bf\u099b\u09c1 \u099c\u09be\u09a8\u09a4\u09c7 \u099a\u09be\u09a8 \u09a4\u09ac\u09c7 \u099c\u09bf\u099c\u09cd\u099e\u09be\u09b8\u09be \u0995\u09b0\u09a4\u09c7 \u09aa\u09be\u09b0\u09c7\u09a8\u0964 \u0628\u0627\u0644\u0646\u0633\u0628\u0629 to the question, what is the capital of France?\nAnswer: Paris\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n",
+          "text_equal": true,
+          "first_sse_s": 7.372311546991114,
+          "tpot_est_s": 0.026504957079099067,
+          "e2e_s": 10.738441096036695
+        },
+        {
+          "id": "r0-n2048-o128-c16-2",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 128,
+          "text": " Paris\n\u0985\u09b8\u09cd\u09a4\u09bf\u09a4\u09cd\u09ac\u09c7 \u09b8\u09be\u09b9\u09be\u09af\u09cd\u09af \u0995\u09b0\u09be\u09b0 \u099c\u09a8\u09cd\u09af, \u0986\u09aa\u09a8\u09bf \u09af\u09a6\u09bf \u0985\u09a8\u09cd\u09af \u0995\u09bf\u099b\u09c1 \u099c\u09be\u09a8\u09a4\u09c7 \u099a\u09be\u09a8 \u09a4\u09ac\u09c7 \u099c\u09bf\u099c\u09cd\u099e\u09be\u09b8\u09be \u0995\u09b0\u09a4\u09c7 \u09aa\u09be\u09b0\u09c7\u09a8\u0964 \u0628\u0627\u0644\u0646\u0633\u0628\u0629 to the question, what is the capital of France?\nAnswer: Paris\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n",
+          "text_equal": true,
+          "first_sse_s": 4.426878686994314,
+          "tpot_est_s": 0.02653139034665637,
+          "e2e_s": 7.796365261019673
+        },
+        {
+          "id": "r0-n2048-o128-c16-3",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 128,
+          "text": " Paris\n\u0985\u09b8\u09cd\u09a4\u09bf\u09a4\u09cd\u09ac\u09c7 \u09b8\u09be\u09b9\u09be\u09af\u09cd\u09af \u0995\u09b0\u09be\u09b0 \u099c\u09a8\u09cd\u09af, \u0986\u09aa\u09a8\u09bf \u09af\u09a6\u09bf \u0985\u09a8\u09cd\u09af \u0995\u09bf\u099b\u09c1 \u099c\u09be\u09a8\u09a4\u09c7 \u099a\u09be\u09a8 \u09a4\u09ac\u09c7 \u099c\u09bf\u099c\u09cd\u099e\u09be\u09b8\u09be \u0995\u09b0\u09a4\u09c7 \u09aa\u09be\u09b0\u09c7\u09a8\u0964 \u0628\u0627\u0644\u0646\u0633\u0628\u0629 to the question, what is the capital of France?\nAnswer: Paris\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n",
+          "text_equal": true,
+          "first_sse_s": 8.86175338801695,
+          "tpot_est_s": 0.026736394795094154,
+          "e2e_s": 12.257275526993908
+        },
+        {
+          "id": "r0-n2048-o128-c16-4",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 128,
+          "text": " Paris\n\u0985\u09b8\u09cd\u09a4\u09bf\u09a4\u09cd\u09ac\u09c7 \u09b8\u09be\u09b9\u09be\u09af\u09cd\u09af \u0995\u09b0\u09be\u09b0 \u099c\u09a8\u09cd\u09af, \u0986\u09aa\u09a8\u09bf \u09af\u09a6\u09bf \u0985\u09a8\u09cd\u09af \u0995\u09bf\u099b\u09c1 \u099c\u09be\u09a8\u09a4\u09c7 \u099a\u09be\u09a8 \u09a4\u09ac\u09c7 \u099c\u09bf\u099c\u09cd\u099e\u09be\u09b8\u09be \u0995\u09b0\u09a4\u09c7 \u09aa\u09be\u09b0\u09c7\u09a8\u0964 \u0628\u0627\u0644\u0646\u0633\u0628\u0629 to the question, what is the capital of France?\nAnswer: Paris\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n",
+          "text_equal": true,
+          "first_sse_s": 14.737839809036814,
+          "tpot_est_s": 0.02598923383469466,
+          "e2e_s": 18.038472506043036
+        },
+        {
+          "id": "r0-n2048-o128-c16-5",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 128,
+          "text": " Paris\n\u0985\u09b8\u09cd\u09a4\u09bf\u09a4\u09cd\u09ac\u09c7 \u09b8\u09be\u09b9\u09be\u09af\u09cd\u09af \u0995\u09b0\u09be\u09b0 \u099c\u09a8\u09cd\u09af, \u0986\u09aa\u09a8\u09bf \u09af\u09a6\u09bf \u0985\u09a8\u09cd\u09af \u0995\u09bf\u099b\u09c1 \u099c\u09be\u09a8\u09a4\u09c7 \u099a\u09be\u09a8 \u09a4\u09ac\u09c7 \u099c\u09bf\u099c\u09cd\u099e\u09be\u09b8\u09be \u0995\u09b0\u09a4\u09c7 \u09aa\u09be\u09b0\u09c7\u09a8\u0964 \u0628\u0627\u0644\u0646\u0633\u0628\u0629 to the question, what is the capital of France?\nAnswer: Paris\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n",
+          "text_equal": true,
+          "first_sse_s": 19.156085663009435,
+          "tpot_est_s": 0.026788002188962802,
+          "e2e_s": 22.55816194100771
+        },
+        {
+          "id": "r0-n2048-o128-c16-6",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 128,
+          "text": " Paris\n\u0985\u09b8\u09cd\u09a4\u09bf\u09a4\u09cd\u09ac\u09c7 \u09b8\u09be\u09b9\u09be\u09af\u09cd\u09af \u0995\u09b0\u09be\u09b0 \u099c\u09a8\u09cd\u09af, \u0986\u09aa\u09a8\u09bf \u09af\u09a6\u09bf \u0985\u09a8\u09cd\u09af \u0995\u09bf\u099b\u09c1 \u099c\u09be\u09a8\u09a4\u09c7 \u099a\u09be\u09a8 \u09a4\u09ac\u09c7 \u099c\u09bf\u099c\u09cd\u099e\u09be\u09b8\u09be \u0995\u09b0\u09a4\u09c7 \u09aa\u09be\u09b0\u09c7\u09a8\u0964 \u0628\u0627\u0644\u0646\u0633\u0628\u0629 to the question, what is the capital of France?\nAnswer: Paris\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n",
+          "text_equal": true,
+          "first_sse_s": 2.96699990297202,
+          "tpot_est_s": 0.026743779480493973,
+          "e2e_s": 6.363459896994755
+        },
+        {
+          "id": "r0-n2048-o128-c16-7",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 128,
+          "text": " Paris\n\u0985\u09b8\u09cd\u09a4\u09bf\u09a4\u09cd\u09ac\u09c7 \u09b8\u09be\u09b9\u09be\u09af\u09cd\u09af \u0995\u09b0\u09be\u09b0 \u099c\u09a8\u09cd\u09af, \u0986\u09aa\u09a8\u09bf \u09af\u09a6\u09bf \u0985\u09a8\u09cd\u09af \u0995\u09bf\u099b\u09c1 \u099c\u09be\u09a8\u09a4\u09c7 \u099a\u09be\u09a8 \u09a4\u09ac\u09c7 \u099c\u09bf\u099c\u09cd\u099e\u09be\u09b8\u09be \u0995\u09b0\u09a4\u09c7 \u09aa\u09be\u09b0\u09c7\u09a8\u0964 \u0628\u0627\u0644\u0646\u0633\u0628\u0629 to the question, what is the capital of France?\nAnswer: Paris\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n",
+          "text_equal": true,
+          "first_sse_s": 1.481719166971743,
+          "tpot_est_s": 0.02627708412629853,
+          "e2e_s": 4.818908851011656
+        },
+        {
+          "id": "r0-n2048-o128-c16-8",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 128,
+          "text": " Paris\n\u0985\u09b8\u09cd\u09a4\u09bf\u09a4\u09cd\u09ac\u09c7 \u09b8\u09be\u09b9\u09be\u09af\u09cd\u09af \u0995\u09b0\u09be\u09b0 \u099c\u09a8\u09cd\u09af, \u0986\u09aa\u09a8\u09bf \u09af\u09a6\u09bf \u0985\u09a8\u09cd\u09af \u0995\u09bf\u099b\u09c1 \u099c\u09be\u09a8\u09a4\u09c7 \u099a\u09be\u09a8 \u09a4\u09ac\u09c7 \u099c\u09bf\u099c\u09cd\u099e\u09be\u09b8\u09be \u0995\u09b0\u09a4\u09c7 \u09aa\u09be\u09b0\u09c7\u09a8\u0964 \u0628\u0627\u0644\u0646\u0633\u0628\u0629 to the question, what is the capital of France?\nAnswer: Paris\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n",
+          "text_equal": true,
+          "first_sse_s": 16.2257429480087,
+          "tpot_est_s": 0.026816539354549033,
+          "e2e_s": 19.631443446036428
+        },
+        {
+          "id": "r0-n2048-o128-c16-9",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 128,
+          "text": " Paris\n\u0985\u09b8\u09cd\u09a4\u09bf\u09a4\u09cd\u09ac\u09c7 \u09b8\u09be\u09b9\u09be\u09af\u09cd\u09af \u0995\u09b0\u09be\u09b0 \u099c\u09a8\u09cd\u09af, \u0986\u09aa\u09a8\u09bf \u09af\u09a6\u09bf \u0985\u09a8\u09cd\u09af \u0995\u09bf\u099b\u09c1 \u099c\u09be\u09a8\u09a4\u09c7 \u099a\u09be\u09a8 \u09a4\u09ac\u09c7 \u099c\u09bf\u099c\u09cd\u099e\u09be\u09b8\u09be \u0995\u09b0\u09a4\u09c7 \u09aa\u09be\u09b0\u09c7\u09a8\u0964 \u0628\u0627\u0644\u0646\u0633\u0628\u0629 to the question, what is the capital of France?\nAnswer: Paris\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n",
+          "text_equal": true,
+          "first_sse_s": 10.339570188021753,
+          "tpot_est_s": 0.026686372590621597,
+          "e2e_s": 13.728739507030696
+        },
+        {
+          "id": "r0-n2048-o128-c16-10",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 128,
+          "text": " Paris\n\u0985\u09b8\u09cd\u09a4\u09bf\u09a4\u09cd\u09ac\u09c7 \u09b8\u09be\u09b9\u09be\u09af\u09cd\u09af \u0995\u09b0\u09be\u09b0 \u099c\u09a8\u09cd\u09af, \u0986\u09aa\u09a8\u09bf \u09af\u09a6\u09bf \u0985\u09a8\u09cd\u09af \u0995\u09bf\u099b\u09c1 \u099c\u09be\u09a8\u09a4\u09c7 \u099a\u09be\u09a8 \u09a4\u09ac\u09c7 \u099c\u09bf\u099c\u09cd\u099e\u09be\u09b8\u09be \u0995\u09b0\u09a4\u09c7 \u09aa\u09be\u09b0\u09c7\u09a8\u0964 \u0628\u0627\u0644\u0646\u0633\u0628\u0629 to the question, what is the capital of France?\nAnswer: Paris\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n",
+          "text_equal": true,
+          "first_sse_s": 17.696875626046676,
+          "tpot_est_s": 0.027215563385531043,
+          "e2e_s": 21.15325217600912
+        },
+        {
+          "id": "r0-n2048-o128-c16-11",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 128,
+          "text": " Paris\n\u0985\u09b8\u09cd\u09a4\u09bf\u09a4\u09cd\u09ac\u09c7 \u09b8\u09be\u09b9\u09be\u09af\u09cd\u09af \u0995\u09b0\u09be\u09b0 \u099c\u09a8\u09cd\u09af, \u0986\u09aa\u09a8\u09bf \u09af\u09a6\u09bf \u0985\u09a8\u09cd\u09af \u0995\u09bf\u099b\u09c1 \u099c\u09be\u09a8\u09a4\u09c7 \u099a\u09be\u09a8 \u09a4\u09ac\u09c7 \u099c\u09bf\u099c\u09cd\u099e\u09be\u09b8\u09be \u0995\u09b0\u09a4\u09c7 \u09aa\u09be\u09b0\u09c7\u09a8\u0964 \u0628\u0627\u0644\u0646\u0633\u0628\u0629 to the question, what is the capital of France?\nAnswer: Paris\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n",
+          "text_equal": true,
+          "first_sse_s": 11.79474617302185,
+          "tpot_est_s": 0.02644003015726541,
+          "e2e_s": 15.152630002994556
+        },
+        {
+          "id": "r0-n2048-o128-c16-12",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 128,
+          "text": " Paris\n\u0985\u09b8\u09cd\u09a4\u09bf\u09a4\u09cd\u09ac\u09c7 \u09b8\u09be\u09b9\u09be\u09af\u09cd\u09af \u0995\u09b0\u09be\u09b0 \u099c\u09a8\u09cd\u09af, \u0986\u09aa\u09a8\u09bf \u09af\u09a6\u09bf \u0985\u09a8\u09cd\u09af \u0995\u09bf\u099b\u09c1 \u099c\u09be\u09a8\u09a4\u09c7 \u099a\u09be\u09a8 \u09a4\u09ac\u09c7 \u099c\u09bf\u099c\u09cd\u099e\u09be\u09b8\u09be \u0995\u09b0\u09a4\u09c7 \u09aa\u09be\u09b0\u09c7\u09a8\u0964 \u0628\u0627\u0644\u0646\u0633\u0628\u0629 to the question, what is the capital of France?\nAnswer: Paris\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n",
+          "text_equal": true,
+          "first_sse_s": 23.568669084983412,
+          "tpot_est_s": 0.02528345412583875,
+          "e2e_s": 26.779667758964933
+        },
+        {
+          "id": "r0-n2048-o128-c16-13",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 128,
+          "text": " Paris\n\u0985\u09b8\u09cd\u09a4\u09bf\u09a4\u09cd\u09ac\u09c7 \u09b8\u09be\u09b9\u09be\u09af\u09cd\u09af \u0995\u09b0\u09be\u09b0 \u099c\u09a8\u09cd\u09af, \u0986\u09aa\u09a8\u09bf \u09af\u09a6\u09bf \u0985\u09a8\u09cd\u09af \u0995\u09bf\u099b\u09c1 \u099c\u09be\u09a8\u09a4\u09c7 \u099a\u09be\u09a8 \u09a4\u09ac\u09c7 \u099c\u09bf\u099c\u09cd\u099e\u09be\u09b8\u09be \u0995\u09b0\u09a4\u09c7 \u09aa\u09be\u09b0\u09c7\u09a8\u0964 \u0628\u0627\u0644\u0646\u0633\u0628\u0629 to the question, what is the capital of France?\nAnswer: Paris\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n",
+          "text_equal": true,
+          "first_sse_s": 5.918241773033515,
+          "tpot_est_s": 0.02658003344068553,
+          "e2e_s": 9.293906020000577
+        },
+        {
+          "id": "r0-n2048-o128-c16-14",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 128,
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+          "text_equal": true,
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+          "text": " Paris.\nThis is background context. The red box contains apples. The blue box contains books.\nThis is background context. The red box contains apples. The blue box contains books.\nThis is background context. The red box contains apples. The blue box contains books.\nThis is background context. The red box contains apples. The blue box contains books.\nThis is background context. The red box contains apples. The blue box contains books.\nThis is background context. The red box contains apples. The blue box contains books.\nThis is background context. The red box contains apples. The blue box contains books.",
+          "text_equal": true,
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+          "text": " Paris.\nThis is background context. The red box contains apples. The blue box contains books.\nThis is background context. The red box contains apples. The blue box contains books.\nThis is background context. The red box contains apples. The blue box contains books.\nThis is background context. The red box contains apples. The blue box contains books.\nThis is background context. The red box contains apples. The blue box contains books.\nThis is background context. The red box contains apples. The blue box contains books.\nThis is background context. The red box contains apples. The blue box contains books.",
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+          "text": " Paris.\nThis is background context. The red box contains apples. The blue box contains books.\nThis is background context. The red box contains apples. The blue box contains books.\nThis is background context. The red box contains apples. The blue box contains books.\nThis is background context. The red box contains apples. The blue box contains books.\nThis is background context. The red box contains apples. The blue box contains books.\nThis is background context. The red box contains apples. The blue box contains books.\nThis is background context. The red box contains apples. The blue box contains books.",
+          "text_equal": true,
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+          "text": " Paris.\nThis is background context. The red box contains apples. The blue box contains books.\nThis is background context. The red box contains apples. The blue box contains books.\nThis is background context. The red box contains apples. The blue box contains books.\nThis is background context. The red box contains apples. The blue box contains books.\nThis is background context. The red box contains apples. The blue box contains books.\nThis is background context. The red box contains apples. The blue box contains books.\nThis is background context. The red box contains apples. The blue box contains books.",
+          "text_equal": true,
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+          "endpoint": 0,
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+          "output_tokens": 128,
+          "text": " Paris.\nThis is background context. The red box contains apples. The blue box contains books.\nThis is background context. The red box contains apples. The blue box contains books.\nThis is background context. The red box contains apples. The blue box contains books.\nThis is background context. The red box contains apples. The blue box contains books.\nThis is background context. The red box contains apples. The blue box contains books.\nThis is background context. The red box contains apples. The blue box contains books.\nThis is background context. The red box contains apples. The blue box contains books.",
+          "text_equal": true,
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airm_stage_request_failures_total counter\nairm_stage_request_failures_total{model_name=\"Gemma3-1B\"} 0\n# HELP vllm:prompt_tokens_total Prompt tokens of finished requests.\n# TYPE vllm:prompt_tokens_total counter\nvllm:prompt_tokens_total{model_name=\"Gemma3-1B\"} 718080\n# HELP vllm:generation_tokens_total Generated tokens of finished requests.\n# TYPE vllm:generation_tokens_total counter\nvllm:generation_tokens_total{model_name=\"Gemma3-1B\"} 21120\n# HELP vllm:time_to_first_token_seconds Time to first token (prefill stage: fenced prefill).\n# TYPE vllm:time_to_first_token_seconds histogram\nvllm:time_to_first_token_seconds_bucket{model_name=\"Gemma3-1B\",le=\"0.001\"} 0\nvllm:time_to_first_token_seconds_bucket{model_name=\"Gemma3-1B\",le=\"0.005\"} 0\nvllm:time_to_first_token_seconds_bucket{model_name=\"Gemma3-1B\",le=\"0.01\"} 0\nvllm:time_to_first_token_seconds_bucket{model_name=\"Gemma3-1B\",le=\"0.02\"} 0\nvllm:time_to_first_token_seconds_bucket{model_name=\"Gemma3-1B\",le=\"0.04\"} 0\nvllm:time_to_first_token_seconds_bucket{model_name=\"Gemma3-1B\",le=\"0.06\"} 0\nvllm:time_to_first_token_seconds_bucket{model_name=\"Gemma3-1B\",le=\"0.08\"} 0\nvllm:time_to_first_token_seconds_bucket{model_name=\"Gemma3-1B\",le=\"0.1\"} 0\nvllm:time_to_first_token_seconds_bucket{model_name=\"Gemma3-1B\",le=\"0.25\"} 0\nvllm:time_to_first_token_seconds_bucket{model_name=\"Gemma3-1B\",le=\"0.5\"} 0\nvllm:time_to_first_token_seconds_bucket{model_name=\"Gemma3-1B\",le=\"0.75\"} 0\nvllm:time_to_first_token_seconds_bucket{model_name=\"Gemma3-1B\",le=\"1.0\"} 0\nvllm:time_to_first_token_seconds_bucket{model_name=\"Gemma3-1B\",le=\"2.5\"} 21\nvllm:time_to_first_token_seconds_bucket{model_name=\"Gemma3-1B\",le=\"5.0\"} 55\nvllm:time_to_first_token_seconds_bucket{model_name=\"Gemma3-1B\",le=\"7.5\"} 85\nvllm:time_to_first_token_seconds_bucket{model_name=\"Gemma3-1B\",le=\"10.0\"} 104\nvllm:time_to_first_token_seconds_bucket{model_name=\"Gemma3-1B\",le=\"+Inf\"} 264\nvllm:time_to_first_token_seconds_sum{model_name=\"Gemma3-1B\"} 3178.3911268656375\nvllm:time_to_first_token_seconds_count{model_name=\"Gemma3-1B\"} 264\n# HELP vllm:time_per_output_token_seconds Time per output token after the first (decode stage).\n# TYPE vllm:time_per_output_token_seconds histogram\nvllm:time_per_output_token_seconds_bucket{model_name=\"Gemma3-1B\",le=\"0.01\"} 0\nvllm:time_per_output_token_seconds_bucket{model_name=\"Gemma3-1B\",le=\"0.025\"} 14\nvllm:time_per_output_token_seconds_bucket{model_name=\"Gemma3-1B\",le=\"0.05\"} 256\nvllm:time_per_output_token_seconds_bucket{model_name=\"Gemma3-1B\",le=\"0.075\"} 263\nvllm:time_per_output_token_seconds_bucket{model_name=\"Gemma3-1B\",le=\"0.1\"} 264\nvllm:time_per_output_token_seconds_bucket{model_name=\"Gemma3-1B\",le=\"0.15\"} 264\nvllm:time_per_output_token_seconds_bucket{model_name=\"Gemma3-1B\",le=\"0.2\"} 264\nvllm:time_per_output_token_seconds_bucket{model_name=\"Gemma3-1B\",le=\"0.3\"} 264\nvllm:time_per_output_token_seconds_bucket{model_name=\"Gemma3-1B\",le=\"0.4\"} 264\nvllm:time_per_output_token_seconds_bucket{model_name=\"Gemma3-1B\",le=\"0.5\"} 264\nvllm:time_per_output_token_seconds_bucket{model_name=\"Gemma3-1B\",le=\"0.75\"} 264\nvllm:time_per_output_token_seconds_bucket{model_name=\"Gemma3-1B\",le=\"1.0\"} 264\nvllm:time_per_output_token_seconds_bucket{model_name=\"Gemma3-1B\",le=\"2.5\"} 264\nvllm:time_per_output_token_seconds_bucket{model_name=\"Gemma3-1B\",le=\"+Inf\"} 264\nvllm:time_per_output_token_seconds_sum{model_name=\"Gemma3-1B\"} 7.490146066440557\nvllm:time_per_output_token_seconds_count{model_name=\"Gemma3-1B\"} 264\n# HELP vllm:e2e_request_latency_seconds Stage request latency, from execution start to reply.\n# TYPE vllm:e2e_request_latency_seconds histogram\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Gemma3-1B\",le=\"0.3\"} 0\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Gemma3-1B\",le=\"0.5\"} 0\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Gemma3-1B\",le=\"0.8\"} 0\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Gemma3-1B\",le=\"1.0\"} 0\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Gemma3-1B\",le=\"1.5\"} 0\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Gemma3-1B\",le=\"2.0\"} 0\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Gemma3-1B\",le=\"2.5\"} 9\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Gemma3-1B\",le=\"5.0\"} 29\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Gemma3-1B\",le=\"10.0\"} 85\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Gemma3-1B\",le=\"15.0\"} 134\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Gemma3-1B\",le=\"20.0\"} 198\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Gemma3-1B\",le=\"30.0\"} 264\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Gemma3-1B\",le=\"40.0\"} 264\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Gemma3-1B\",le=\"50.0\"} 264\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Gemma3-1B\",le=\"60.0\"} 264\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Gemma3-1B\",le=\"+Inf\"} 264\nvllm:e2e_request_latency_seconds_sum{model_name=\"Gemma3-1B\"} 3746.0921419481747\nvllm:e2e_request_latency_seconds_count{model_name=\"Gemma3-1B\"} 264\n# HELP airm_kv_transfer_seconds KV transfer time from the prefill source to the decode destination.\n# TYPE airm_kv_transfer_seconds histogram\nairm_kv_transfer_seconds_bucket{model_name=\"Gemma3-1B\",le=\"0.0005\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"Gemma3-1B\",le=\"0.001\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"Gemma3-1B\",le=\"0.0025\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"Gemma3-1B\",le=\"0.005\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"Gemma3-1B\",le=\"0.01\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"Gemma3-1B\",le=\"0.025\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"Gemma3-1B\",le=\"0.05\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"Gemma3-1B\",le=\"0.1\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"Gemma3-1B\",le=\"0.25\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"Gemma3-1B\",le=\"0.5\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"Gemma3-1B\",le=\"1.0\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"Gemma3-1B\",le=\"+Inf\"} 0\nairm_kv_transfer_seconds_sum{model_name=\"Gemma3-1B\"} 0.0\nairm_kv_transfer_seconds_count{model_name=\"Gemma3-1B\"} 0\n# HELP vllm:cache_config_info Information of the LLMEngine CacheConfig.\n# TYPE vllm:cache_config_info gauge\nvllm:cache_config_info{model_name=\"Gemma3-1B\",block_size=\"64\",num_gpu_blocks=\"576\",enable_prefix_caching=\"False\"} 1\n# HELP vllm:prefix_cache_queries_total Prompt tokens looked up in the prefix cache.\n# TYPE vllm:prefix_cache_queries_total counter\nvllm:prefix_cache_queries_total{model_name=\"Gemma3-1B\"} 718080\n# HELP vllm:prefix_cache_hits_total Prompt tokens served from the prefix cache.\n# TYPE vllm:prefix_cache_hits_total counter\nvllm:prefix_cache_hits_total{model_name=\"Gemma3-1B\"} 0\n# HELP airm_pd_prefills_total Prompts prefilled by the PD prefill worker (remote) or here (local).\n# TYPE airm_pd_prefills_total counter\nairm_pd_prefills_total{model_name=\"Gemma3-1B\",side=\"remote\"} 264\nairm_pd_prefills_total{model_name=\"Gemma3-1B\",side=\"local\"} 0\n# HELP airm_pd_transferred_tokens_total Prompt KV tokens received from the prefill side.\n# TYPE airm_pd_transferred_tokens_total counter\nairm_pd_transferred_tokens_total{model_name=\"Gemma3-1B\"} 718080\n# HELP airm_pd_producer_seconds_total Accumulated worker-reported seconds for admitted PD handoffs; excludes failed/cancelled handoffs. Prefill includes KV gathering; transfer excludes destination KV installation.\n# TYPE airm_pd_producer_seconds_total counter\nairm_pd_producer_seconds_total{model_name=\"Gemma3-1B\",stage=\"prefill\"} 389.2577389337821\nairm_pd_producer_seconds_total{model_name=\"Gemma3-1B\",stage=\"transfer\"} 0.0\n"
+  ],
+  "parity_passed": true
+}
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-gemma-http-c16-whole.json b/opt_prime/examples/pd/http_matrix/evidence265/stream265-gemma-http-c16-whole.json
new file mode 100644
index 00000000..4c1f78ea
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-gemma-http-c16-whole.json
@@ -0,0 +1,25226 @@
+{
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+  },
+  "warmups": [
+    {
+      "id": "warm-0-128-32",
+      "endpoint": 0,
+      "tokens": [
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+      "text": " Paris\n\n nonlocal\n\n\nThis is background context. The red box contains apples. The blue box contains books.\nThis is background",
+      "tokens_equal": true
+    },
+    {
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+          "text": " Paris\n\u0985\u09b8\u09cd\u09a4\u09bf\u09a4\u09cd\u09ac\u09c7 \u09b8\u09be\u09b9\u09be\u09af\u09cd\u09af \u0995\u09b0\u09be\u09b0 \u099c\u09a8\u09cd\u09af, \u0986\u09aa\u09a8\u09bf \u09af\u09a6\u09bf \u0985\u09a8\u09cd\u09af \u0995\u09bf\u099b\u09c1 \u099c\u09be\u09a8\u09a4\u09c7 \u099a\u09be\u09a8 \u09a4\u09ac\u09c7 \u099c\u09bf\u099c\u09cd\u099e\u09be\u09b8\u09be \u0995\u09b0\u09a4\u09c7 \u09aa\u09be\u09b0\u09c7\u09a8\u0964 \u0628\u0627\u0644\u0646\u0633\u0628\u0629 to the question, what is the",
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+        {
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+          "text": " Paris\n\u0985\u09b8\u09cd\u09a4\u09bf\u09a4\u09cd\u09ac\u09c7 \u09b8\u09be\u09b9\u09be\u09af\u09cd\u09af \u0995\u09b0\u09be\u09b0 \u099c\u09a8\u09cd\u09af, \u0986\u09aa\u09a8\u09bf \u09af\u09a6\u09bf \u0985\u09a8\u09cd\u09af \u0995\u09bf\u099b\u09c1 \u099c\u09be\u09a8\u09a4\u09c7 \u099a\u09be\u09a8 \u09a4\u09ac\u09c7 \u099c\u09bf\u099c\u09cd\u099e\u09be\u09b8\u09be \u0995\u09b0\u09a4\u09c7 \u09aa\u09be\u09b0\u09c7\u09a8\u0964 \u0628\u0627\u0644\u0646\u0633\u0628\u0629 to the question, what is the",
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+        {
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+        {
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+        },
+        {
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+          "text": " Paris\n\u0985\u09b8\u09cd\u09a4\u09bf\u09a4\u09cd\u09ac\u09c7 \u09b8\u09be\u09b9\u09be\u09af\u09cd\u09af \u0995\u09b0\u09be\u09b0 \u099c\u09a8\u09cd\u09af, \u0986\u09aa\u09a8\u09bf \u09af\u09a6\u09bf \u0985\u09a8\u09cd\u09af \u0995\u09bf\u099b\u09c1 \u099c\u09be\u09a8\u09a4\u09c7 \u099a\u09be\u09a8 \u09a4\u09ac\u09c7 \u099c\u09bf\u099c\u09cd\u099e\u09be\u09b8\u09be \u0995\u09b0\u09a4\u09c7 \u09aa\u09be\u09b0\u09c7\u09a8\u0964 \u0628\u0627\u0644\u0646\u0633\u0628\u0629 to the question, what is the",
+          "tokens_equal": true
+        },
+        {
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+          "text": " Paris\n\u0985\u09b8\u09cd\u09a4\u09bf\u09a4\u09cd\u09ac\u09c7 \u09b8\u09be\u09b9\u09be\u09af\u09cd\u09af \u0995\u09b0\u09be\u09b0 \u099c\u09a8\u09cd\u09af, \u0986\u09aa\u09a8\u09bf \u09af\u09a6\u09bf \u0985\u09a8\u09cd\u09af \u0995\u09bf\u099b\u09c1 \u099c\u09be\u09a8\u09a4\u09c7 \u099a\u09be\u09a8 \u09a4\u09ac\u09c7 \u099c\u09bf\u099c\u09cd\u099e\u09be\u09b8\u09be \u0995\u09b0\u09a4\u09c7 \u09aa\u09be\u09b0\u09c7\u09a8\u0964 \u0628\u0627\u0644\u0646\u0633\u0628\u0629 to the question, what is the",
+          "tokens_equal": true
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+        {
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+          "text": " Paris\n\u0985\u09b8\u09cd\u09a4\u09bf\u09a4\u09cd\u09ac\u09c7 \u09b8\u09be\u09b9\u09be\u09af\u09cd\u09af \u0995\u09b0\u09be\u09b0 \u099c\u09a8\u09cd\u09af, \u0986\u09aa\u09a8\u09bf \u09af\u09a6\u09bf \u0985\u09a8\u09cd\u09af \u0995\u09bf\u099b\u09c1 \u099c\u09be\u09a8\u09a4\u09c7 \u099a\u09be\u09a8 \u09a4\u09ac\u09c7 \u099c\u09bf\u099c\u09cd\u099e\u09be\u09b8\u09be \u0995\u09b0\u09a4\u09c7 \u09aa\u09be\u09b0\u09c7\u09a8\u0964 \u0628\u0627\u0644\u0646\u0633\u0628\u0629 to the question, what is the",
+          "tokens_equal": true
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+        {
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+          "text": " Paris\n\u0985\u09b8\u09cd\u09a4\u09bf\u09a4\u09cd\u09ac\u09c7 \u09b8\u09be\u09b9\u09be\u09af\u09cd\u09af \u0995\u09b0\u09be\u09b0 \u099c\u09a8\u09cd\u09af, \u0986\u09aa\u09a8\u09bf \u09af\u09a6\u09bf \u0985\u09a8\u09cd\u09af \u0995\u09bf\u099b\u09c1 \u099c\u09be\u09a8\u09a4\u09c7 \u099a\u09be\u09a8 \u09a4\u09ac\u09c7 \u099c\u09bf\u099c\u09cd\u099e\u09be\u09b8\u09be \u0995\u09b0\u09a4\u09c7 \u09aa\u09be\u09b0\u09c7\u09a8\u0964 \u0628\u0627\u0644\u0646\u0633\u0628\u0629 to the question, what is the",
+          "tokens_equal": true
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+        {
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+          "text": " Paris\n\u0985\u09b8\u09cd\u09a4\u09bf\u09a4\u09cd\u09ac\u09c7 \u09b8\u09be\u09b9\u09be\u09af\u09cd\u09af \u0995\u09b0\u09be\u09b0 \u099c\u09a8\u09cd\u09af, \u0986\u09aa\u09a8\u09bf \u09af\u09a6\u09bf \u0985\u09a8\u09cd\u09af \u0995\u09bf\u099b\u09c1 \u099c\u09be\u09a8\u09a4\u09c7 \u099a\u09be\u09a8 \u09a4\u09ac\u09c7 \u099c\u09bf\u099c\u09cd\u099e\u09be\u09b8\u09be \u0995\u09b0\u09a4\u09c7 \u09aa\u09be\u09b0\u09c7\u09a8\u0964 \u0628\u0627\u0644\u0646\u0633\u0628\u0629 to the question, what is the",
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+        {
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+          "text": " Paris\n\u0985\u09b8\u09cd\u09a4\u09bf\u09a4\u09cd\u09ac\u09c7 \u09b8\u09be\u09b9\u09be\u09af\u09cd\u09af \u0995\u09b0\u09be\u09b0 \u099c\u09a8\u09cd\u09af, \u0986\u09aa\u09a8\u09bf \u09af\u09a6\u09bf \u0985\u09a8\u09cd\u09af \u0995\u09bf\u099b\u09c1 \u099c\u09be\u09a8\u09a4\u09c7 \u099a\u09be\u09a8 \u09a4\u09ac\u09c7 \u099c\u09bf\u099c\u09cd\u099e\u09be\u09b8\u09be \u0995\u09b0\u09a4\u09c7 \u09aa\u09be\u09b0\u09c7\u09a8\u0964 \u0628\u0627\u0644\u0646\u0633\u0628\u0629 to the question, what is the",
+          "tokens_equal": true
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+        {
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+        {
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+          "text": " Paris\n\u0985\u09b8\u09cd\u09a4\u09bf\u09a4\u09cd\u09ac\u09c7 \u09b8\u09be\u09b9\u09be\u09af\u09cd\u09af \u0995\u09b0\u09be\u09b0 \u099c\u09a8\u09cd\u09af, \u0986\u09aa\u09a8\u09bf \u09af\u09a6\u09bf \u0985\u09a8\u09cd\u09af \u0995\u09bf\u099b\u09c1 \u099c\u09be\u09a8\u09a4\u09c7 \u099a\u09be\u09a8 \u09a4\u09ac\u09c7 \u099c\u09bf\u099c\u09cd\u099e\u09be\u09b8\u09be \u0995\u09b0\u09a4\u09c7 \u09aa\u09be\u09b0\u09c7\u09a8\u0964 \u0628\u0627\u0644\u0646\u0633\u0628\u0629 to the question, what is the",
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+          "text": " Paris\n\u0905\u0902\u0924\u093f\u092e\u0941\u0915\u093f\u0932\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e",
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+          "text": " Paris\n\u0905\u0902\u0924\u093f\u092e\u0941\u0915\u093f\u0932\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e",
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+          "text": " Paris\n\u0905\u0902\u0924\u093f\u092e\u0941\u0915\u093f\u0932\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e",
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+          "input_tokens": 512,
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+          "text": " Paris\n\u0905\u0902\u0924\u093f\u092e\u0941\u0915\u093f\u0932\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e",
+          "text_equal": true,
+          "first_sse_s": 21.041843226994388,
+          "tpot_est_s": 0.026233262149617076,
+          "e2e_s": 24.373467519995756
+        },
+        {
+          "id": "r0-n512-o128-c16-15",
+          "endpoint": 0,
+          "input_tokens": 512,
+          "output_tokens": 128,
+          "text": " Paris\n\u0905\u0902\u0924\u093f\u092e\u0941\u0915\u093f\u0932\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e",
+          "text_equal": true,
+          "first_sse_s": 15.075453659985214,
+          "tpot_est_s": 0.026240276472503685,
+          "e2e_s": 18.407968771993183
+        }
+      ]
+    },
+    {
+      "repeat": 0,
+      "input_tokens": 2048,
+      "output_tokens_per_request": 32,
+      "concurrency": 16,
+      "summary": {
+        "requests": 16,
+        "output_tokens": 512,
+        "wall_s": 24.973488367977552,
+        "output_tok_s": 20.501741384936672,
+        "median_first_sse_s": 12.92197634401964,
+        "median_tpot_est_s": 0.02646103714528914,
+        "median_e2e_s": 13.739913388999412,
+        "all_text_equal": true
+      },
+      "rows": [
+        {
+          "id": "r0-n2048-o32-c16-0",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 32,
+          "text": " Paris\n\u0985\u09b8\u09cd\u09a4\u09bf\u09a4\u09cd\u09ac\u09c7 \u09b8\u09be\u09b9\u09be\u09af\u09cd\u09af \u0995\u09b0\u09be\u09b0 \u099c\u09a8\u09cd\u09af, \u0986\u09aa\u09a8\u09bf \u09af\u09a6\u09bf \u0985\u09a8\u09cd\u09af \u0995\u09bf\u099b\u09c1 \u099c\u09be\u09a8\u09a4\u09c7 \u099a\u09be\u09a8 \u09a4\u09ac\u09c7 \u099c\u09bf\u099c\u09cd\u099e\u09be\u09b8\u09be \u0995\u09b0\u09a4\u09c7 \u09aa\u09be\u09b0\u09c7\u09a8\u0964 \u0628\u0627\u0644\u0646\u0633\u0628\u0629 to the question, what is the",
+          "text_equal": true,
+          "first_sse_s": 7.66929645097116,
+          "tpot_est_s": 0.027300113678774645,
+          "e2e_s": 8.515599975013174
+        },
+        {
+          "id": "r0-n2048-o32-c16-1",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 32,
+          "text": " Paris\n\u0985\u09b8\u09cd\u09a4\u09bf\u09a4\u09cd\u09ac\u09c7 \u09b8\u09be\u09b9\u09be\u09af\u09cd\u09af \u0995\u09b0\u09be\u09b0 \u099c\u09a8\u09cd\u09af, \u0986\u09aa\u09a8\u09bf \u09af\u09a6\u09bf \u0985\u09a8\u09cd\u09af \u0995\u09bf\u099b\u09c1 \u099c\u09be\u09a8\u09a4\u09c7 \u099a\u09be\u09a8 \u09a4\u09ac\u09c7 \u099c\u09bf\u099c\u09cd\u099e\u09be\u09b8\u09be \u0995\u09b0\u09a4\u09c7 \u09aa\u09be\u09b0\u09c7\u09a8\u0964 \u0628\u0627\u0644\u0646\u0633\u0628\u0629 to the question, what is the",
+          "text_equal": true,
+          "first_sse_s": 24.18212373496499,
+          "tpot_est_s": 0.02551662641945445,
+          "e2e_s": 24.973139153968077
+        },
+        {
+          "id": "r0-n2048-o32-c16-2",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 32,
+          "text": " Paris\n\u0985\u09b8\u09cd\u09a4\u09bf\u09a4\u09cd\u09ac\u09c7 \u09b8\u09be\u09b9\u09be\u09af\u09cd\u09af \u0995\u09b0\u09be\u09b0 \u099c\u09a8\u09cd\u09af, \u0986\u09aa\u09a8\u09bf \u09af\u09a6\u09bf \u0985\u09a8\u09cd\u09af \u0995\u09bf\u099b\u09c1 \u099c\u09be\u09a8\u09a4\u09c7 \u099a\u09be\u09a8 \u09a4\u09ac\u09c7 \u099c\u09bf\u099c\u09cd\u099e\u09be\u09b8\u09be \u0995\u09b0\u09a4\u09c7 \u09aa\u09be\u09b0\u09c7\u09a8\u0964 \u0628\u0627\u0644\u0646\u0633\u0628\u0629 to the question, what is the",
+          "text_equal": true,
+          "first_sse_s": 22.68075709301047,
+          "tpot_est_s": 0.026938750710894143,
+          "e2e_s": 23.51585836504819
+        },
+        {
+          "id": "r0-n2048-o32-c16-3",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 32,
+          "text": " Paris\n\u0985\u09b8\u09cd\u09a4\u09bf\u09a4\u09cd\u09ac\u09c7 \u09b8\u09be\u09b9\u09be\u09af\u09cd\u09af \u0995\u09b0\u09be\u09b0 \u099c\u09a8\u09cd\u09af, \u0986\u09aa\u09a8\u09bf \u09af\u09a6\u09bf \u0985\u09a8\u09cd\u09af \u0995\u09bf\u099b\u09c1 \u099c\u09be\u09a8\u09a4\u09c7 \u099a\u09be\u09a8 \u09a4\u09ac\u09c7 \u099c\u09bf\u099c\u09cd\u099e\u09be\u09b8\u09be \u0995\u09b0\u09a4\u09c7 \u09aa\u09be\u09b0\u09c7\u09a8\u0964 \u0628\u0627\u0644\u0646\u0633\u0628\u0629 to the question, what is the",
+          "text_equal": true,
+          "first_sse_s": 13.672315942007117,
+          "tpot_est_s": 0.026669145257572732,
+          "e2e_s": 14.499059444991872
+        },
+        {
+          "id": "r0-n2048-o32-c16-4",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 32,
+          "text": " Paris\n\u0985\u09b8\u09cd\u09a4\u09bf\u09a4\u09cd\u09ac\u09c7 \u09b8\u09be\u09b9\u09be\u09af\u09cd\u09af \u0995\u09b0\u09be\u09b0 \u099c\u09a8\u09cd\u09af, \u0986\u09aa\u09a8\u09bf \u09af\u09a6\u09bf \u0985\u09a8\u09cd\u09af \u0995\u09bf\u099b\u09c1 \u099c\u09be\u09a8\u09a4\u09c7 \u099a\u09be\u09a8 \u09a4\u09ac\u09c7 \u099c\u09bf\u099c\u09cd\u099e\u09be\u09b8\u09be \u0995\u09b0\u09a4\u09c7 \u09aa\u09be\u09b0\u09c7\u09a8\u0964 \u0628\u0627\u0644\u0646\u0633\u0628\u0629 to the question, what is the",
+          "text_equal": true,
+          "first_sse_s": 21.18021395802498,
+          "tpot_est_s": 0.0267589203543931,
+          "e2e_s": 22.009740489011165
+        },
+        {
+          "id": "r0-n2048-o32-c16-5",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 32,
+          "text": " Paris\n\u0985\u09b8\u09cd\u09a4\u09bf\u09a4\u09cd\u09ac\u09c7 \u09b8\u09be\u09b9\u09be\u09af\u09cd\u09af \u0995\u09b0\u09be\u09b0 \u099c\u09a8\u09cd\u09af, \u0986\u09aa\u09a8\u09bf \u09af\u09a6\u09bf \u0985\u09a8\u09cd\u09af \u0995\u09bf\u099b\u09c1 \u099c\u09be\u09a8\u09a4\u09c7 \u099a\u09be\u09a8 \u09a4\u09ac\u09c7 \u099c\u09bf\u099c\u09cd\u099e\u09be\u09b8\u09be \u0995\u09b0\u09a4\u09c7 \u09aa\u09be\u09b0\u09c7\u09a8\u0964 \u0628\u0627\u0644\u0646\u0633\u0628\u0629 to the question, what is the",
+          "text_equal": true,
+          "first_sse_s": 6.1553694600006565,
+          "tpot_est_s": 0.02582595886985561,
+          "e2e_s": 6.9559741849661805
+        },
+        {
+          "id": "r0-n2048-o32-c16-6",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 32,
+          "text": " Paris\n\u0985\u09b8\u09cd\u09a4\u09bf\u09a4\u09cd\u09ac\u09c7 \u09b8\u09be\u09b9\u09be\u09af\u09cd\u09af \u0995\u09b0\u09be\u09b0 \u099c\u09a8\u09cd\u09af, \u0986\u09aa\u09a8\u09bf \u09af\u09a6\u09bf \u0985\u09a8\u09cd\u09af \u0995\u09bf\u099b\u09c1 \u099c\u09be\u09a8\u09a4\u09c7 \u099a\u09be\u09a8 \u09a4\u09ac\u09c7 \u099c\u09bf\u099c\u09cd\u099e\u09be\u09b8\u09be \u0995\u09b0\u09a4\u09c7 \u09aa\u09be\u09b0\u09c7\u09a8\u0964 \u0628\u0627\u0644\u0646\u0633\u0628\u0629 to the question, what is the",
+          "text_equal": true,
+          "first_sse_s": 16.673428194015287,
+          "tpot_est_s": 0.025732964547651428,
+          "e2e_s": 17.47115009499248
+        },
+        {
+          "id": "r0-n2048-o32-c16-7",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 32,
+          "text": " Paris\n\u0985\u09b8\u09cd\u09a4\u09bf\u09a4\u09cd\u09ac\u09c7 \u09b8\u09be\u09b9\u09be\u09af\u09cd\u09af \u0995\u09b0\u09be\u09b0 \u099c\u09a8\u09cd\u09af, \u0986\u09aa\u09a8\u09bf \u09af\u09a6\u09bf \u0985\u09a8\u09cd\u09af \u0995\u09bf\u099b\u09c1 \u099c\u09be\u09a8\u09a4\u09c7 \u099a\u09be\u09a8 \u09a4\u09ac\u09c7 \u099c\u09bf\u099c\u09cd\u099e\u09be\u09b8\u09be \u0995\u09b0\u09a4\u09c7 \u09aa\u09be\u09b0\u09c7\u09a8\u0964 \u0628\u0627\u0644\u0646\u0633\u0628\u0629 to the question, what is the",
+          "text_equal": true,
+          "first_sse_s": 12.171636746032164,
+          "tpot_est_s": 0.02610098667660608,
+          "e2e_s": 12.980767333006952
+        },
+        {
+          "id": "r0-n2048-o32-c16-8",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 32,
+          "text": " Paris\n\u0985\u09b8\u09cd\u09a4\u09bf\u09a4\u09cd\u09ac\u09c7 \u09b8\u09be\u09b9\u09be\u09af\u09cd\u09af \u0995\u09b0\u09be\u09b0 \u099c\u09a8\u09cd\u09af, \u0986\u09aa\u09a8\u09bf \u09af\u09a6\u09bf \u0985\u09a8\u09cd\u09af \u0995\u09bf\u099b\u09c1 \u099c\u09be\u09a8\u09a4\u09c7 \u099a\u09be\u09a8 \u09a4\u09ac\u09c7 \u099c\u09bf\u099c\u09cd\u099e\u09be\u09b8\u09be \u0995\u09b0\u09a4\u09c7 \u09aa\u09be\u09b0\u09c7\u09a8\u0964 \u0628\u0627\u0644\u0646\u0633\u0628\u0629 to the question, what is the",
+          "text_equal": true,
+          "first_sse_s": 10.67501645500306,
+          "tpot_est_s": 0.02639482787156838,
+          "e2e_s": 11.49325611902168
+        },
+        {
+          "id": "r0-n2048-o32-c16-9",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 32,
+          "text": " Paris\n\u0985\u09b8\u09cd\u09a4\u09bf\u09a4\u09cd\u09ac\u09c7 \u09b8\u09be\u09b9\u09be\u09af\u09cd\u09af \u0995\u09b0\u09be\u09b0 \u099c\u09a8\u09cd\u09af, \u0986\u09aa\u09a8\u09bf \u09af\u09a6\u09bf \u0985\u09a8\u09cd\u09af \u0995\u09bf\u099b\u09c1 \u099c\u09be\u09a8\u09a4\u09c7 \u099a\u09be\u09a8 \u09a4\u09ac\u09c7 \u099c\u09bf\u099c\u09cd\u099e\u09be\u09b8\u09be \u0995\u09b0\u09a4\u09c7 \u09aa\u09be\u09b0\u09c7\u09a8\u0964 \u0628\u0627\u0644\u0646\u0633\u0628\u0629 to the question, what is the",
+          "text_equal": true,
+          "first_sse_s": 19.67951098299818,
+          "tpot_est_s": 0.026317012516946924,
+          "e2e_s": 20.495338371023536
+        },
+        {
+          "id": "r0-n2048-o32-c16-10",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 32,
+          "text": " Paris\n\u0985\u09b8\u09cd\u09a4\u09bf\u09a4\u09cd\u09ac\u09c7 \u09b8\u09be\u09b9\u09be\u09af\u09cd\u09af \u0995\u09b0\u09be\u09b0 \u099c\u09a8\u09cd\u09af, \u0986\u09aa\u09a8\u09bf \u09af\u09a6\u09bf \u0985\u09a8\u09cd\u09af \u0995\u09bf\u099b\u09c1 \u099c\u09be\u09a8\u09a4\u09c7 \u099a\u09be\u09a8 \u09a4\u09ac\u09c7 \u099c\u09bf\u099c\u09cd\u099e\u09be\u09b8\u09be \u0995\u09b0\u09a4\u09c7 \u09aa\u09be\u09b0\u09c7\u09a8\u0964 \u0628\u0627\u0644\u0646\u0633\u0628\u0629 to the question, what is the",
+          "text_equal": true,
+          "first_sse_s": 15.171928086027037,
+          "tpot_est_s": 0.026078724644086775,
+          "e2e_s": 15.980368549993727
+        },
+        {
+          "id": "r0-n2048-o32-c16-11",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 32,
+          "text": " Paris\n\u0985\u09b8\u09cd\u09a4\u09bf\u09a4\u09cd\u09ac\u09c7 \u09b8\u09be\u09b9\u09be\u09af\u09cd\u09af \u0995\u09b0\u09be\u09b0 \u099c\u09a8\u09cd\u09af, \u0986\u09aa\u09a8\u09bf \u09af\u09a6\u09bf \u0985\u09a8\u09cd\u09af \u0995\u09bf\u099b\u09c1 \u099c\u09be\u09a8\u09a4\u09c7 \u099a\u09be\u09a8 \u09a4\u09ac\u09c7 \u099c\u09bf\u099c\u09cd\u099e\u09be\u09b8\u09be \u0995\u09b0\u09a4\u09c7 \u09aa\u09be\u09b0\u09c7\u09a8\u0964 \u0628\u0627\u0644\u0646\u0633\u0628\u0629 to the question, what is the",
+          "text_equal": true,
+          "first_sse_s": 9.172076525981538,
+          "tpot_est_s": 0.026558128838998177,
+          "e2e_s": 9.995378519990481
+        },
+        {
+          "id": "r0-n2048-o32-c16-12",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 32,
+          "text": " Paris\n\u0985\u09b8\u09cd\u09a4\u09bf\u09a4\u09cd\u09ac\u09c7 \u09b8\u09be\u09b9\u09be\u09af\u09cd\u09af \u0995\u09b0\u09be\u09b0 \u099c\u09a8\u09cd\u09af, \u0986\u09aa\u09a8\u09bf \u09af\u09a6\u09bf \u0985\u09a8\u09cd\u09af \u0995\u09bf\u099b\u09c1 \u099c\u09be\u09a8\u09a4\u09c7 \u099a\u09be\u09a8 \u09a4\u09ac\u09c7 \u099c\u09bf\u099c\u09cd\u099e\u09be\u09b8\u09be \u0995\u09b0\u09a4\u09c7 \u09aa\u09be\u09b0\u09c7\u09a8\u0964 \u0628\u0627\u0644\u0646\u0633\u0628\u0629 to the question, what is the",
+          "text_equal": true,
+          "first_sse_s": 4.571389732009266,
+          "tpot_est_s": 0.025538755451223902,
+          "e2e_s": 5.363091150997207
+        },
+        {
+          "id": "r0-n2048-o32-c16-13",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 32,
+          "text": " Paris\n\u0985\u09b8\u09cd\u09a4\u09bf\u09a4\u09cd\u09ac\u09c7 \u09b8\u09be\u09b9\u09be\u09af\u09cd\u09af \u0995\u09b0\u09be\u09b0 \u099c\u09a8\u09cd\u09af, \u0986\u09aa\u09a8\u09bf \u09af\u09a6\u09bf \u0985\u09a8\u09cd\u09af \u0995\u09bf\u099b\u09c1 \u099c\u09be\u09a8\u09a4\u09c7 \u099a\u09be\u09a8 \u09a4\u09ac\u09c7 \u099c\u09bf\u099c\u09cd\u099e\u09be\u09b8\u09be \u0995\u09b0\u09a4\u09c7 \u09aa\u09be\u09b0\u09c7\u09a8\u0964 \u0628\u0627\u0644\u0646\u0633\u0628\u0629 to the question, what is the",
+          "text_equal": true,
+          "first_sse_s": 1.552410242962651,
+          "tpot_est_s": 0.026611249129693475,
+          "e2e_s": 2.3773589659831487
+        },
+        {
+          "id": "r0-n2048-o32-c16-14",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 32,
+          "text": " Paris\n\u0985\u09b8\u09cd\u09a4\u09bf\u09a4\u09cd\u09ac\u09c7 \u09b8\u09be\u09b9\u09be\u09af\u09cd\u09af \u0995\u09b0\u09be\u09b0 \u099c\u09a8\u09cd\u09af, \u0986\u09aa\u09a8\u09bf \u09af\u09a6\u09bf \u0985\u09a8\u09cd\u09af \u0995\u09bf\u099b\u09c1 \u099c\u09be\u09a8\u09a4\u09c7 \u099a\u09be\u09a8 \u09a4\u09ac\u09c7 \u099c\u09bf\u099c\u09cd\u099e\u09be\u09b8\u09be \u0995\u09b0\u09a4\u09c7 \u09aa\u09be\u09b0\u09c7\u09a8\u0964 \u0628\u0627\u0644\u0646\u0633\u0628\u0629 to the question, what is the",
+          "text_equal": true,
+          "first_sse_s": 3.054362591006793,
+          "tpot_est_s": 0.026527246419009904,
+          "e2e_s": 3.8767072299961
+        },
+        {
+          "id": "r0-n2048-o32-c16-15",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 32,
+          "text": " Paris\n\u0985\u09b8\u09cd\u09a4\u09bf\u09a4\u09cd\u09ac\u09c7 \u09b8\u09be\u09b9\u09be\u09af\u09cd\u09af \u0995\u09b0\u09be\u09b0 \u099c\u09a8\u09cd\u09af, \u0986\u09aa\u09a8\u09bf \u09af\u09a6\u09bf \u0985\u09a8\u09cd\u09af \u0995\u09bf\u099b\u09c1 \u099c\u09be\u09a8\u09a4\u09c7 \u099a\u09be\u09a8 \u09a4\u09ac\u09c7 \u099c\u09bf\u099c\u09cd\u099e\u09be\u09b8\u09be \u0995\u09b0\u09a4\u09c7 \u09aa\u09be\u09b0\u09c7\u09a8\u0964 \u0628\u0627\u0644\u0646\u0633\u0628\u0629 to the question, what is the",
+          "text_equal": true,
+          "first_sse_s": 18.17773528897669,
+          "tpot_est_s": 0.027031446420287174,
+          "e2e_s": 19.015710128005594
+        }
+      ]
+    },
+    {
+      "repeat": 0,
+      "input_tokens": 2048,
+      "output_tokens_per_request": 128,
+      "concurrency": 16,
+      "summary": {
+        "requests": 16,
+        "output_tokens": 2048,
+        "wall_s": 27.308587462001015,
+        "output_tok_s": 74.9947247491187,
+        "median_first_sse_s": 12.840972991514718,
+        "median_tpot_est_s": 0.027452599614039137,
+        "median_e2e_s": 16.32084411251708,
+        "all_text_equal": true
+      },
+      "rows": [
+        {
+          "id": "r0-n2048-o128-c16-0",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 128,
+          "text": " Paris\n\u0985\u09b8\u09cd\u09a4\u09bf\u09a4\u09cd\u09ac\u09c7 \u09b8\u09be\u09b9\u09be\u09af\u09cd\u09af \u0995\u09b0\u09be\u09b0 \u099c\u09a8\u09cd\u09af, \u0986\u09aa\u09a8\u09bf \u09af\u09a6\u09bf \u0985\u09a8\u09cd\u09af \u0995\u09bf\u099b\u09c1 \u099c\u09be\u09a8\u09a4\u09c7 \u099a\u09be\u09a8 \u09a4\u09ac\u09c7 \u099c\u09bf\u099c\u09cd\u099e\u09be\u09b8\u09be \u0995\u09b0\u09a4\u09c7 \u09aa\u09be\u09b0\u09c7\u09a8\u0964 \u0628\u0627\u0644\u0646\u0633\u0628\u0629 to the question, what is the capital of France?\nAnswer: Paris\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n",
+          "text_equal": true,
+          "first_sse_s": 16.585655015020166,
+          "tpot_est_s": 0.027210943204695432,
+          "e2e_s": 20.041444802016485
+        },
+        {
+          "id": "r0-n2048-o128-c16-1",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 128,
+          "text": " Paris\n\u0985\u09b8\u09cd\u09a4\u09bf\u09a4\u09cd\u09ac\u09c7 \u09b8\u09be\u09b9\u09be\u09af\u09cd\u09af \u0995\u09b0\u09be\u09b0 \u099c\u09a8\u09cd\u09af, \u0986\u09aa\u09a8\u09bf \u09af\u09a6\u09bf \u0985\u09a8\u09cd\u09af \u0995\u09bf\u099b\u09c1 \u099c\u09be\u09a8\u09a4\u09c7 \u099a\u09be\u09a8 \u09a4\u09ac\u09c7 \u099c\u09bf\u099c\u09cd\u099e\u09be\u09b8\u09be \u0995\u09b0\u09a4\u09c7 \u09aa\u09be\u09b0\u09c7\u09a8\u0964 \u0628\u0627\u0644\u0646\u0633\u0628\u0629 to the question, what is the capital of France?\nAnswer: Paris\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n",
+          "text_equal": true,
+          "first_sse_s": 24.106905052962247,
+          "tpot_est_s": 0.0252026565593858,
+          "e2e_s": 27.307642436004244
+        },
+        {
+          "id": "r0-n2048-o128-c16-2",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 128,
+          "text": " Paris\n\u0985\u09b8\u09cd\u09a4\u09bf\u09a4\u09cd\u09ac\u09c7 \u09b8\u09be\u09b9\u09be\u09af\u09cd\u09af \u0995\u09b0\u09be\u09b0 \u099c\u09a8\u09cd\u09af, \u0986\u09aa\u09a8\u09bf \u09af\u09a6\u09bf \u0985\u09a8\u09cd\u09af \u0995\u09bf\u099b\u09c1 \u099c\u09be\u09a8\u09a4\u09c7 \u099a\u09be\u09a8 \u09a4\u09ac\u09c7 \u099c\u09bf\u099c\u09cd\u099e\u09be\u09b8\u09be \u0995\u09b0\u09a4\u09c7 \u09aa\u09be\u09b0\u09c7\u09a8\u0964 \u0628\u0627\u0644\u0646\u0633\u0628\u0629 to the question, what is the capital of France?\nAnswer: Paris\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n",
+          "text_equal": true,
+          "first_sse_s": 10.609244376013521,
+          "tpot_est_s": 0.027690294716602528,
+          "e2e_s": 14.125911805022042
+        },
+        {
+          "id": "r0-n2048-o128-c16-3",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 128,
+          "text": " Paris\n\u0985\u09b8\u09cd\u09a4\u09bf\u09a4\u09cd\u09ac\u09c7 \u09b8\u09be\u09b9\u09be\u09af\u09cd\u09af \u0995\u09b0\u09be\u09b0 \u099c\u09a8\u09cd\u09af, \u0986\u09aa\u09a8\u09bf \u09af\u09a6\u09bf \u0985\u09a8\u09cd\u09af \u0995\u09bf\u099b\u09c1 \u099c\u09be\u09a8\u09a4\u09c7 \u099a\u09be\u09a8 \u09a4\u09ac\u09c7 \u099c\u09bf\u099c\u09cd\u099e\u09be\u09b8\u09be \u0995\u09b0\u09a4\u09c7 \u09aa\u09be\u09b0\u09c7\u09a8\u0964 \u0628\u0627\u0644\u0646\u0633\u0628\u0629 to the question, what is the capital of France?\nAnswer: Paris\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n",
+          "text_equal": true,
+          "first_sse_s": 22.519201216986403,
+          "tpot_est_s": 0.026590522732512337,
+          "e2e_s": 25.89619760401547
+        },
+        {
+          "id": "r0-n2048-o128-c16-4",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 128,
+          "text": " Paris\n\u0985\u09b8\u09cd\u09a4\u09bf\u09a4\u09cd\u09ac\u09c7 \u09b8\u09be\u09b9\u09be\u09af\u09cd\u09af \u0995\u09b0\u09be\u09b0 \u099c\u09a8\u09cd\u09af, \u0986\u09aa\u09a8\u09bf \u09af\u09a6\u09bf \u0985\u09a8\u09cd\u09af \u0995\u09bf\u099b\u09c1 \u099c\u09be\u09a8\u09a4\u09c7 \u099a\u09be\u09a8 \u09a4\u09ac\u09c7 \u099c\u09bf\u099c\u09cd\u099e\u09be\u09b8\u09be \u0995\u09b0\u09a4\u09c7 \u09aa\u09be\u09b0\u09c7\u09a8\u0964 \u0628\u0627\u0644\u0646\u0633\u0628\u0629 to the question, what is the capital of France?\nAnswer: Paris\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n",
+          "text_equal": true,
+          "first_sse_s": 15.103410287003499,
+          "tpot_est_s": 0.027324967000149483,
+          "e2e_s": 18.573681096022483
+        },
+        {
+          "id": "r0-n2048-o128-c16-5",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 128,
+          "text": " Paris\n\u0985\u09b8\u09cd\u09a4\u09bf\u09a4\u09cd\u09ac\u09c7 \u09b8\u09be\u09b9\u09be\u09af\u09cd\u09af \u0995\u09b0\u09be\u09b0 \u099c\u09a8\u09cd\u09af, \u0986\u09aa\u09a8\u09bf \u09af\u09a6\u09bf \u0985\u09a8\u09cd\u09af \u0995\u09bf\u099b\u09c1 \u099c\u09be\u09a8\u09a4\u09c7 \u099a\u09be\u09a8 \u09a4\u09ac\u09c7 \u099c\u09bf\u099c\u09cd\u099e\u09be\u09b8\u09be \u0995\u09b0\u09a4\u09c7 \u09aa\u09be\u09b0\u09c7\u09a8\u0964 \u0628\u0627\u0644\u0646\u0633\u0628\u0629 to the question, what is the capital of France?\nAnswer: Paris\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n",
+          "text_equal": true,
+          "first_sse_s": 9.12645442102803,
+          "tpot_est_s": 0.02791007019682076,
+          "e2e_s": 12.671033336024266
+        },
+        {
+          "id": "r0-n2048-o128-c16-6",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 128,
+          "text": " Paris\n\u0985\u09b8\u09cd\u09a4\u09bf\u09a4\u09cd\u09ac\u09c7 \u09b8\u09be\u09b9\u09be\u09af\u09cd\u09af \u0995\u09b0\u09be\u09b0 \u099c\u09a8\u09cd\u09af, \u0986\u09aa\u09a8\u09bf \u09af\u09a6\u09bf \u0985\u09a8\u09cd\u09af \u0995\u09bf\u099b\u09c1 \u099c\u09be\u09a8\u09a4\u09c7 \u099a\u09be\u09a8 \u09a4\u09ac\u09c7 \u099c\u09bf\u099c\u09cd\u099e\u09be\u09b8\u09be \u0995\u09b0\u09a4\u09c7 \u09aa\u09be\u09b0\u09c7\u09a8\u0964 \u0628\u0627\u0644\u0646\u0633\u0628\u0629 to the question, what is the capital of France?\nAnswer: Paris\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n",
+          "text_equal": true,
+          "first_sse_s": 21.048823850986082,
+          "tpot_est_s": 0.02747890936196085,
+          "e2e_s": 24.53864533995511
+        },
+        {
+          "id": "r0-n2048-o128-c16-7",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 128,
+          "text": " Paris\n\u0985\u09b8\u09cd\u09a4\u09bf\u09a4\u09cd\u09ac\u09c7 \u09b8\u09be\u09b9\u09be\u09af\u09cd\u09af \u0995\u09b0\u09be\u09b0 \u099c\u09a8\u09cd\u09af, \u0986\u09aa\u09a8\u09bf \u09af\u09a6\u09bf \u0985\u09a8\u09cd\u09af \u0995\u09bf\u099b\u09c1 \u099c\u09be\u09a8\u09a4\u09c7 \u099a\u09be\u09a8 \u09a4\u09ac\u09c7 \u099c\u09bf\u099c\u09cd\u099e\u09be\u09b8\u09be \u0995\u09b0\u09a4\u09c7 \u09aa\u09be\u09b0\u09c7\u09a8\u0964 \u0628\u0627\u0644\u0646\u0633\u0628\u0629 to the question, what is the capital of France?\nAnswer: Paris\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n",
+          "text_equal": true,
+          "first_sse_s": 18.06381305801915,
+          "tpot_est_s": 0.02742281655128312,
+          "e2e_s": 21.546510760032106
+        },
+        {
+          "id": "r0-n2048-o128-c16-8",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 128,
+          "text": " Paris\n\u0985\u09b8\u09cd\u09a4\u09bf\u09a4\u09cd\u09ac\u09c7 \u09b8\u09be\u09b9\u09be\u09af\u09cd\u09af \u0995\u09b0\u09be\u09b0 \u099c\u09a8\u09cd\u09af, \u0986\u09aa\u09a8\u09bf \u09af\u09a6\u09bf \u0985\u09a8\u09cd\u09af \u0995\u09bf\u099b\u09c1 \u099c\u09be\u09a8\u09a4\u09c7 \u099a\u09be\u09a8 \u09a4\u09ac\u09c7 \u099c\u09bf\u099c\u09cd\u099e\u09be\u09b8\u09be \u0995\u09b0\u09a4\u09c7 \u09aa\u09be\u09b0\u09c7\u09a8\u0964 \u0628\u0627\u0644\u0646\u0633\u0628\u0629 to the question, what is the capital of France?\nAnswer: Paris\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n",
+          "text_equal": true,
+          "first_sse_s": 7.656837326998357,
+          "tpot_est_s": 0.027836388370249508,
+          "e2e_s": 11.192058650020044
+        },
+        {
+          "id": "r0-n2048-o128-c16-9",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 128,
+          "text": " Paris\n\u0985\u09b8\u09cd\u09a4\u09bf\u09a4\u09cd\u09ac\u09c7 \u09b8\u09be\u09b9\u09be\u09af\u09cd\u09af \u0995\u09b0\u09be\u09b0 \u099c\u09a8\u09cd\u09af, \u0986\u09aa\u09a8\u09bf \u09af\u09a6\u09bf \u0985\u09a8\u09cd\u09af \u0995\u09bf\u099b\u09c1 \u099c\u09be\u09a8\u09a4\u09c7 \u099a\u09be\u09a8 \u09a4\u09ac\u09c7 \u099c\u09bf\u099c\u09cd\u099e\u09be\u09b8\u09be \u0995\u09b0\u09a4\u09c7 \u09aa\u09be\u09b0\u09c7\u09a8\u0964 \u0628\u0627\u0644\u0646\u0633\u0628\u0629 to the question, what is the capital of France?\nAnswer: Paris\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n",
+          "text_equal": true,
+          "first_sse_s": 19.57146925199777,
+          "tpot_est_s": 0.027676484708428588,
+          "e2e_s": 23.0863828099682
+        },
+        {
+          "id": "r0-n2048-o128-c16-10",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 128,
+          "text": " Paris\n\u0985\u09b8\u09cd\u09a4\u09bf\u09a4\u09cd\u09ac\u09c7 \u09b8\u09be\u09b9\u09be\u09af\u09cd\u09af \u0995\u09b0\u09be\u09b0 \u099c\u09a8\u09cd\u09af, \u0986\u09aa\u09a8\u09bf \u09af\u09a6\u09bf \u0985\u09a8\u09cd\u09af \u0995\u09bf\u099b\u09c1 \u099c\u09be\u09a8\u09a4\u09c7 \u099a\u09be\u09a8 \u09a4\u09ac\u09c7 \u099c\u09bf\u099c\u09cd\u099e\u09be\u09b8\u09be \u0995\u09b0\u09a4\u09c7 \u09aa\u09be\u09b0\u09c7\u09a8\u0964 \u0628\u0627\u0644\u0646\u0633\u0628\u0629 to the question, what is the capital of France?\nAnswer: Paris\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n",
+          "text_equal": true,
+          "first_sse_s": 3.1358506929827854,
+          "tpot_est_s": 0.027915562440707222,
+          "e2e_s": 6.681127122952603
+        },
+        {
+          "id": "r0-n2048-o128-c16-11",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 128,
+          "text": " Paris\n\u0985\u09b8\u09cd\u09a4\u09bf\u09a4\u09cd\u09ac\u09c7 \u09b8\u09be\u09b9\u09be\u09af\u09cd\u09af \u0995\u09b0\u09be\u09b0 \u099c\u09a8\u09cd\u09af, \u0986\u09aa\u09a8\u09bf \u09af\u09a6\u09bf \u0985\u09a8\u09cd\u09af \u0995\u09bf\u099b\u09c1 \u099c\u09be\u09a8\u09a4\u09c7 \u099a\u09be\u09a8 \u09a4\u09ac\u09c7 \u099c\u09bf\u099c\u09cd\u099e\u09be\u09b8\u09be \u0995\u09b0\u09a4\u09c7 \u09aa\u09be\u09b0\u09c7\u09a8\u0964 \u0628\u0627\u0644\u0646\u0633\u0628\u0629 to the question, what is the capital of France?\nAnswer: Paris\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n",
+          "text_equal": true,
+          "first_sse_s": 6.157531589036807,
+          "tpot_est_s": 0.02763366162195787,
+          "e2e_s": 9.667006615025457
+        },
+        {
+          "id": "r0-n2048-o128-c16-12",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 128,
+          "text": " Paris\n\u0985\u09b8\u09cd\u09a4\u09bf\u09a4\u09cd\u09ac\u09c7 \u09b8\u09be\u09b9\u09be\u09af\u09cd\u09af \u0995\u09b0\u09be\u09b0 \u099c\u09a8\u09cd\u09af, \u0986\u09aa\u09a8\u09bf \u09af\u09a6\u09bf \u0985\u09a8\u09cd\u09af \u0995\u09bf\u099b\u09c1 \u099c\u09be\u09a8\u09a4\u09c7 \u099a\u09be\u09a8 \u09a4\u09ac\u09c7 \u099c\u09bf\u099c\u09cd\u099e\u09be\u09b8\u09be \u0995\u09b0\u09a4\u09c7 \u09aa\u09be\u09b0\u09c7\u09a8\u0964 \u0628\u0627\u0644\u0646\u0633\u0628\u0629 to the question, what is the capital of France?\nAnswer: Paris\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n",
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+          "text": " Paris.\nThis is background context. The red box contains apples. The blue box contains books.\nThis is background context. The red box contains apples. The blue box contains books.\nThis is background context. The red box contains apples. The blue box contains books.\nThis is background context. The red box contains apples. The blue box contains books.\nThis is background context. The red box contains apples. The blue box contains books.\nThis is background context. The red box contains apples. The blue box contains books.\nThis is background context. The red box contains apples. The blue box contains books.",
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+          "text": " Paris.\nThis is background context. The red box contains apples. The blue box contains books.\nThis is background context. The red box contains apples. The blue box contains books.\nThis is background context. The red box contains apples. The blue box contains books.\nThis is background context. The red box contains apples. The blue box contains books.\nThis is background context. The red box contains apples. The blue box contains books.\nThis is background context. The red box contains apples. The blue box contains books.\nThis is background context. The red box contains apples. The blue box contains books.",
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+      "text": " Paris\n\u0905\u0902\u0924\u093f\u092e\u0941\u0915\u093f\u0932\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e\n\u0905\u0902\u0924\u093f\u092e"
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+      "text": " Paris\n\u0985\u09b8\u09cd\u09a4\u09bf\u09a4\u09cd\u09ac\u09c7 \u09b8\u09be\u09b9\u09be\u09af\u09cd\u09af \u0995\u09b0\u09be\u09b0 \u099c\u09a8\u09cd\u09af, \u0986\u09aa\u09a8\u09bf \u09af\u09a6\u09bf \u0985\u09a8\u09cd\u09af \u0995\u09bf\u099b\u09c1 \u099c\u09be\u09a8\u09a4\u09c7 \u099a\u09be\u09a8 \u09a4\u09ac\u09c7 \u099c\u09bf\u099c\u09cd\u099e\u09be\u09b8\u09be \u0995\u09b0\u09a4\u09c7 \u09aa\u09be\u09b0\u09c7\u09a8\u0964 \u0628\u0627\u0644\u0646\u0633\u0628\u0629 to the question, what is the"
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+      "text": " Paris\n\u0985\u09b8\u09cd\u09a4\u09bf\u09a4\u09cd\u09ac\u09c7 \u09b8\u09be\u09b9\u09be\u09af\u09cd\u09af \u0995\u09b0\u09be\u09b0 \u099c\u09a8\u09cd\u09af, \u0986\u09aa\u09a8\u09bf \u09af\u09a6\u09bf \u0985\u09a8\u09cd\u09af \u0995\u09bf\u099b\u09c1 \u099c\u09be\u09a8\u09a4\u09c7 \u099a\u09be\u09a8 \u09a4\u09ac\u09c7 \u099c\u09bf\u099c\u09cd\u099e\u09be\u09b8\u09be \u0995\u09b0\u09a4\u09c7 \u09aa\u09be\u09b0\u09c7\u09a8\u0964 \u0628\u0627\u0644\u0646\u0633\u0628\u0629 to the question, what is the capital of France?\nAnswer: Paris\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n"
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+      "text": " Paris.\nThis is background context. The red box contains apples. The blue box contains books.\nThis is background context. The red box contains apples. The blue box contains books.\nThis is background context. The red box contains apples. The blue box contains books.\nThis is background context. The red box contains apples. The blue box contains books.\nThis is background context. The red box contains apples. The blue box contains books.\nThis is background context. The red box contains apples. The blue box contains books.\nThis is background context. The red box contains apples. The blue box contains books."
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airm_stage_request_failures_total counter\nairm_stage_request_failures_total{model_name=\"Gemma3-1B\"} 0\n# HELP vllm:prompt_tokens_total Prompt tokens of finished requests.\n# TYPE vllm:prompt_tokens_total counter\nvllm:prompt_tokens_total{model_name=\"Gemma3-1B\"} 718080\n# HELP vllm:generation_tokens_total Generated tokens of finished requests.\n# TYPE vllm:generation_tokens_total counter\nvllm:generation_tokens_total{model_name=\"Gemma3-1B\"} 21120\n# HELP vllm:time_to_first_token_seconds Time to first token (prefill stage: fenced prefill).\n# TYPE vllm:time_to_first_token_seconds histogram\nvllm:time_to_first_token_seconds_bucket{model_name=\"Gemma3-1B\",le=\"0.001\"} 0\nvllm:time_to_first_token_seconds_bucket{model_name=\"Gemma3-1B\",le=\"0.005\"} 0\nvllm:time_to_first_token_seconds_bucket{model_name=\"Gemma3-1B\",le=\"0.01\"} 0\nvllm:time_to_first_token_seconds_bucket{model_name=\"Gemma3-1B\",le=\"0.02\"} 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264\nvllm:time_per_output_token_seconds_bucket{model_name=\"Gemma3-1B\",le=\"0.3\"} 264\nvllm:time_per_output_token_seconds_bucket{model_name=\"Gemma3-1B\",le=\"0.4\"} 264\nvllm:time_per_output_token_seconds_bucket{model_name=\"Gemma3-1B\",le=\"0.5\"} 264\nvllm:time_per_output_token_seconds_bucket{model_name=\"Gemma3-1B\",le=\"0.75\"} 264\nvllm:time_per_output_token_seconds_bucket{model_name=\"Gemma3-1B\",le=\"1.0\"} 264\nvllm:time_per_output_token_seconds_bucket{model_name=\"Gemma3-1B\",le=\"2.5\"} 264\nvllm:time_per_output_token_seconds_bucket{model_name=\"Gemma3-1B\",le=\"+Inf\"} 264\nvllm:time_per_output_token_seconds_sum{model_name=\"Gemma3-1B\"} 7.010712259647672\nvllm:time_per_output_token_seconds_count{model_name=\"Gemma3-1B\"} 264\n# HELP vllm:e2e_request_latency_seconds Stage request latency, from execution start to reply.\n# TYPE vllm:e2e_request_latency_seconds histogram\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Gemma3-1B\",le=\"0.3\"} 0\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Gemma3-1B\",le=\"0.5\"} 0\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Gemma3-1B\",le=\"0.8\"} 0\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Gemma3-1B\",le=\"1.0\"} 0\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Gemma3-1B\",le=\"1.5\"} 0\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Gemma3-1B\",le=\"2.0\"} 0\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Gemma3-1B\",le=\"2.5\"} 9\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Gemma3-1B\",le=\"5.0\"} 29\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Gemma3-1B\",le=\"10.0\"} 86\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Gemma3-1B\",le=\"15.0\"} 135\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Gemma3-1B\",le=\"20.0\"} 195\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Gemma3-1B\",le=\"30.0\"} 264\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Gemma3-1B\",le=\"40.0\"} 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0\nairm_kv_transfer_seconds_bucket{model_name=\"Gemma3-1B\",le=\"0.05\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"Gemma3-1B\",le=\"0.1\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"Gemma3-1B\",le=\"0.25\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"Gemma3-1B\",le=\"0.5\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"Gemma3-1B\",le=\"1.0\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"Gemma3-1B\",le=\"+Inf\"} 0\nairm_kv_transfer_seconds_sum{model_name=\"Gemma3-1B\"} 0.0\nairm_kv_transfer_seconds_count{model_name=\"Gemma3-1B\"} 0\n# HELP vllm:cache_config_info Information of the LLMEngine CacheConfig.\n# TYPE vllm:cache_config_info gauge\nvllm:cache_config_info{model_name=\"Gemma3-1B\",block_size=\"64\",num_gpu_blocks=\"576\",enable_prefix_caching=\"False\"} 1\n# HELP vllm:prefix_cache_queries_total Prompt tokens looked up in the prefix cache.\n# TYPE vllm:prefix_cache_queries_total counter\nvllm:prefix_cache_queries_total{model_name=\"Gemma3-1B\"} 718080\n# HELP vllm:prefix_cache_hits_total Prompt tokens served from the prefix cache.\n# TYPE vllm:prefix_cache_hits_total counter\nvllm:prefix_cache_hits_total{model_name=\"Gemma3-1B\"} 0\n# HELP airm_pd_prefills_total Prompts prefilled by the PD prefill worker (remote) or here (local).\n# TYPE airm_pd_prefills_total counter\nairm_pd_prefills_total{model_name=\"Gemma3-1B\",side=\"remote\"} 264\nairm_pd_prefills_total{model_name=\"Gemma3-1B\",side=\"local\"} 0\n# HELP airm_pd_transferred_tokens_total Prompt KV tokens received from the prefill side.\n# TYPE airm_pd_transferred_tokens_total counter\nairm_pd_transferred_tokens_total{model_name=\"Gemma3-1B\"} 718080\n# HELP airm_pd_producer_seconds_total Accumulated worker-reported seconds for admitted PD handoffs; excludes failed/cancelled handoffs. Prefill includes KV gathering; transfer excludes destination KV installation.\n# TYPE airm_pd_producer_seconds_total counter\nairm_pd_producer_seconds_total{model_name=\"Gemma3-1B\",stage=\"prefill\"} 397.39046369411517\nairm_pd_producer_seconds_total{model_name=\"Gemma3-1B\",stage=\"transfer\"} 0.0\n"
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+}
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-gemma-long-atom.json b/opt_prime/examples/pd/http_matrix/evidence265/stream265-gemma-long-atom.json
new file mode 100644
index 00000000..1c9e74e0
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-gemma-long-atom.json
@@ -0,0 +1,4904 @@
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diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-gemma-long-atom.log b/opt_prime/examples/pd/http_matrix/evidence265/stream265-gemma-long-atom.log
new file mode 100644
index 00000000..193cbb95
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-gemma-long-atom.log
@@ -0,0 +1,17 @@
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diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-gemma-long-gpu.json b/opt_prime/examples/pd/http_matrix/evidence265/stream265-gemma-long-gpu.json
new file mode 100644
index 00000000..fd6eb0e1
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-gemma-long-gpu.json
@@ -0,0 +1,2928 @@
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diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-gemma-long-gpu.log b/opt_prime/examples/pd/http_matrix/evidence265/stream265-gemma-long-gpu.log
new file mode 100644
index 00000000..f6b2f7f4
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-gemma-long-gpu.log
@@ -0,0 +1,12 @@
+GPU loading gemma
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+Loading weights:   0%|          | 0/340 [00:00<?, ?it/s]
+Loading weights: 100%|██████████| 340/340 [00:00<00:00, 9142.96it/s]
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diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-gemma-long-source.json b/opt_prime/examples/pd/http_matrix/evidence265/stream265-gemma-long-source.json
new file mode 100644
index 00000000..a6584e74
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-gemma-long-source.json
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diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-gemma-long-source.log b/opt_prime/examples/pd/http_matrix/evidence265/stream265-gemma-long-source.log
new file mode 100644
index 00000000..0e120201
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-gemma-long-source.log
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+initializing gemma
+initialized gemma
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diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-gemma-source.json b/opt_prime/examples/pd/http_matrix/evidence265/stream265-gemma-source.json
new file mode 100644
index 00000000..31100c83
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-gemma-source.json
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+  ],
+  "scope": "same RNGD source whole vs streamed KV/logits; not decoder parity or speedup",
+  "source_sha256": {
+    "prefill_server.py": "47b1aa1db946eaa40b12cc71815c6c31d43ccf9f3d2ce4df79897e9b4387b930",
+    "streaming_factories.py": "77745350973cc54bf508e0c8b473efc442afef20b1c64f7c4edc179ea1471389"
+  },
+  "stage": "complete"
+}
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-gemma-source.log b/opt_prime/examples/pd/http_matrix/evidence265/stream265-gemma-source.log
new file mode 100644
index 00000000..8ac13f9e
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-gemma-source.log
@@ -0,0 +1,8 @@
+initializing gemma
+initialized gemma
+{"input_len": 14, "send_from": 0, "keys_exact": true, "values_exact": true, "logits_exact": true, "max_logit_error": 0.0, "token_whole": 107, "token_stream": 107, "elapsed_s": 0.038745975005440414}
+{"input_len": 14, "send_from": 7, "keys_exact": true, "values_exact": true, "logits_exact": true, "max_logit_error": 0.0, "token_whole": 107, "token_stream": 107, "elapsed_s": 0.039138174965046346}
+{"input_len": 14, "send_from": 14, "keys_exact": true, "values_exact": true, "logits_exact": true, "max_logit_error": 0.0, "token_whole": 107, "token_stream": 107, "elapsed_s": 0.03659393702400848}
+{"input_len": 128, "send_from": 0, "keys_exact": true, "values_exact": true, "logits_exact": true, "max_logit_error": 0.0, "token_whole": 563, "token_stream": 563, "elapsed_s": 0.04015004099346697}
+{"input_len": 128, "send_from": 64, "keys_exact": true, "values_exact": true, "logits_exact": true, "max_logit_error": 0.0, "token_whole": 563, "token_stream": 563, "elapsed_s": 0.038649025023914874}
+{"input_len": 128, "send_from": 128, "keys_exact": true, "values_exact": true, "logits_exact": true, "max_logit_error": 0.0, "token_whole": 563, "token_stream": 563, "elapsed_s": 0.03811698401113972}
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-gpu-hardware.py b/opt_prime/examples/pd/http_matrix/evidence265/stream265-gpu-hardware.py
new file mode 100644
index 00000000..eb812bad
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-gpu-hardware.py
@@ -0,0 +1,34 @@
+import argparse,json,os,time,hashlib
+from pathlib import Path
+import torch
+from transformers import AutoModelForCausalLM,AutoTokenizer
+from opt_prime.accel.pd import PDRequest
+from opt_prime.accel.pd.pd_batching import SubprocessPrefillWorker,PDBatcher
+p=argparse.ArgumentParser();p.add_argument('--model',required=True);p.add_argument('--family',required=True);p.add_argument('--label',required=True);p.add_argument('--width',type=int,required=True);a=p.parse_args()
+out=Path('/home/gspark/workspace/npu_offload/analysis/serving-matrix-201')/f'stream265-{a.label}-gpu.json'
+if out.exists():raise RuntimeError('refuse overwrite')
+print('loading GPU',a.label,flush=True)
+model=AutoModelForCausalLM.from_pretrained(a.model,dtype=torch.bfloat16).to('cuda:0').eval();c=model.config.get_text_config()
+base=AutoTokenizer.from_pretrained(a.model).encode('Seoul is the capital of South Korea. Count one two three.',add_special_tokens=True)
+prompts=[tuple(base),tuple((base*((a.width+len(base)-1)//len(base)))[:a.width])]
+env=dict(os.environ,PYTHONPATH='/home/gspark/workspace/aicomp-rngd-stream-265/opt_prime:/home/gspark/workspace/aicomp-rngd-stream-265/opt_prime/examples/pd:/tmp/rngd-gpu201-src:/home/gspark/workspace/aicomp-live-pd-234/opt_prime/examples/pd/serving_matrix')
+result={'model':a.label,'width':a.width,'cases':[],'scope':'same-route real RNGD to GPU token parity, not cross-backend quality or speedup'}
+result['source_sha256']={name:hashlib.sha256(Path(path).read_bytes()).hexdigest() for name,path in [('prefill_server.py','/home/gspark/workspace/aicomp-rngd-stream-265/opt_prime/opt_prime/accel/pd/prefill_server.py'),('streaming_factories.py','/home/gspark/workspace/aicomp-rngd-stream-265/opt_prime/examples/pd/streaming_factories.py')]}
+try:
+ for stream in [False,True]:
+  print('start mode',stream,flush=True)
+  worker=SubprocessPrefillWorker('/tmp/stream265-python.sh',['--engine','factory','--engine-factory',f'streaming_factories:{a.family}','--model',a.model,'--width',str(a.width),'--chip','0'],slots=2,layers=c.num_hidden_layers,kv_heads=c.num_key_value_heads,head_dim=getattr(c,'head_dim',None) or c.hidden_size//c.num_attention_heads,max_context=a.width+64,env=env,stream_layers=stream)
+  try:
+   batch=PDBatcher(model,worker,slots=2,max_context=a.width+64,page_size=64,prefix_cache=False)
+   futures=[batch.submit(PDRequest(f'{stream}-{i}',ids,8)) for i,ids in enumerate(prompts)]
+   batch.run_until_idle();tokens=[f.result(timeout=1) for f in futures]
+   case={'stream_layers':stream,'tokens':tokens,'stats':dict(batch.stats),'idle':batch.idle}
+   result['cases'].append(case);out.write_text(json.dumps(result,indent=2)+'\n');print(json.dumps(case),flush=True)
+  finally:worker.close()
+ result['tokens_exact']=result['cases'][0]['tokens']==result['cases'][1]['tokens']
+ assert result['tokens_exact']
+ assert result['cases'][1]['stats']['progress_spans']>0
+ result['stage']='complete'
+except Exception as error:
+ result['stage']='failed';result['error']=repr(error);raise
+finally:out.write_text(json.dumps(result,indent=2)+'\n')
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-gpu-queue-failed-list.json b/opt_prime/examples/pd/http_matrix/evidence265/stream265-gpu-queue-failed-list.json
new file mode 100644
index 00000000..7807f9e4
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-gpu-queue-failed-list.json
@@ -0,0 +1,6 @@
+[
+  {
+    "label": "qwen06b",
+    "exit_code": 1
+  }
+]
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-gpu-queue.json b/opt_prime/examples/pd/http_matrix/evidence265/stream265-gpu-queue.json
new file mode 100644
index 00000000..ef57f7cc
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-gpu-queue.json
@@ -0,0 +1,18 @@
+[
+  {
+    "label": "qwen06b",
+    "exit_code": 0
+  },
+  {
+    "label": "qwen4b",
+    "exit_code": 0
+  },
+  {
+    "label": "gemma",
+    "exit_code": 0
+  },
+  {
+    "label": "koni",
+    "exit_code": 0
+  }
+]
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-gpu-queue.py b/opt_prime/examples/pd/http_matrix/evidence265/stream265-gpu-queue.py
new file mode 100644
index 00000000..cb25aea3
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-gpu-queue.py
@@ -0,0 +1,13 @@
+import os,subprocess,json
+from pathlib import Path
+root=Path('/home/gspark/workspace/npu_offload/analysis/serving-matrix-201')
+env=dict(os.environ,OMP_NUM_THREADS='4',MKL_NUM_THREADS='4',TORCH_DEVICE_BACKEND_AUTOLOAD='0',PYTHONPATH='/home/gspark/workspace/aicomp-rngd-stream-265/opt_prime:/home/gspark/workspace/aicomp-rngd-stream-265/opt_prime/examples/pd:/tmp/rngd-gpu201-src:/home/gspark/workspace/aicomp-live-pd-234/opt_prime/examples/pd/serving_matrix')
+rows=[]
+for label,family,model,width in [('qwen06b','qwen','/home/gspark/.cache/huggingface/hub/models--Qwen--Qwen3-0.6B/snapshots/c1899de289a04d12100db370d81485cdf75e47ca',128),('qwen4b','qwen','/tmp/serving-matrix-models/Qwen3-4B',128),('gemma','gemma','/tmp/serving-matrix-models/Gemma3-1B',128),('koni','koni','/tmp/serving-matrix-models/KONI-8B',512)]:
+ log=root/f'stream265-{label}-gpu.log'
+ print('start',label,flush=True)
+ with log.open('x') as f:
+  p=subprocess.run(['/home/gspark/venvs/pt213/bin/python','/tmp/stream265-gpu-hardware.py','--label',label,'--family',family,'--model',model,'--width',str(width)],env=env,stdout=f,stderr=subprocess.STDOUT)
+ rows.append({'label':label,'exit_code':p.returncode});(root/'stream265-gpu-queue.json').write_text(json.dumps(rows,indent=2)+'\n')
+ print('finished',label,p.returncode,flush=True)
+ if p.returncode:break
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-http-c16-manifest.json b/opt_prime/examples/pd/http_matrix/evidence265/stream265-http-c16-manifest.json
new file mode 100644
index 00000000..a0b24e37
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-http-c16-manifest.json
@@ -0,0 +1,15 @@
+{
+  "stage": "prepared_not_executed",
+  "runtime_head": "5978c35d9d478a62c6e30819f03e7962925c1ae1",
+  "sha256": {
+    "http_driver": "37884efe3d87506437eb0c2dee4a8ee31fbbf75c13b553656e70ee48f88c05b3",
+    "benchmark": "cfd4821e2a2112f3fcf7f242f3e76efe47134640e4bf22b3b63fb808be6148f0",
+    "http_module": "9f97e5a9b270dc5719e339748a6d6bdccbe6dfdf5267c1dd4451bab123687975",
+    "source_launcher": "1a417cc6dde8ec6837362153406e618b006169513851131f8d324ff46aa96d9a",
+    "serve_batched": "cf509e2c392a39aab7b47d7296447ff62cc8237e193e42d7d9cd76fdc47d9c30",
+    "factories": "77745350973cc54bf508e0c8b473efc442afef20b1c64f7c4edc179ea1471389",
+    "pd_batching": "c827775c1d399bf724a9747c3424ee5ea69f93054493bfbb71d5f0cf08fff65b",
+    "prefill_server": "5af1169ff5c6bfac6ce265ea3edf7f6a031c207bda7ce7bcbc996d7133576b1d"
+  },
+  "scope": "HTTP c16 smoke; hashes captured before launch; recheck if code changes"
+}
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-http-c16-queue.json b/opt_prime/examples/pd/http_matrix/evidence265/stream265-http-c16-queue.json
new file mode 100644
index 00000000..b26b47c8
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-http-c16-queue.json
@@ -0,0 +1,62 @@
+[
+  {
+    "model": "qwen06b",
+    "mode": "native",
+    "exit_code": 0
+  },
+  {
+    "model": "qwen06b",
+    "mode": "whole",
+    "exit_code": 0
+  },
+  {
+    "model": "qwen06b",
+    "mode": "stream",
+    "exit_code": 0
+  },
+  {
+    "model": "qwen4b",
+    "mode": "native",
+    "exit_code": 0
+  },
+  {
+    "model": "qwen4b",
+    "mode": "whole",
+    "exit_code": 0
+  },
+  {
+    "model": "qwen4b",
+    "mode": "stream",
+    "exit_code": 0
+  },
+  {
+    "model": "gemma",
+    "mode": "native",
+    "exit_code": 0
+  },
+  {
+    "model": "gemma",
+    "mode": "whole",
+    "exit_code": 0
+  },
+  {
+    "model": "gemma",
+    "mode": "stream",
+    "exit_code": 0
+  },
+  {
+    "model": "koni",
+    "mode": "native",
+    "exit_code": 0
+  },
+  {
+    "model": "koni",
+    "mode": "whole",
+    "exit_code": 0
+  },
+  {
+    "model": "koni",
+    "mode": "stream",
+    "exit_code": 0
+  }
+]
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-id-owner-final.log b/opt_prime/examples/pd/http_matrix/evidence265/stream265-id-owner-final.log
new file mode 100644
index 00000000..1221e0f8
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-id-owner-final.log
@@ -0,0 +1,19 @@
+..................                                                       [100%]
+=============================== warnings summary ===============================
+tests/test_pd_stream_admission.py::test_hybrid_partial_import_happens_before_completion_without_admission[cpu]
+  /home/gspark/venvs/pt213/lib/python3.10/site-packages/torch/nn/attention/flex_attention.py:2481: UserWarning: flex_attention called without torch.compile() - this will use an unfused implementation that materializes the full scores matrix instead of generating a fused kernel.
+
+  SOLUTION: Use torch.compile(flex_attention)(...)
+
+  If you want to debug your score_mod/mask_mod, you can set:
+  torch.nn.attention.flex_attention._FLEX_ATTENTION_DISABLE_COMPILE_DEBUG = True
+
+  This will allow you to use print statements or breakpoints. Note: This doesn't work with the backwards pass and may produce incorrect results.
+    _warn_once(
+
+tests/test_pd_stream_admission.py: 14 warnings
+  /home/gspark/venvs/pt213/lib/python3.10/site-packages/torch/jit/_script.py:365: DeprecationWarning: `torch.jit.script_method` is deprecated. Please switch to `torch.compile` or `torch.export`.
+    warnings.warn(
+
+-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html
+18 passed, 15 warnings in 9.44s
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-id-red.log b/opt_prime/examples/pd/http_matrix/evidence265/stream265-id-red.log
new file mode 100644
index 00000000..407a3401
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-id-red.log
@@ -0,0 +1,24 @@
+F                                                                        [100%]
+=================================== FAILURES ===================================
+________ test_boolean_response_id_cannot_alias_another_inflight_request ________
+
+children = (<function children.<locals>.make at 0x7c91f15a70a0>, [<Popen: returncode: None args: ['/tmp/pytest-of-gspark/pytest-188/test_boole...>], ['psm_19b6ddc5'], PosixPath('/tmp/pytest-of-gspark/pytest-188/test_boolean_response_id_canno0/gate'))
+
+    def test_boolean_response_id_cannot_alias_another_inflight_request(children):
+        w,gate=make_stream(children,'bad_id')
+        first=w.submit(PDRequest('first',(1,2),1),slot=0)
+        second=w.submit(PDRequest('second',(1,2),1),slot=1)
+        gate.touch()
+        for f in [first,second]:
+            with pytest.raises(RuntimeError):f.result(timeout=3)
+>       assert second.kv_progress.drain()==[], 'boolean ID was routed to integer request1'
+E       AssertionError: boolean ID was routed to integer request1
+E       assert [(0, 0, tenso...ch.bfloat16))] == []
+E
+E         Left contains one more item: (0, 0, tensor([[[[1., 1.]]]], dtype=torch.bfloat16), tensor([[[[1., 1.]]]], dtype=torch.bfloat16))
+E         Use -v to get more diff
+
+tests/test_subprocess_layer_progress.py:132: AssertionError
+=========================== short test summary info ============================
+FAILED tests/test_subprocess_layer_progress.py::test_boolean_response_id_cannot_alias_another_inflight_request
+1 failed in 1.43s
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-import-red-controlled.log b/opt_prime/examples/pd/http_matrix/evidence265/stream265-import-red-controlled.log
new file mode 100644
index 00000000..5ea9f2e8
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-import-red-controlled.log
@@ -0,0 +1,97 @@
+...........FFFFFF                                                        [100%]
+=================================== FAILURES ===================================
+________ test_partial_hybrid_write_preserves_other_layers_and_positions ________
+
+    def test_partial_hybrid_write_preserves_other_layers_and_positions():
+        import torch
+        from test_pd_dense_hybrid import pool
+        cache=pool()
+>       assert hasattr(cache,'write_layer_span'), 'partial receiver import is not implemented'
+E       AssertionError: partial receiver import is not implemented
+E       assert False
+E        +  where False = hasattr(<opt_prime.accel.pd.paged.PagedKV object at 0x7a8c9345b7f0>, 'write_layer_span')
+
+tests/test_pd_layer_progress.py:58: AssertionError
+__ test_partial_write_rejects_unreserved_positions_and_unknown_layers[0-10-0] __
+
+slot = 0, start = 10, layer = 0
+
+    @pytest.mark.parametrize('slot,start,layer',[(0,10,0),(0,-1,0),(1,0,0),(0,0,2)])
+    def test_partial_write_rejects_unreserved_positions_and_unknown_layers(slot,start,layer):
+        import torch
+        from test_pd_dense_hybrid import pool
+        cache=pool()
+>       assert hasattr(cache,'write_layer_span'), 'partial receiver import is not implemented'
+E       AssertionError: partial receiver import is not implemented
+E       assert False
+E        +  where False = hasattr(<opt_prime.accel.pd.paged.PagedKV object at 0x7a8bbd226980>, 'write_layer_span')
+
+tests/test_pd_layer_progress.py:75: AssertionError
+__ test_partial_write_rejects_unreserved_positions_and_unknown_layers[0--1-0] __
+
+slot = 0, start = -1, layer = 0
+
+    @pytest.mark.parametrize('slot,start,layer',[(0,10,0),(0,-1,0),(1,0,0),(0,0,2)])
+    def test_partial_write_rejects_unreserved_positions_and_unknown_layers(slot,start,layer):
+        import torch
+        from test_pd_dense_hybrid import pool
+        cache=pool()
+>       assert hasattr(cache,'write_layer_span'), 'partial receiver import is not implemented'
+E       AssertionError: partial receiver import is not implemented
+E       assert False
+E        +  where False = hasattr(<opt_prime.accel.pd.paged.PagedKV object at 0x7a8bbd233970>, 'write_layer_span')
+
+tests/test_pd_layer_progress.py:75: AssertionError
+__ test_partial_write_rejects_unreserved_positions_and_unknown_layers[1-0-0] ___
+
+slot = 1, start = 0, layer = 0
+
+    @pytest.mark.parametrize('slot,start,layer',[(0,10,0),(0,-1,0),(1,0,0),(0,0,2)])
+    def test_partial_write_rejects_unreserved_positions_and_unknown_layers(slot,start,layer):
+        import torch
+        from test_pd_dense_hybrid import pool
+        cache=pool()
+>       assert hasattr(cache,'write_layer_span'), 'partial receiver import is not implemented'
+E       AssertionError: partial receiver import is not implemented
+E       assert False
+E        +  where False = hasattr(<opt_prime.accel.pd.paged.PagedKV object at 0x7a8bbd2259f0>, 'write_layer_span')
+
+tests/test_pd_layer_progress.py:75: AssertionError
+__ test_partial_write_rejects_unreserved_positions_and_unknown_layers[0-0-2] ___
+
+slot = 0, start = 0, layer = 2
+
+    @pytest.mark.parametrize('slot,start,layer',[(0,10,0),(0,-1,0),(1,0,0),(0,0,2)])
+    def test_partial_write_rejects_unreserved_positions_and_unknown_layers(slot,start,layer):
+        import torch
+        from test_pd_dense_hybrid import pool
+        cache=pool()
+>       assert hasattr(cache,'write_layer_span'), 'partial receiver import is not implemented'
+E       AssertionError: partial receiver import is not implemented
+E       assert False
+E        +  where False = hasattr(<opt_prime.accel.pd.paged.PagedKV object at 0x7a8bbd216bc0>, 'write_layer_span')
+
+tests/test_pd_layer_progress.py:75: AssertionError
+_______ test_partial_write_rejects_later_group_geometry_before_mutation ________
+
+    def test_partial_write_rejects_later_group_geometry_before_mutation():
+        import torch
+        from opt_prime.accel.pd.paged import PagedKV
+        from opt_prime.accel.pd.torch_models import KVGroup
+        cache=PagedKV(2,1,1,2,torch.float32,'cpu',pages=4,page_size=4,max_context=16,
+            groups=(KVGroup('a',None,1,2,(0,)),KVGroup('b',None,2,2,(1,))))
+        cache.alloc(0,12)
+>       assert hasattr(cache,'write_layer_span'), 'partial receiver import is not implemented'
+E       AssertionError: partial receiver import is not implemented
+E       assert False
+E        +  where False = hasattr(<opt_prime.accel.pd.paged.PagedKV object at 0x7a8bbd21f460>, 'write_layer_span')
+
+tests/test_pd_layer_progress.py:88: AssertionError
+=========================== short test summary info ============================
+FAILED tests/test_pd_layer_progress.py::test_partial_hybrid_write_preserves_other_layers_and_positions
+FAILED tests/test_pd_layer_progress.py::test_partial_write_rejects_unreserved_positions_and_unknown_layers[0-10-0]
+FAILED tests/test_pd_layer_progress.py::test_partial_write_rejects_unreserved_positions_and_unknown_layers[0--1-0]
+FAILED tests/test_pd_layer_progress.py::test_partial_write_rejects_unreserved_positions_and_unknown_layers[1-0-0]
+FAILED tests/test_pd_layer_progress.py::test_partial_write_rejects_unreserved_positions_and_unknown_layers[0-0-2]
+FAILED tests/test_pd_layer_progress.py::test_partial_write_rejects_later_group_geometry_before_mutation
+6 failed, 11 passed in 2.43s
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-koni-atom.json b/opt_prime/examples/pd/http_matrix/evidence265/stream265-koni-atom.json
new file mode 100644
index 00000000..106d10f0
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-koni-atom.json
@@ -0,0 +1,552 @@
+{
+  "model": "KISTI-KONI/KONI-Llama3.1-8B-Instruct-20241024",
+  "width": 512,
+  "output_tokens": 8,
+  "batch_size": 1,
+  "cases": [
+    {
+      "stream": false,
+      "prefetch": false,
+      "tokens": [
+        [
+          51289,
+          374,
+          279,
+          6864,
+          315,
+          4987,
+          12126,
+          13
+        ],
+        [
+          11206,
+          374,
+          279,
+          6864,
+          315,
+          4987,
+          12126,
+          13
+        ]
+      ],
+      "tokens_exact": true,
+      "progress_spans": 0,
+      "imports": [],
+      "idle": true
+    },
+    {
+      "stream": true,
+      "prefetch": false,
+      "tokens": [
+        [
+          51289,
+          374,
+          279,
+          6864,
+          315,
+          4987,
+          12126,
+          13
+        ],
+        [
+          11206,
+          374,
+          279,
+          6864,
+          315,
+          4987,
+          12126,
+          13
+        ]
+      ],
+      "tokens_exact": true,
+      "progress_spans": 64,
+      "imports": [
+        {
+          "spans": 1,
+          "active_decode": 0,
+          "source_done_before": false,
+          "source_done_after": false
+        },
+        {
+          "spans": 12,
+          "active_decode": 0,
+          "source_done_before": false,
+          "source_done_after": false
+        },
+        {
+          "spans": 1,
+          "active_decode": 0,
+          "source_done_before": false,
+          "source_done_after": false
+        },
+        {
+          "spans": 1,
+          "active_decode": 0,
+          "source_done_before": false,
+          "source_done_after": false
+        },
+        {
+          "spans": 1,
+          "active_decode": 0,
+          "source_done_before": false,
+          "source_done_after": false
+        },
+        {
+          "spans": 1,
+          "active_decode": 0,
+          "source_done_before": false,
+          "source_done_after": false
+        },
+        {
+          "spans": 1,
+          "active_decode": 0,
+          "source_done_before": false,
+          "source_done_after": false
+        },
+        {
+          "spans": 1,
+          "active_decode": 0,
+          "source_done_before": false,
+          "source_done_after": false
+        },
+        {
+          "spans": 1,
+          "active_decode": 0,
+          "source_done_before": false,
+          "source_done_after": false
+        },
+        {
+          "spans": 1,
+          "active_decode": 0,
+          "source_done_before": false,
+          "source_done_after": false
+        },
+        {
+          "spans": 1,
+          "active_decode": 0,
+          "source_done_before": false,
+          "source_done_after": false
+        },
+        {
+          "spans": 1,
+          "active_decode": 0,
+          "source_done_before": false,
+          "source_done_after": false
+        },
+        {
+          "spans": 1,
+          "active_decode": 0,
+          "source_done_before": false,
+          "source_done_after": false
+        },
+        {
+          "spans": 1,
+          "active_decode": 0,
+          "source_done_before": false,
+          "source_done_after": false
+        },
+        {
+          "spans": 1,
+          "active_decode": 0,
+          "source_done_before": false,
+          "source_done_after": false
+        },
+        {
+          "spans": 1,
+          "active_decode": 0,
+          "source_done_before": false,
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new file mode 100644
index 00000000..1e63417e
--- /dev/null
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diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-koni-c16-gpu.json b/opt_prime/examples/pd/http_matrix/evidence265/stream265-koni-c16-gpu.json
new file mode 100644
index 00000000..9cb75e89
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-koni-c16-gpu.json
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diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-koni-c16-gpu.log b/opt_prime/examples/pd/http_matrix/evidence265/stream265-koni-c16-gpu.log
new file mode 100644
index 00000000..a4bf9bb5
--- /dev/null
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+GPU loading koni
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Loading weights:   0%|          | 0/291 [00:00<?, ?it/s]
Loading weights: 100%|██████████| 291/291 [00:00<00:00, 8979.07it/s]
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diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-koni-c4-gpu.json b/opt_prime/examples/pd/http_matrix/evidence265/stream265-koni-c4-gpu.json
new file mode 100644
index 00000000..b4626f62
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-koni-c4-gpu.json
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diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-koni-c4-gpu.log b/opt_prime/examples/pd/http_matrix/evidence265/stream265-koni-c4-gpu.log
new file mode 100644
index 00000000..3650909f
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-koni-c4-gpu.log
@@ -0,0 +1,12 @@
+GPU loading koni
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+Loading weights:   0%|          | 0/291 [00:00<?, ?it/s]
+Loading weights: 100%|██████████| 291/291 [00:00<00:00, 8454.09it/s]
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diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-koni-control-gpu.json b/opt_prime/examples/pd/http_matrix/evidence265/stream265-koni-control-gpu.json
new file mode 100644
index 00000000..5362c171
--- /dev/null
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diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-koni-control-gpu.log b/opt_prime/examples/pd/http_matrix/evidence265/stream265-koni-control-gpu.log
new file mode 100644
index 00000000..b2d31c82
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-koni-control-gpu.log
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+GPU loading koni
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+Loading weights:   0%|          | 0/291 [00:00<?, ?it/s]
+Loading weights: 100%|██████████| 291/291 [00:00<00:00, 9117.71it/s]
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diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-koni-fault-atom.json b/opt_prime/examples/pd/http_matrix/evidence265/stream265-koni-fault-atom.json
new file mode 100644
index 00000000..00270702
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-koni-fault-atom.json
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+    }
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+  "scope": "actual partial-import cancellation, lane reuse and producer death; not performance",
+  "code_sha256": {
+    "accel/platforms/atom/batching.py": "d98a51d75a36cd8c379a6220adb92528b3607cf80145b85a032270494a429580",
+    "accel/pd/pd_batching.py": "c827775c1d399bf724a9747c3424ee5ea69f93054493bfbb71d5f0cf08fff65b",
+    "accel/pd/prefill_server.py": "5af1169ff5c6bfac6ce265ea3edf7f6a031c207bda7ce7bcbc996d7133576b1d",
+    "accel/pd/layer_progress.py": "19c8853cd20b386dc11064d4be63fa696468c86a26b76ecf5c48cdf3905a700b"
+  },
+  "stage": "complete"
+}
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-koni-fault-atom.log b/opt_prime/examples/pd/http_matrix/evidence265/stream265-koni-fault-atom.log
new file mode 100644
index 00000000..328c5c6e
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-koni-fault-atom.log
@@ -0,0 +1 @@
+ATOM_BINDING {"visible_count": 1, "physical": 3, "opened": ["/dev/rbln3"]}
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-koni-fault-gpu-v2.json b/opt_prime/examples/pd/http_matrix/evidence265/stream265-koni-fault-gpu-v2.json
new file mode 100644
index 00000000..e05b42f3
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-koni-fault-gpu-v2.json
@@ -0,0 +1,43 @@
+{
+  "model": "koni",
+  "width": 8192,
+  "input_lengths": [
+    2048,
+    8192
+  ],
+  "output_tokens": 512,
+  "concurrency": 2,
+  "runs": [
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+          "admitted": false
+        }
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+      "idle": true,
+      "prefix_cache": false
+    },
+    {
+      "case": "producer-death",
+      "trigger": [
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+          "spans": 33,
+          "admitted": false,
+          "active_tokens": 10
+        }
+      ],
+      "error": "RuntimeError('prefill subprocess response failed: prefill subprocess exited')",
+      "idle": true,
+      "prefix_cache": false
+    }
+  ],
+  "scope": "actual streaming cancellation/reuse and source death with active512-token decode; same-route parity, not performance",
+  "source_sha256": {
+    "prefill_server.py": "5af1169ff5c6bfac6ce265ea3edf7f6a031c207bda7ce7bcbc996d7133576b1d",
+    "streaming_factories.py": "77745350973cc54bf508e0c8b473efc442afef20b1c64f7c4edc179ea1471389"
+  },
+  "stage": "complete"
+}
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-koni-fault-gpu-v2.log b/opt_prime/examples/pd/http_matrix/evidence265/stream265-koni-fault-gpu-v2.log
new file mode 100644
index 00000000..ccfd7848
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-koni-fault-gpu-v2.log
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diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-koni-gpu.json b/opt_prime/examples/pd/http_matrix/evidence265/stream265-koni-gpu.json
new file mode 100644
index 00000000..7472f8a9
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-koni-gpu.json
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diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-koni-gpu.log b/opt_prime/examples/pd/http_matrix/evidence265/stream265-koni-gpu.log
new file mode 100644
index 00000000..0730d792
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+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-koni-gpu.log
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diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-koni-http-c16-native.json b/opt_prime/examples/pd/http_matrix/evidence265/stream265-koni-http-c16-native.json
new file mode 100644
index 00000000..9154f412
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-koni-http-c16-native.json
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+          "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the",
+          "tokens_equal": true
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+          "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the",
+          "tokens_equal": true
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+          "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the",
+          "tokens_equal": true
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+          "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the",
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+          "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the",
+          "tokens_equal": true
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+          "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the",
+          "tokens_equal": true
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+          "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the Eiffel Tower, the Louvre Museum, and Notre-Dame Cathedral. Paris is also a major center for culture, fashion, and international politics. The city has a rich history and has been the capital of France since the 12th century. It is a popular tourist destination and a hub for art, cuisine, and entertainment. The city's beautiful architecture, vibrant streets, and world-class museums make it a must-visit for anyone interested in exploring the culture and heritage",
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+          "tokens_equal": true
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+          "tokens_equal": true
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+          "text": " The capital of France is Paris. \nExplanation: Paris is the largest city in France and serves as the country's political, economic, and cultural center. It is known for its iconic landmarks like the Eiffel Tower, the Louvre Museum, and the Notre-Dame Cathedral. Paris has a rich history, being a major hub for art, fashion, and gastronomy. It attracts millions of tourists each year, making it one of the most visited cities in the world. The city is also famous for its beautiful gardens, picturesque riverside walks, and vibrant neighborhoods like Montmartre and Le Marais. French is the primary language",
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+          "text": " The capital of France is Paris. \nExplanation: Paris is the largest city in France and serves as the country's political, economic, and cultural center. It is known for its iconic landmarks like the Eiffel Tower, the Louvre Museum, and the Notre-Dame Cathedral. Paris has a rich history, being a major hub for art, fashion, and gastronomy. It attracts millions of tourists each year, making it one of the most visited cities in the world. The city is also famous for its beautiful gardens, picturesque riverside walks, and vibrant neighborhoods like Montmartre and Le Marais. French is the primary language",
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+          "text": " The capital of France is Paris. \nExplanation: Paris is the largest city in France and serves as the country's political, economic, and cultural center. It is known for its iconic landmarks like the Eiffel Tower, the Louvre Museum, and the Notre-Dame Cathedral. Paris has a rich history, being a major hub for art, fashion, and gastronomy. It attracts millions of tourists each year, making it one of the most visited cities in the world. The city is also famous for its beautiful gardens, picturesque riverside walks, and vibrant neighborhoods like Montmartre and Le Marais. French is the primary language",
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+          "text": " The capital of France is Paris. \nExplanation: Paris is the largest city in France and serves as the country's political, economic, and cultural center. It is known for its iconic landmarks like the Eiffel Tower, the Louvre Museum, and the Notre-Dame Cathedral. Paris has a rich history, being a major hub for art, fashion, and gastronomy. It attracts millions of tourists each year, making it one of the most visited cities in the world. The city is also famous for its beautiful gardens, picturesque riverside walks, and vibrant neighborhoods like Montmartre and Le Marais. French is the primary language",
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+          "text": " The capital of France is Paris. \nExplanation: Paris is the largest city in France and serves as the country's political, economic, and cultural center. It is known for its iconic landmarks like the Eiffel Tower, the Louvre Museum, and the Notre-Dame Cathedral. Paris has a rich history, being a major hub for art, fashion, and gastronomy. It attracts millions of tourists each year, making it one of the most visited cities in the world. The city is also famous for its beautiful gardens, picturesque riverside walks, and vibrant neighborhoods like Montmartre and Le Marais. French is the primary language",
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+          "text": " Paris\nExplanation: Paris is the capital city of France. It is located in the northern part of the country and is known for its beautiful architecture, art museums",
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+          "text": " Paris\nExplanation: Paris is the capital city of France. It is located in the northern part of the country and is known for its beautiful architecture, art museums",
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+          "text": " Paris\nExplanation: Paris is the capital city of France. It is located in the northern part of the country and is known for its beautiful architecture, art museums",
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+          "text": " Paris\nExplanation: Paris is the capital city of France. It is located in the northern part of the country and is known for its beautiful architecture, art museums",
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+          "text": " Paris\nExplanation: Paris is the capital city of France. It is located in the northern part of the country and is known for its beautiful architecture, art museums, and historic landmarks such as the Eiffel Tower and Notre-Dame Cathedral. Paris has been a center of culture and politics for centuries and is considered one of the most romantic and visited cities in the world. The city is also famous for its fashion industry, cuisine, and vibrant cultural scene. The official language spoken in Paris is French. Paris is a popular tourist destination, attracting millions of visitors each year who come to explore its rich history, art, and culture. The",
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+          "text": " Paris\nExplanation: Paris is the capital city of France. It is located in the northern part of the country and is known for its beautiful architecture, art museums, and historic landmarks such as the Eiffel Tower and Notre-Dame Cathedral. Paris has been a center of culture and politics for centuries and is considered one of the most romantic and visited cities in the world. The city is also famous for its fashion industry, cuisine, and vibrant cultural scene. The official language spoken in Paris is French. Paris is a popular tourist destination, attracting millions of visitors each year who come to explore its rich history, art, and culture. The",
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+          "text": " Paris\nExplanation: Paris is the capital city of France. It is located in the northern part of the country and is known for its beautiful architecture, art museums, and historic landmarks such as the Eiffel Tower and Notre-Dame Cathedral. Paris has been a center of culture and politics for centuries and is considered one of the most romantic and visited cities in the world. The city is also famous for its fashion industry, cuisine, and vibrant cultural scene. The official language spoken in Paris is French. Paris is a popular tourist destination, attracting millions of visitors each year who come to explore its rich history, art, and culture. The",
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+          "text": " Paris\nExplanation: Paris is the capital city of France. It is located in the northern part of the country and is known for its beautiful architecture, art museums, and historic landmarks such as the Eiffel Tower and Notre-Dame Cathedral. Paris has been a center of culture and politics for centuries and is considered one of the most romantic and visited cities in the world. The city is also famous for its fashion industry, cuisine, and vibrant cultural scene. The official language spoken in Paris is French. Paris is a popular tourist destination, attracting millions of visitors each year who come to explore its rich history, art, and culture. The",
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+          "text": " Paris\nExplanation: Paris is the capital city of France. It is located in the northern part of the country and is known for its beautiful architecture, art museums, and historic landmarks such as the Eiffel Tower and Notre-Dame Cathedral. Paris has been a center of culture and politics for centuries and is considered one of the most romantic and visited cities in the world. The city is also famous for its fashion industry, cuisine, and vibrant cultural scene. The official language spoken in Paris is French. Paris is a popular tourist destination, attracting millions of visitors each year who come to explore its rich history, art, and culture. The",
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+          "text": " Paris\nExplanation: Paris is the capital city of France. It is located in the northern part of the country and is known for its beautiful architecture, art museums, and historic landmarks such as the Eiffel Tower and Notre-Dame Cathedral. Paris has been a center of culture and politics for centuries and is considered one of the most romantic and visited cities in the world. The city is also famous for its fashion industry, cuisine, and vibrant cultural scene. The official language spoken in Paris is French. Paris is a popular tourist destination, attracting millions of visitors each year who come to explore its rich history, art, and culture. The",
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+          "text": " Paris\nExplanation: Paris is the capital city of France. It is located in the northern part of the country and is known for its beautiful architecture, art museums, and historic landmarks such as the Eiffel Tower and Notre-Dame Cathedral. Paris has been a center of culture and politics for centuries and is considered one of the most romantic and visited cities in the world. The city is also famous for its fashion industry, cuisine, and vibrant cultural scene. The official language spoken in Paris is French. Paris is a popular tourist destination, attracting millions of visitors each year who come to explore its rich history, art, and culture. The",
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+          "text": " Paris\nExplanation: Paris is the capital city of France. It is located in the northern part of the country and is known for its beautiful architecture, art museums, and historic landmarks such as the Eiffel Tower and Notre-Dame Cathedral. Paris has been a center of culture and politics for centuries and is considered one of the most romantic and visited cities in the world. The city is also famous for its fashion industry, cuisine, and vibrant cultural scene. The official language spoken in Paris is French. Paris is a popular tourist destination, attracting millions of visitors each year who come to explore its rich history, art, and culture. The",
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+          "text": " Paris\nExplanation: Paris is the capital city of France. It is located in the northern part of the country and is known for its beautiful architecture, art museums, and historic landmarks such as the Eiffel Tower and Notre-Dame Cathedral. Paris has been a center of culture and politics for centuries and is considered one of the most romantic and visited cities in the world. The city is also famous for its fashion industry, cuisine, and vibrant cultural scene. The official language spoken in Paris is French. Paris is a popular tourist destination, attracting millions of visitors each year who come to explore its rich history, art, and culture. The",
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+          "text": " Paris\nExplanation: Paris is the capital city of France. It is located in the northern part of the country and is known for its beautiful architecture, art museums, and historic landmarks such as the Eiffel Tower and Notre-Dame Cathedral. Paris has been a center of culture and politics for centuries and is considered one of the most romantic and visited cities in the world. The city is also famous for its fashion industry, cuisine, and vibrant cultural scene. The official language spoken in Paris is French. Paris is a popular tourist destination, attracting millions of visitors each year who come to explore its rich history, art, and culture. The",
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+          "text": " Paris\nExplanation: Paris is the capital city of France. It is located in the northern part of the country and is known for its beautiful architecture, art museums, and historic landmarks such as the Eiffel Tower and Notre-Dame Cathedral. Paris has been a center of culture and politics for centuries and is considered one of the most romantic and visited cities in the world. The city is also famous for its fashion industry, cuisine, and vibrant cultural scene. The official language spoken in Paris is French. Paris is a popular tourist destination, attracting millions of visitors each year who come to explore its rich history, art, and culture. The",
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+          "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the",
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+          "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the",
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+          "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the",
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+          "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the",
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+          "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the",
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+          "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the",
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+          "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the",
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+          "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the",
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+          "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the",
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+          "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the Eiffel Tower, the Louvre Museum, and Notre-Dame Cathedral. Paris is also a major center for culture, fashion, and international politics. The city has a rich history and has been the capital of France since the 12th century. It is a popular tourist destination and a hub for art, cuisine, and entertainment. The city's beautiful architecture, vibrant streets, and world-class museums make it a must-visit for anyone interested in exploring the culture and heritage",
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+          "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the Eiffel Tower, the Louvre Museum, and Notre-Dame Cathedral. Paris is also a major center for culture, fashion, and international politics. The city has a rich history and has been the capital of France since the 12th century. It is a popular tourist destination and a hub for art, cuisine, and entertainment. The city's beautiful architecture, vibrant streets, and world-class museums make it a must-visit for anyone interested in exploring the culture and heritage",
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+          "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the Eiffel Tower, the Louvre Museum, and Notre-Dame Cathedral. Paris is also a major center for culture, fashion, and international politics. The city has a rich history and has been the capital of France since the 12th century. It is a popular tourist destination and a hub for art, cuisine, and entertainment. The city's beautiful architecture, vibrant streets, and world-class museums make it a must-visit for anyone interested in exploring the culture and heritage",
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+          "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the Eiffel Tower, the Louvre Museum, and Notre-Dame Cathedral. Paris is also a major center for culture, fashion, and international politics. The city has a rich history and has been the capital of France since the 12th century. It is a popular tourist destination and a hub for art, cuisine, and entertainment. The city's beautiful architecture, vibrant streets, and world-class museums make it a must-visit for anyone interested in exploring the culture and heritage",
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+          "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the Eiffel Tower, the Louvre Museum, and Notre-Dame Cathedral. Paris is also a major center for culture, fashion, and international politics. The city has a rich history and has been the capital of France since the 12th century. It is a popular tourist destination and a hub for art, cuisine, and entertainment. The city's beautiful architecture, vibrant streets, and world-class museums make it a must-visit for anyone interested in exploring the culture and heritage",
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+          "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the Eiffel Tower, the Louvre Museum, and Notre-Dame Cathedral. Paris is also a major center for culture, fashion, and international politics. The city has a rich history and has been the capital of France since the 12th century. It is a popular tourist destination and a hub for art, cuisine, and entertainment. The city's beautiful architecture, vibrant streets, and world-class museums make it a must-visit for anyone interested in exploring the culture and heritage",
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+          "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the Eiffel Tower, the Louvre Museum, and Notre-Dame Cathedral. Paris is also a major center for culture, fashion, and international politics. The city has a rich history and has been the capital of France since the 12th century. It is a popular tourist destination and a hub for art, cuisine, and entertainment. The city's beautiful architecture, vibrant streets, and world-class museums make it a must-visit for anyone interested in exploring the culture and heritage",
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+          "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the Eiffel Tower, the Louvre Museum, and Notre-Dame Cathedral. Paris is also a major center for culture, fashion, and international politics. The city has a rich history and has been the capital of France since the 12th century. It is a popular tourist destination and a hub for art, cuisine, and entertainment. The city's beautiful architecture, vibrant streets, and world-class museums make it a must-visit for anyone interested in exploring the culture and heritage",
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+          "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the Eiffel Tower, the Louvre Museum, and Notre-Dame Cathedral. Paris is also a major center for culture, fashion, and international politics. The city has a rich history and has been the capital of France since the 12th century. It is a popular tourist destination and a hub for art, cuisine, and entertainment. The city's beautiful architecture, vibrant streets, and world-class museums make it a must-visit for anyone interested in exploring the culture and heritage",
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+          "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the Eiffel Tower, the Louvre Museum, and Notre-Dame Cathedral. Paris is also a major center for culture, fashion, and international politics. The city has a rich history and has been the capital of France since the 12th century. It is a popular tourist destination and a hub for art, cuisine, and entertainment. The city's beautiful architecture, vibrant streets, and world-class museums make it a must-visit for anyone interested in exploring the culture and heritage",
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+          "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the Eiffel Tower, the Louvre Museum, and Notre-Dame Cathedral. Paris is also a major center for culture, fashion, and international politics. The city has a rich history and has been the capital of France since the 12th century. It is a popular tourist destination and a hub for art, cuisine, and entertainment. The city's beautiful architecture, vibrant streets, and world-class museums make it a must-visit for anyone interested in exploring the culture and heritage",
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+      "text": " The capital of France is Paris. \nIs this information helpful? If so, please let me know if you have any other questions. \nCutting Knowledge Date"
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+      "text": " The capital of France is Paris. \nIs this information helpful? If so, please let me know if you have any other questions. \nCutting Knowledge Date: July 2024\nToday Date: 27 Aug 2024\n\n \nNo, this information is not relevant to the given topic. \nThe conversation started with a mention of red and blue boxes containing apples and books, respectively. \nThe question about the capital of France seems out of place in this context. \nIf you have any other questions related to the original topic, feel free to ask. \nI'm here to assist you with any inquiries you may have."
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+      "text": " Paris\nExplanation: Paris is the capital city of France. It is located in the northern part of the country and is known for its beautiful architecture, art museums"
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264\nvllm:time_to_first_token_seconds_sum{model_name=\"KONI-8B\"} 2081.7858363590785\nvllm:time_to_first_token_seconds_count{model_name=\"KONI-8B\"} 264\n# HELP vllm:time_per_output_token_seconds Time per output token after the first (decode stage).\n# TYPE vllm:time_per_output_token_seconds histogram\nvllm:time_per_output_token_seconds_bucket{model_name=\"KONI-8B\",le=\"0.01\"} 0\nvllm:time_per_output_token_seconds_bucket{model_name=\"KONI-8B\",le=\"0.025\"} 115\nvllm:time_per_output_token_seconds_bucket{model_name=\"KONI-8B\",le=\"0.05\"} 179\nvllm:time_per_output_token_seconds_bucket{model_name=\"KONI-8B\",le=\"0.075\"} 234\nvllm:time_per_output_token_seconds_bucket{model_name=\"KONI-8B\",le=\"0.1\"} 264\nvllm:time_per_output_token_seconds_bucket{model_name=\"KONI-8B\",le=\"0.15\"} 264\nvllm:time_per_output_token_seconds_bucket{model_name=\"KONI-8B\",le=\"0.2\"} 264\nvllm:time_per_output_token_seconds_bucket{model_name=\"KONI-8B\",le=\"0.3\"} 264\nvllm:time_per_output_token_seconds_bucket{model_name=\"KONI-8B\",le=\"0.4\"} 264\nvllm:time_per_output_token_seconds_bucket{model_name=\"KONI-8B\",le=\"0.5\"} 264\nvllm:time_per_output_token_seconds_bucket{model_name=\"KONI-8B\",le=\"0.75\"} 264\nvllm:time_per_output_token_seconds_bucket{model_name=\"KONI-8B\",le=\"1.0\"} 264\nvllm:time_per_output_token_seconds_bucket{model_name=\"KONI-8B\",le=\"2.5\"} 264\nvllm:time_per_output_token_seconds_bucket{model_name=\"KONI-8B\",le=\"+Inf\"} 264\nvllm:time_per_output_token_seconds_sum{model_name=\"KONI-8B\"} 9.917622707842597\nvllm:time_per_output_token_seconds_count{model_name=\"KONI-8B\"} 264\n# HELP vllm:e2e_request_latency_seconds Stage request latency, from execution start to reply.\n# TYPE vllm:e2e_request_latency_seconds histogram\nvllm:e2e_request_latency_seconds_bucket{model_name=\"KONI-8B\",le=\"0.3\"} 0\nvllm:e2e_request_latency_seconds_bucket{model_name=\"KONI-8B\",le=\"0.5\"} 0\nvllm:e2e_request_latency_seconds_bucket{model_name=\"KONI-8B\",le=\"0.8\"} 5\nvllm:e2e_request_latency_seconds_bucket{model_name=\"KONI-8B\",le=\"1.0\"} 12\nvllm:e2e_request_latency_seconds_bucket{model_name=\"KONI-8B\",le=\"1.5\"} 21\nvllm:e2e_request_latency_seconds_bucket{model_name=\"KONI-8B\",le=\"2.0\"} 38\nvllm:e2e_request_latency_seconds_bucket{model_name=\"KONI-8B\",le=\"2.5\"} 55\nvllm:e2e_request_latency_seconds_bucket{model_name=\"KONI-8B\",le=\"5.0\"} 108\nvllm:e2e_request_latency_seconds_bucket{model_name=\"KONI-8B\",le=\"10.0\"} 178\nvllm:e2e_request_latency_seconds_bucket{model_name=\"KONI-8B\",le=\"15.0\"} 208\nvllm:e2e_request_latency_seconds_bucket{model_name=\"KONI-8B\",le=\"20.0\"} 222\nvllm:e2e_request_latency_seconds_bucket{model_name=\"KONI-8B\",le=\"30.0\"} 237\nvllm:e2e_request_latency_seconds_bucket{model_name=\"KONI-8B\",le=\"40.0\"} 252\nvllm:e2e_request_latency_seconds_bucket{model_name=\"KONI-8B\",le=\"50.0\"} 264\nvllm:e2e_request_latency_seconds_bucket{model_name=\"KONI-8B\",le=\"60.0\"} 264\nvllm:e2e_request_latency_seconds_bucket{model_name=\"KONI-8B\",le=\"+Inf\"} 264\nvllm:e2e_request_latency_seconds_sum{model_name=\"KONI-8B\"} 2799.5537201544503\nvllm:e2e_request_latency_seconds_count{model_name=\"KONI-8B\"} 264\n# HELP airm_kv_transfer_seconds KV transfer time from the prefill source to the decode destination.\n# TYPE airm_kv_transfer_seconds histogram\nairm_kv_transfer_seconds_bucket{model_name=\"KONI-8B\",le=\"0.0005\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"KONI-8B\",le=\"0.001\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"KONI-8B\",le=\"0.0025\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"KONI-8B\",le=\"0.005\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"KONI-8B\",le=\"0.01\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"KONI-8B\",le=\"0.025\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"KONI-8B\",le=\"0.05\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"KONI-8B\",le=\"0.1\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"KONI-8B\",le=\"0.25\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"KONI-8B\",le=\"0.5\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"KONI-8B\",le=\"1.0\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"KONI-8B\",le=\"+Inf\"} 0\nairm_kv_transfer_seconds_sum{model_name=\"KONI-8B\"} 0.0\nairm_kv_transfer_seconds_count{model_name=\"KONI-8B\"} 0\n# HELP vllm:cache_config_info Information of the LLMEngine CacheConfig.\n# TYPE vllm:cache_config_info gauge\nvllm:cache_config_info{model_name=\"KONI-8B\",block_size=\"64\",num_gpu_blocks=\"576\",enable_prefix_caching=\"False\"} 1\n# HELP vllm:prefix_cache_queries_total Prompt tokens looked up in the prefix cache.\n# TYPE vllm:prefix_cache_queries_total counter\nvllm:prefix_cache_queries_total{model_name=\"KONI-8B\"} 718080\n# HELP vllm:prefix_cache_hits_total Prompt tokens served from the prefix cache.\n# TYPE vllm:prefix_cache_hits_total counter\nvllm:prefix_cache_hits_total{model_name=\"KONI-8B\"} 0\n"
+  ],
+  "parity_passed": true
+}
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-koni-http-c16-stream.json b/opt_prime/examples/pd/http_matrix/evidence265/stream265-koni-http-c16-stream.json
new file mode 100644
index 00000000..f60fa7b0
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-koni-http-c16-stream.json
@@ -0,0 +1,25227 @@
+{
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+      "text": " Paris\nExplanation: Paris is the capital city of France. It is located in the northern part of the country and is known for its beautiful architecture, art museums",
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+          "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the",
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+          "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the",
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+          "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the",
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+          "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the",
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+          "tpot_est_s": 0.019490397362016493,
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+        {
+          "id": "r0-n128-o128-c16-2",
+          "endpoint": 0,
+          "input_tokens": 128,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris. \nIs this information helpful? If so, please let me know if you have any other questions. \nCutting Knowledge Date: July 2024\nToday Date: 27 Aug 2024\n\n \nNo, this information is not relevant to the given topic. \nThe conversation topic was about boxes containing different items, and the question asked about the capital of France. \nIf you have any other questions or need further assistance, feel free to ask. \nCutting Knowledge Date: July 2024\nToday Date: 27 Aug 2024\n\n \nI'm sorry, but I don",
+          "text_equal": true,
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+        {
+          "id": "r0-n128-o128-c16-3",
+          "endpoint": 0,
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+          "output_tokens": 128,
+          "text": " The capital of France is Paris. \nIs this information helpful? If so, please let me know if you have any other questions. \nCutting Knowledge Date: July 2024\nToday Date: 27 Aug 2024\n\n \nNo, this information is not relevant to the given topic. \nThe conversation topic was about boxes containing different items, and the question asked about the capital of France. \nIf you have any other questions or need further assistance, feel free to ask. \nCutting Knowledge Date: July 2024\nToday Date: 27 Aug 2024\n\n \nI'm sorry, but I don",
+          "text_equal": true,
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+        {
+          "id": "r0-n128-o128-c16-4",
+          "endpoint": 0,
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+          "output_tokens": 128,
+          "text": " The capital of France is Paris. \nIs this information helpful? If so, please let me know if you have any other questions. \nCutting Knowledge Date: July 2024\nToday Date: 27 Aug 2024\n\n \nNo, this information is not relevant to the given topic. \nThe conversation topic was about boxes containing different items, and the question asked about the capital of France. \nIf you have any other questions or need further assistance, feel free to ask. \nCutting Knowledge Date: July 2024\nToday Date: 27 Aug 2024\n\n \nI'm sorry, but I don",
+          "text_equal": true,
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+          "output_tokens": 128,
+          "text": " The capital of France is Paris. \nIs this information helpful? If so, please let me know if you have any other questions. \nCutting Knowledge Date: July 2024\nToday Date: 27 Aug 2024\n\n \nNo, this information is not relevant to the given topic. \nThe conversation topic was about boxes containing different items, and the question asked about the capital of France. \nIf you have any other questions or need further assistance, feel free to ask. \nCutting Knowledge Date: July 2024\nToday Date: 27 Aug 2024\n\n \nI'm sorry, but I don",
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+          "output_tokens": 128,
+          "text": " The capital of France is Paris. \nIs this information helpful? If so, please let me know if you have any other questions. \nCutting Knowledge Date: July 2024\nToday Date: 27 Aug 2024\n\n \nNo, this information is not relevant to the given topic. \nThe conversation topic was about boxes containing different items, and the question asked about the capital of France. \nIf you have any other questions or need further assistance, feel free to ask. \nCutting Knowledge Date: July 2024\nToday Date: 27 Aug 2024\n\n \nI'm sorry, but I don",
+          "text_equal": true,
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+          "output_tokens": 128,
+          "text": " The capital of France is Paris. \nIs this information helpful? If so, please let me know if you have any other questions. \nCutting Knowledge Date: July 2024\nToday Date: 27 Aug 2024\n\n \nNo, this information is not relevant to the given topic. \nThe conversation topic was about boxes containing different items, and the question asked about the capital of France. \nIf you have any other questions or need further assistance, feel free to ask. \nCutting Knowledge Date: July 2024\nToday Date: 27 Aug 2024\n\n \nI'm sorry, but I don",
+          "text_equal": true,
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+          "endpoint": 0,
+          "input_tokens": 128,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris. \nIs this information helpful? If so, please let me know if you have any other questions. \nCutting Knowledge Date: July 2024\nToday Date: 27 Aug 2024\n\n \nNo, this information is not relevant to the given topic. \nThe conversation topic was about boxes containing different items, and the question asked about the capital of France. \nIf you have any other questions or need further assistance, feel free to ask. \nCutting Knowledge Date: July 2024\nToday Date: 27 Aug 2024\n\n \nI'm sorry, but I don",
+          "text_equal": true,
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+          "output_tokens": 128,
+          "text": " The capital of France is Paris. \nIs this information helpful? If so, please let me know if you have any other questions. \nCutting Knowledge Date: July 2024\nToday Date: 27 Aug 2024\n\n \nNo, this information is not relevant to the given topic. \nThe conversation topic was about boxes containing different items, and the question asked about the capital of France. \nIf you have any other questions or need further assistance, feel free to ask. \nCutting Knowledge Date: July 2024\nToday Date: 27 Aug 2024\n\n \nI'm sorry, but I don",
+          "text_equal": true,
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+          "id": "r0-n128-o128-c16-10",
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+          "output_tokens": 128,
+          "text": " The capital of France is Paris. \nIs this information helpful? If so, please let me know if you have any other questions. \nCutting Knowledge Date: July 2024\nToday Date: 27 Aug 2024\n\n \nNo, this information is not relevant to the given topic. \nThe conversation topic was about boxes containing different items, and the question asked about the capital of France. \nIf you have any other questions or need further assistance, feel free to ask. \nCutting Knowledge Date: July 2024\nToday Date: 27 Aug 2024\n\n \nI'm sorry, but I don",
+          "text_equal": true,
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+          "output_tokens": 128,
+          "text": " The capital of France is Paris. \nIs this information helpful? If so, please let me know if you have any other questions. \nCutting Knowledge Date: July 2024\nToday Date: 27 Aug 2024\n\n \nNo, this information is not relevant to the given topic. \nThe conversation topic was about boxes containing different items, and the question asked about the capital of France. \nIf you have any other questions or need further assistance, feel free to ask. \nCutting Knowledge Date: July 2024\nToday Date: 27 Aug 2024\n\n \nI'm sorry, but I don",
+          "text_equal": true,
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+          "output_tokens": 128,
+          "text": " The capital of France is Paris. \nIs this information helpful? If so, please let me know if you have any other questions. \nCutting Knowledge Date: July 2024\nToday Date: 27 Aug 2024\n\n \nNo, this information is not relevant to the given topic. \nThe conversation topic was about boxes containing different items, and the question asked about the capital of France. \nIf you have any other questions or need further assistance, feel free to ask. \nCutting Knowledge Date: July 2024\nToday Date: 27 Aug 2024\n\n \nI'm sorry, but I don",
+          "text_equal": true,
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+          "input_tokens": 128,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris. \nIs this information helpful? If so, please let me know if you have any other questions. \nCutting Knowledge Date: July 2024\nToday Date: 27 Aug 2024\n\n \nNo, this information is not relevant to the given topic. \nThe conversation topic was about boxes containing different items, and the question asked about the capital of France. \nIf you have any other questions or need further assistance, feel free to ask. \nCutting Knowledge Date: July 2024\nToday Date: 27 Aug 2024\n\n \nI'm sorry, but I don",
+          "text_equal": true,
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+          "endpoint": 0,
+          "input_tokens": 128,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris. \nIs this information helpful? If so, please let me know if you have any other questions. \nCutting Knowledge Date: July 2024\nToday Date: 27 Aug 2024\n\n \nNo, this information is not relevant to the given topic. \nThe conversation topic was about boxes containing different items, and the question asked about the capital of France. \nIf you have any other questions or need further assistance, feel free to ask. \nCutting Knowledge Date: July 2024\nToday Date: 27 Aug 2024\n\n \nI'm sorry, but I don",
+          "text_equal": true,
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+          "endpoint": 0,
+          "input_tokens": 128,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris. \nIs this information helpful? If so, please let me know if you have any other questions. \nCutting Knowledge Date: July 2024\nToday Date: 27 Aug 2024\n\n \nNo, this information is not relevant to the given topic. \nThe conversation topic was about boxes containing different items, and the question asked about the capital of France. \nIf you have any other questions or need further assistance, feel free to ask. \nCutting Knowledge Date: July 2024\nToday Date: 27 Aug 2024\n\n \nI'm sorry, but I don",
+          "text_equal": true,
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+          "id": "r0-n512-o32-c16-0",
+          "endpoint": 0,
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+          "text": " The capital of France is Paris. \nExplanation: Paris is the largest city in France and serves as the country's political, economic, and cultural center. It",
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+          "id": "r0-n512-o32-c16-1",
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+          "text": " The capital of France is Paris. \nExplanation: Paris is the largest city in France and serves as the country's political, economic, and cultural center. It",
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+          "id": "r0-n512-o32-c16-2",
+          "endpoint": 0,
+          "input_tokens": 512,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris. \nExplanation: Paris is the largest city in France and serves as the country's political, economic, and cultural center. It",
+          "text_equal": true,
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+          "text": " The capital of France is Paris. \nExplanation: Paris is the largest city in France and serves as the country's political, economic, and cultural center. It",
+          "text_equal": true,
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+          "input_tokens": 512,
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+          "text": " The capital of France is Paris. \nExplanation: Paris is the largest city in France and serves as the country's political, economic, and cultural center. It",
+          "text_equal": true,
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+          "text": " The capital of France is Paris. \nExplanation: Paris is the largest city in France and serves as the country's political, economic, and cultural center. It",
+          "text_equal": true,
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+          "text": " The capital of France is Paris. \nExplanation: Paris is the largest city in France and serves as the country's political, economic, and cultural center. It",
+          "text_equal": true,
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+          "endpoint": 0,
+          "input_tokens": 512,
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+          "text": " The capital of France is Paris. \nExplanation: Paris is the largest city in France and serves as the country's political, economic, and cultural center. It",
+          "text_equal": true,
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+          "id": "r0-n512-o32-c16-8",
+          "endpoint": 0,
+          "input_tokens": 512,
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+          "text": " The capital of France is Paris. \nExplanation: Paris is the largest city in France and serves as the country's political, economic, and cultural center. It",
+          "text_equal": true,
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+          "text": " The capital of France is Paris. \nExplanation: Paris is the largest city in France and serves as the country's political, economic, and cultural center. It",
+          "text_equal": true,
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+          "text": " The capital of France is Paris. \nExplanation: Paris is the largest city in France and serves as the country's political, economic, and cultural center. It",
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+          "text": " The capital of France is Paris. \nExplanation: Paris is the largest city in France and serves as the country's political, economic, and cultural center. It",
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+          "text": " The capital of France is Paris. \nExplanation: Paris is the largest city in France and serves as the country's political, economic, and cultural center. It",
+          "text_equal": true,
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+          "text": " The capital of France is Paris. \nExplanation: Paris is the largest city in France and serves as the country's political, economic, and cultural center. It",
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+          "endpoint": 0,
+          "input_tokens": 512,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris. \nExplanation: Paris is the largest city in France and serves as the country's political, economic, and cultural center. It",
+          "text_equal": true,
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+          "text": " The capital of France is Paris. \nExplanation: Paris is the largest city in France and serves as the country's political, economic, and cultural center. It",
+          "text_equal": true,
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+          "id": "r0-n512-o128-c16-0",
+          "endpoint": 0,
+          "input_tokens": 512,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris. \nExplanation: Paris is the largest city in France and serves as the country's political, economic, and cultural center. It is known for its iconic landmarks like the Eiffel Tower, the Louvre Museum, and the Notre-Dame Cathedral. Paris has a rich history, being a major hub for art, fashion, and gastronomy. It attracts millions of tourists each year, making it one of the most visited cities in the world. The city is also famous for its beautiful gardens, picturesque riverside walks, and vibrant neighborhoods like Montmartre and Le Marais. French is the primary language",
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+          "endpoint": 0,
+          "input_tokens": 512,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris. \nExplanation: Paris is the largest city in France and serves as the country's political, economic, and cultural center. It is known for its iconic landmarks like the Eiffel Tower, the Louvre Museum, and the Notre-Dame Cathedral. Paris has a rich history, being a major hub for art, fashion, and gastronomy. It attracts millions of tourists each year, making it one of the most visited cities in the world. The city is also famous for its beautiful gardens, picturesque riverside walks, and vibrant neighborhoods like Montmartre and Le Marais. French is the primary language",
+          "text_equal": true,
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+          "endpoint": 0,
+          "input_tokens": 512,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris. \nExplanation: Paris is the largest city in France and serves as the country's political, economic, and cultural center. It is known for its iconic landmarks like the Eiffel Tower, the Louvre Museum, and the Notre-Dame Cathedral. Paris has a rich history, being a major hub for art, fashion, and gastronomy. It attracts millions of tourists each year, making it one of the most visited cities in the world. The city is also famous for its beautiful gardens, picturesque riverside walks, and vibrant neighborhoods like Montmartre and Le Marais. French is the primary language",
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+          "text": " The capital of France is Paris. \nExplanation: Paris is the largest city in France and serves as the country's political, economic, and cultural center. It is known for its iconic landmarks like the Eiffel Tower, the Louvre Museum, and the Notre-Dame Cathedral. Paris has a rich history, being a major hub for art, fashion, and gastronomy. It attracts millions of tourists each year, making it one of the most visited cities in the world. The city is also famous for its beautiful gardens, picturesque riverside walks, and vibrant neighborhoods like Montmartre and Le Marais. French is the primary language",
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+          "text": " The capital of France is Paris. \nExplanation: Paris is the largest city in France and serves as the country's political, economic, and cultural center. It is known for its iconic landmarks like the Eiffel Tower, the Louvre Museum, and the Notre-Dame Cathedral. Paris has a rich history, being a major hub for art, fashion, and gastronomy. It attracts millions of tourists each year, making it one of the most visited cities in the world. The city is also famous for its beautiful gardens, picturesque riverside walks, and vibrant neighborhoods like Montmartre and Le Marais. French is the primary language",
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+          "text": " The capital of France is Paris. \nExplanation: Paris is the largest city in France and serves as the country's political, economic, and cultural center. It is known for its iconic landmarks like the Eiffel Tower, the Louvre Museum, and the Notre-Dame Cathedral. Paris has a rich history, being a major hub for art, fashion, and gastronomy. It attracts millions of tourists each year, making it one of the most visited cities in the world. The city is also famous for its beautiful gardens, picturesque riverside walks, and vibrant neighborhoods like Montmartre and Le Marais. French is the primary language",
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+          "text": " The capital of France is Paris. \nExplanation: Paris is the largest city in France and serves as the country's political, economic, and cultural center. It is known for its iconic landmarks like the Eiffel Tower, the Louvre Museum, and the Notre-Dame Cathedral. Paris has a rich history, being a major hub for art, fashion, and gastronomy. It attracts millions of tourists each year, making it one of the most visited cities in the world. The city is also famous for its beautiful gardens, picturesque riverside walks, and vibrant neighborhoods like Montmartre and Le Marais. French is the primary language",
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+          "text": " The capital of France is Paris. \nExplanation: Paris is the largest city in France and serves as the country's political, economic, and cultural center. It is known for its iconic landmarks like the Eiffel Tower, the Louvre Museum, and the Notre-Dame Cathedral. Paris has a rich history, being a major hub for art, fashion, and gastronomy. It attracts millions of tourists each year, making it one of the most visited cities in the world. The city is also famous for its beautiful gardens, picturesque riverside walks, and vibrant neighborhoods like Montmartre and Le Marais. French is the primary language",
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+          "text": " The capital of France is Paris. \nExplanation: Paris is the largest city in France and serves as the country's political, economic, and cultural center. It is known for its iconic landmarks like the Eiffel Tower, the Louvre Museum, and the Notre-Dame Cathedral. Paris has a rich history, being a major hub for art, fashion, and gastronomy. It attracts millions of tourists each year, making it one of the most visited cities in the world. The city is also famous for its beautiful gardens, picturesque riverside walks, and vibrant neighborhoods like Montmartre and Le Marais. French is the primary language",
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+          "text": " The capital of France is Paris. \nExplanation: Paris is the largest city in France and serves as the country's political, economic, and cultural center. It is known for its iconic landmarks like the Eiffel Tower, the Louvre Museum, and the Notre-Dame Cathedral. Paris has a rich history, being a major hub for art, fashion, and gastronomy. It attracts millions of tourists each year, making it one of the most visited cities in the world. The city is also famous for its beautiful gardens, picturesque riverside walks, and vibrant neighborhoods like Montmartre and Le Marais. French is the primary language",
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+          "text": " The capital of France is Paris. \nExplanation: Paris is the largest city in France and serves as the country's political, economic, and cultural center. It is known for its iconic landmarks like the Eiffel Tower, the Louvre Museum, and the Notre-Dame Cathedral. Paris has a rich history, being a major hub for art, fashion, and gastronomy. It attracts millions of tourists each year, making it one of the most visited cities in the world. The city is also famous for its beautiful gardens, picturesque riverside walks, and vibrant neighborhoods like Montmartre and Le Marais. French is the primary language",
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+          "text": " The capital of France is Paris. \nExplanation: Paris is the largest city in France and serves as the country's political, economic, and cultural center. It is known for its iconic landmarks like the Eiffel Tower, the Louvre Museum, and the Notre-Dame Cathedral. Paris has a rich history, being a major hub for art, fashion, and gastronomy. It attracts millions of tourists each year, making it one of the most visited cities in the world. The city is also famous for its beautiful gardens, picturesque riverside walks, and vibrant neighborhoods like Montmartre and Le Marais. French is the primary language",
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+          "text": " The capital of France is Paris. \nExplanation: Paris is the largest city in France and serves as the country's political, economic, and cultural center. It is known for its iconic landmarks like the Eiffel Tower, the Louvre Museum, and the Notre-Dame Cathedral. Paris has a rich history, being a major hub for art, fashion, and gastronomy. It attracts millions of tourists each year, making it one of the most visited cities in the world. The city is also famous for its beautiful gardens, picturesque riverside walks, and vibrant neighborhoods like Montmartre and Le Marais. French is the primary language",
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+          "text": " Paris\nExplanation: Paris is the capital city of France. It is located in the northern part of the country and is known for its beautiful architecture, art museums",
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+          "text": " Paris\nExplanation: Paris is the capital city of France. It is located in the northern part of the country and is known for its beautiful architecture, art museums",
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+          "text": " Paris\nExplanation: Paris is the capital city of France. It is located in the northern part of the country and is known for its beautiful architecture, art museums",
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+          "text": " Paris\nExplanation: Paris is the capital city of France. It is located in the northern part of the country and is known for its beautiful architecture, art museums",
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+          "text": " Paris\nExplanation: Paris is the capital city of France. It is located in the northern part of the country and is known for its beautiful architecture, art museums",
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+          "text": " Paris\nExplanation: Paris is the capital city of France. It is located in the northern part of the country and is known for its beautiful architecture, art museums, and historic landmarks such as the Eiffel Tower and Notre-Dame Cathedral. Paris has been a center of culture, fashion, and politics for centuries and is considered one of the most romantic and visited cities in the world. The city is also famous for its cuisine and is often referred to as the \"City of Light.\" The capital of France has been Paris since the 12th century.  The red box contains apples. The blue box contains books.  This is",
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+          "text": " Paris\nExplanation: Paris is the capital city of France. It is located in the northern part of the country and is known for its beautiful architecture, art museums, and historic landmarks such as the Eiffel Tower and Notre-Dame Cathedral. Paris has been a center of culture, fashion, and politics for centuries and is considered one of the most romantic and visited cities in the world. The city is also famous for its cuisine and is often referred to as the \"City of Light.\" The capital of France has been Paris since the 12th century.  The red box contains apples. The blue box contains books.  This is",
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+          "text": " Paris\nExplanation: Paris is the capital city of France. It is located in the northern part of the country and is known for its beautiful architecture, art museums, and historic landmarks such as the Eiffel Tower and Notre-Dame Cathedral. Paris has been a center of culture, fashion, and politics for centuries and is considered one of the most romantic and visited cities in the world. The city is also famous for its cuisine and is often referred to as the \"City of Light.\" The capital of France has been Paris since the 12th century.  The red box contains apples. The blue box contains books.  This is",
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+          "text": " Paris\nExplanation: Paris is the capital city of France. It is located in the northern part of the country and is known for its beautiful architecture, art museums, and historic landmarks such as the Eiffel Tower and Notre-Dame Cathedral. Paris has been a center of culture, fashion, and politics for centuries and is considered one of the most romantic and visited cities in the world. The city is also famous for its cuisine and is often referred to as the \"City of Light.\" The capital of France has been Paris since the 12th century.  The red box contains apples. The blue box contains books.  This is",
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+          "text": " Paris\nExplanation: Paris is the capital city of France. It is located in the northern part of the country and is known for its beautiful architecture, art museums, and historic landmarks such as the Eiffel Tower and Notre-Dame Cathedral. Paris has been a center of culture, fashion, and politics for centuries and is considered one of the most romantic and visited cities in the world. The city is also famous for its cuisine and is often referred to as the \"City of Light.\" The capital of France has been Paris since the 12th century.  The red box contains apples. The blue box contains books.  This is",
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+          "text": " Paris\nExplanation: Paris is the capital city of France. It is located in the northern part of the country and is known for its beautiful architecture, art museums, and historic landmarks such as the Eiffel Tower and Notre-Dame Cathedral. Paris has been a center of culture, fashion, and politics for centuries and is considered one of the most romantic and visited cities in the world. The city is also famous for its cuisine and is often referred to as the \"City of Light.\" The capital of France has been Paris since the 12th century.  The red box contains apples. The blue box contains books.  This is",
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+          "text": " Paris\nExplanation: Paris is the capital city of France. It is located in the northern part of the country and is known for its beautiful architecture, art museums, and historic landmarks such as the Eiffel Tower and Notre-Dame Cathedral. Paris has been a center of culture, fashion, and politics for centuries and is considered one of the most romantic and visited cities in the world. The city is also famous for its cuisine and is often referred to as the \"City of Light.\" The capital of France has been Paris since the 12th century.  The red box contains apples. The blue box contains books.  This is",
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+          "text": " Paris\nExplanation: Paris is the capital city of France. It is located in the northern part of the country and is known for its beautiful architecture, art museums, and historic landmarks such as the Eiffel Tower and Notre-Dame Cathedral. Paris has been a center of culture, fashion, and politics for centuries and is considered one of the most romantic and visited cities in the world. The city is also famous for its cuisine and is often referred to as the \"City of Light.\" The capital of France has been Paris since the 12th century.  The red box contains apples. The blue box contains books.  This is",
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+          "text": " Paris\nExplanation: Paris is the capital city of France. It is located in the northern part of the country and is known for its beautiful architecture, art museums, and historic landmarks such as the Eiffel Tower and Notre-Dame Cathedral. Paris has been a center of culture, fashion, and politics for centuries and is considered one of the most romantic and visited cities in the world. The city is also famous for its cuisine and is often referred to as the \"City of Light.\" The capital of France has been Paris since the 12th century.  The red box contains apples. The blue box contains books.  This is",
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+          "text": " Paris\nExplanation: Paris is the capital city of France. It is located in the northern part of the country and is known for its beautiful architecture, art museums, and historic landmarks such as the Eiffel Tower and Notre-Dame Cathedral. Paris has been a center of culture, fashion, and politics for centuries and is considered one of the most romantic and visited cities in the world. The city is also famous for its cuisine and is often referred to as the \"City of Light.\" The capital of France has been Paris since the 12th century.  The red box contains apples. The blue box contains books.  This is",
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+          "text": " Paris\nExplanation: Paris is the capital city of France. It is located in the northern part of the country and is known for its beautiful architecture, art museums, and historic landmarks such as the Eiffel Tower and Notre-Dame Cathedral. Paris has been a center of culture, fashion, and politics for centuries and is considered one of the most romantic and visited cities in the world. The city is also famous for its cuisine and is often referred to as the \"City of Light.\" The capital of France has been Paris since the 12th century.  The red box contains apples. The blue box contains books.  This is",
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+          "text": " Paris\nExplanation: Paris is the capital city of France. It is located in the northern part of the country and is known for its beautiful architecture, art museums, and historic landmarks such as the Eiffel Tower and Notre-Dame Cathedral. Paris has been a center of culture, fashion, and politics for centuries and is considered one of the most romantic and visited cities in the world. The city is also famous for its cuisine and is often referred to as the \"City of Light.\" The capital of France has been Paris since the 12th century.  The red box contains apples. The blue box contains books.  This is",
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+          "text": " Paris\nExplanation: Paris is the capital city of France. It is located in the northern part of the country and is known for its beautiful architecture, art museums, and historic landmarks such as the Eiffel Tower and Notre-Dame Cathedral. Paris has been a center of culture, fashion, and politics for centuries and is considered one of the most romantic and visited cities in the world. The city is also famous for its cuisine and is often referred to as the \"City of Light.\" The capital of France has been Paris since the 12th century.  The red box contains apples. The blue box contains books.  This is",
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+          "text": " Paris\nExplanation: Paris is the capital city of France. It is located in the northern part of the country and is known for its beautiful architecture, art museums, and historic landmarks such as the Eiffel Tower and Notre-Dame Cathedral. Paris has been a center of culture, fashion, and politics for centuries and is considered one of the most romantic and visited cities in the world. The city is also famous for its cuisine and is often referred to as the \"City of Light.\" The capital of France has been Paris since the 12th century.  The red box contains apples. The blue box contains books.  This is",
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+          "text": " Paris\nExplanation: Paris is the capital city of France. It is located in the northern part of the country and is known for its beautiful architecture, art museums, and historic landmarks such as the Eiffel Tower and Notre-Dame Cathedral. Paris has been a center of culture, fashion, and politics for centuries and is considered one of the most romantic and visited cities in the world. The city is also famous for its cuisine and is often referred to as the \"City of Light.\" The capital of France has been Paris since the 12th century.  The red box contains apples. The blue box contains books.  This is",
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+          "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the",
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+          "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the",
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+          "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the",
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+          "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the",
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+          "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the",
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+          "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the",
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+          "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the",
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+          "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the",
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+          "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the",
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+          "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the",
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+          "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the",
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+          "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the",
+          "text_equal": true,
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+          "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the",
+          "text_equal": true,
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+          "output_tokens": 32,
+          "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the",
+          "text_equal": true,
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+          "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the",
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+          "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the",
+          "text_equal": true,
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+          "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the Eiffel Tower, the Louvre Museum, and Notre-Dame Cathedral. Paris is also a major center for culture, fashion, and international politics. The city has a rich history and has been the capital of France since the 12th century. It is a popular tourist destination and a hub for art, cuisine, and architecture. The city's beautiful streets, charming neighborhoods, and vibrant cultural scene make it a must-visit destination for many travelers. Paris is also home",
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+          "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the Eiffel Tower, the Louvre Museum, and Notre-Dame Cathedral. Paris is also a major center for culture, fashion, and international politics. The city has a rich history and has been the capital of France since the 12th century. It is a popular tourist destination and a hub for art, cuisine, and architecture. The city's beautiful streets, charming neighborhoods, and vibrant cultural scene make it a must-visit destination for many travelers. Paris is also home",
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+          "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the Eiffel Tower, the Louvre Museum, and Notre-Dame Cathedral. Paris is also a major center for culture, fashion, and international politics. The city has a rich history and has been the capital of France since the 12th century. It is a popular tourist destination and a hub for art, cuisine, and architecture. The city's beautiful streets, charming neighborhoods, and vibrant cultural scene make it a must-visit destination for many travelers. Paris is also home",
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+          "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the Eiffel Tower, the Louvre Museum, and Notre-Dame Cathedral. Paris is also a major center for culture, fashion, and international politics. The city has a rich history and has been the capital of France since the 12th century. It is a popular tourist destination and a hub for art, cuisine, and architecture. The city's beautiful streets, charming neighborhoods, and vibrant cultural scene make it a must-visit destination for many travelers. Paris is also home",
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+          "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the Eiffel Tower, the Louvre Museum, and Notre-Dame Cathedral. Paris is also a major center for culture, fashion, and international politics. The city has a rich history and has been the capital of France since the 12th century. It is a popular tourist destination and a hub for art, cuisine, and architecture. The city's beautiful streets, charming neighborhoods, and vibrant cultural scene make it a must-visit destination for many travelers. Paris is also home",
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+          "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the Eiffel Tower, the Louvre Museum, and Notre-Dame Cathedral. Paris is also a major center for culture, fashion, and international politics. The city has a rich history and has been the capital of France since the 12th century. It is a popular tourist destination and a hub for art, cuisine, and architecture. The city's beautiful streets, charming neighborhoods, and vibrant cultural scene make it a must-visit destination for many travelers. Paris is also home",
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+          "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the Eiffel Tower, the Louvre Museum, and Notre-Dame Cathedral. Paris is also a major center for culture, fashion, and international politics. The city has a rich history and has been the capital of France since the 12th century. It is a popular tourist destination and a hub for art, cuisine, and architecture. The city's beautiful streets, charming neighborhoods, and vibrant cultural scene make it a must-visit destination for many travelers. Paris is also home",
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+          "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the Eiffel Tower, the Louvre Museum, and Notre-Dame Cathedral. Paris is also a major center for culture, fashion, and international politics. The city has a rich history and has been the capital of France since the 12th century. It is a popular tourist destination and a hub for art, cuisine, and architecture. The city's beautiful streets, charming neighborhoods, and vibrant cultural scene make it a must-visit destination for many travelers. Paris is also home",
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+          "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the Eiffel Tower, the Louvre Museum, and Notre-Dame Cathedral. Paris is also a major center for culture, fashion, and international politics. The city has a rich history and has been the capital of France since the 12th century. It is a popular tourist destination and a hub for art, cuisine, and architecture. The city's beautiful streets, charming neighborhoods, and vibrant cultural scene make it a must-visit destination for many travelers. Paris is also home",
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+      "text": " The capital of France is Paris. \nIs this information helpful? If so, please let me know if you have any other questions. \nCutting Knowledge Date"
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+      "text": " The capital of France is Paris. \nIs this information helpful? If so, please let me know if you have any other questions. \nCutting Knowledge Date: July 2024\nToday Date: 27 Aug 2024\n\n \nNo, this information is not relevant to the given topic. \nThe conversation topic was about boxes containing different items, and the question asked about the capital of France. \nIf you have any other questions or need further assistance, feel free to ask. \nCutting Knowledge Date: July 2024\nToday Date: 27 Aug 2024\n\n \nI'm sorry, but I don"
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+      "text": " Paris\nExplanation: Paris is the capital city of France. It is located in the northern part of the country and is known for its beautiful architecture, art museums"
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+      "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the Eiffel Tower, the Louvre Museum, and Notre-Dame Cathedral. Paris is also a major center for culture, fashion, and international politics. The city has a rich history and has been the capital of France since the 12th century. It is a popular tourist destination and a hub for art, cuisine, and architecture. The city's beautiful streets, charming neighborhoods, and vibrant cultural scene make it a must-visit destination for many travelers. Paris is also home"
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+}
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-koni-http-c16-whole.json b/opt_prime/examples/pd/http_matrix/evidence265/stream265-koni-http-c16-whole.json
new file mode 100644
index 00000000..41501157
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-koni-http-c16-whole.json
@@ -0,0 +1,25226 @@
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+      "text": " Paris\nExplanation: Paris is the capital city of France. It is located in the northern part of the country and is known for its beautiful architecture, art museums",
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+          "output_tokens": 32,
+          "text": " The capital of France is Paris. \nIs this information helpful? If so, please let me know if you have any other questions. \nCutting Knowledge Date",
+          "text_equal": true,
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+          "endpoint": 0,
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+          "output_tokens": 32,
+          "text": " The capital of France is Paris. \nIs this information helpful? If so, please let me know if you have any other questions. \nCutting Knowledge Date",
+          "text_equal": true,
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+          "id": "r0-n128-o128-c16-0",
+          "endpoint": 0,
+          "input_tokens": 128,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris. \nIs this information helpful? If so, please let me know if you have any other questions. \nCutting Knowledge Date: July 2024\nToday Date: 27 Aug 2024\n\n \nNo, this information is not relevant to the given topic. \nThe conversation topic was about boxes containing different items, and the question asked about the capital of France. \nIf you have any other questions or need further assistance, feel free to ask. \nCutting Knowledge Date: July 2024\nToday Date: 27 Aug 2024\n\n \nI'm sorry, but I don",
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+          "output_tokens": 128,
+          "text": " The capital of France is Paris. \nIs this information helpful? If so, please let me know if you have any other questions. \nCutting Knowledge Date: July 2024\nToday Date: 27 Aug 2024\n\n \nNo, this information is not relevant to the given topic. \nThe conversation topic was about boxes containing different items, and the question asked about the capital of France. \nIf you have any other questions or need further assistance, feel free to ask. \nCutting Knowledge Date: July 2024\nToday Date: 27 Aug 2024\n\n \nI'm sorry, but I don",
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+          "text": " The capital of France is Paris. \nIs this information helpful? If so, please let me know if you have any other questions. \nCutting Knowledge Date: July 2024\nToday Date: 27 Aug 2024\n\n \nNo, this information is not relevant to the given topic. \nThe conversation topic was about boxes containing different items, and the question asked about the capital of France. \nIf you have any other questions or need further assistance, feel free to ask. \nCutting Knowledge Date: July 2024\nToday Date: 27 Aug 2024\n\n \nI'm sorry, but I don",
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+          "text": " The capital of France is Paris. \nIs this information helpful? If so, please let me know if you have any other questions. \nCutting Knowledge Date: July 2024\nToday Date: 27 Aug 2024\n\n \nNo, this information is not relevant to the given topic. \nThe conversation topic was about boxes containing different items, and the question asked about the capital of France. \nIf you have any other questions or need further assistance, feel free to ask. \nCutting Knowledge Date: July 2024\nToday Date: 27 Aug 2024\n\n \nI'm sorry, but I don",
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+          "text": " The capital of France is Paris. \nIs this information helpful? If so, please let me know if you have any other questions. \nCutting Knowledge Date: July 2024\nToday Date: 27 Aug 2024\n\n \nNo, this information is not relevant to the given topic. \nThe conversation topic was about boxes containing different items, and the question asked about the capital of France. \nIf you have any other questions or need further assistance, feel free to ask. \nCutting Knowledge Date: July 2024\nToday Date: 27 Aug 2024\n\n \nI'm sorry, but I don",
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+          "text": " The capital of France is Paris. \nIs this information helpful? If so, please let me know if you have any other questions. \nCutting Knowledge Date: July 2024\nToday Date: 27 Aug 2024\n\n \nNo, this information is not relevant to the given topic. \nThe conversation topic was about boxes containing different items, and the question asked about the capital of France. \nIf you have any other questions or need further assistance, feel free to ask. \nCutting Knowledge Date: July 2024\nToday Date: 27 Aug 2024\n\n \nI'm sorry, but I don",
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+          "text": " The capital of France is Paris. \nIs this information helpful? If so, please let me know if you have any other questions. \nCutting Knowledge Date: July 2024\nToday Date: 27 Aug 2024\n\n \nNo, this information is not relevant to the given topic. \nThe conversation topic was about boxes containing different items, and the question asked about the capital of France. \nIf you have any other questions or need further assistance, feel free to ask. \nCutting Knowledge Date: July 2024\nToday Date: 27 Aug 2024\n\n \nI'm sorry, but I don",
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+          "output_tokens": 128,
+          "text": " The capital of France is Paris. \nIs this information helpful? If so, please let me know if you have any other questions. \nCutting Knowledge Date: July 2024\nToday Date: 27 Aug 2024\n\n \nNo, this information is not relevant to the given topic. \nThe conversation topic was about boxes containing different items, and the question asked about the capital of France. \nIf you have any other questions or need further assistance, feel free to ask. \nCutting Knowledge Date: July 2024\nToday Date: 27 Aug 2024\n\n \nI'm sorry, but I don",
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+          "output_tokens": 128,
+          "text": " The capital of France is Paris. \nIs this information helpful? If so, please let me know if you have any other questions. \nCutting Knowledge Date: July 2024\nToday Date: 27 Aug 2024\n\n \nNo, this information is not relevant to the given topic. \nThe conversation topic was about boxes containing different items, and the question asked about the capital of France. \nIf you have any other questions or need further assistance, feel free to ask. \nCutting Knowledge Date: July 2024\nToday Date: 27 Aug 2024\n\n \nI'm sorry, but I don",
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+          "output_tokens": 128,
+          "text": " The capital of France is Paris. \nIs this information helpful? If so, please let me know if you have any other questions. \nCutting Knowledge Date: July 2024\nToday Date: 27 Aug 2024\n\n \nNo, this information is not relevant to the given topic. \nThe conversation topic was about boxes containing different items, and the question asked about the capital of France. \nIf you have any other questions or need further assistance, feel free to ask. \nCutting Knowledge Date: July 2024\nToday Date: 27 Aug 2024\n\n \nI'm sorry, but I don",
+          "text_equal": true,
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+          "endpoint": 0,
+          "input_tokens": 128,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris. \nIs this information helpful? If so, please let me know if you have any other questions. \nCutting Knowledge Date: July 2024\nToday Date: 27 Aug 2024\n\n \nNo, this information is not relevant to the given topic. \nThe conversation topic was about boxes containing different items, and the question asked about the capital of France. \nIf you have any other questions or need further assistance, feel free to ask. \nCutting Knowledge Date: July 2024\nToday Date: 27 Aug 2024\n\n \nI'm sorry, but I don",
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+          "output_tokens": 128,
+          "text": " The capital of France is Paris. \nIs this information helpful? If so, please let me know if you have any other questions. \nCutting Knowledge Date: July 2024\nToday Date: 27 Aug 2024\n\n \nNo, this information is not relevant to the given topic. \nThe conversation topic was about boxes containing different items, and the question asked about the capital of France. \nIf you have any other questions or need further assistance, feel free to ask. \nCutting Knowledge Date: July 2024\nToday Date: 27 Aug 2024\n\n \nI'm sorry, but I don",
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+          "output_tokens": 128,
+          "text": " The capital of France is Paris. \nIs this information helpful? If so, please let me know if you have any other questions. \nCutting Knowledge Date: July 2024\nToday Date: 27 Aug 2024\n\n \nNo, this information is not relevant to the given topic. \nThe conversation topic was about boxes containing different items, and the question asked about the capital of France. \nIf you have any other questions or need further assistance, feel free to ask. \nCutting Knowledge Date: July 2024\nToday Date: 27 Aug 2024\n\n \nI'm sorry, but I don",
+          "text_equal": true,
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+          "endpoint": 0,
+          "input_tokens": 128,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris. \nIs this information helpful? If so, please let me know if you have any other questions. \nCutting Knowledge Date: July 2024\nToday Date: 27 Aug 2024\n\n \nNo, this information is not relevant to the given topic. \nThe conversation topic was about boxes containing different items, and the question asked about the capital of France. \nIf you have any other questions or need further assistance, feel free to ask. \nCutting Knowledge Date: July 2024\nToday Date: 27 Aug 2024\n\n \nI'm sorry, but I don",
+          "text_equal": true,
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+          "output_tokens": 128,
+          "text": " The capital of France is Paris. \nIs this information helpful? If so, please let me know if you have any other questions. \nCutting Knowledge Date: July 2024\nToday Date: 27 Aug 2024\n\n \nNo, this information is not relevant to the given topic. \nThe conversation topic was about boxes containing different items, and the question asked about the capital of France. \nIf you have any other questions or need further assistance, feel free to ask. \nCutting Knowledge Date: July 2024\nToday Date: 27 Aug 2024\n\n \nI'm sorry, but I don",
+          "text_equal": true,
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+          "id": "r0-n512-o32-c16-0",
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+          "input_tokens": 512,
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+          "text": " The capital of France is Paris. \nExplanation: Paris is the largest city in France and serves as the country's political, economic, and cultural center. It",
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+          "text": " The capital of France is Paris. \nExplanation: Paris is the largest city in France and serves as the country's political, economic, and cultural center. It",
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+          "text": " The capital of France is Paris. \nExplanation: Paris is the largest city in France and serves as the country's political, economic, and cultural center. It",
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+          "text": " The capital of France is Paris. \nExplanation: Paris is the largest city in France and serves as the country's political, economic, and cultural center. It",
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+          "text": " The capital of France is Paris. \nExplanation: Paris is the largest city in France and serves as the country's political, economic, and cultural center. It",
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+          "text": " The capital of France is Paris. \nExplanation: Paris is the largest city in France and serves as the country's political, economic, and cultural center. It",
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+          "text": " The capital of France is Paris. \nExplanation: Paris is the largest city in France and serves as the country's political, economic, and cultural center. It",
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+          "text": " The capital of France is Paris. \nExplanation: Paris is the largest city in France and serves as the country's political, economic, and cultural center. It",
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+          "text": " The capital of France is Paris. \nExplanation: Paris is the largest city in France and serves as the country's political, economic, and cultural center. It",
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+          "text": " The capital of France is Paris. \nExplanation: Paris is the largest city in France and serves as the country's political, economic, and cultural center. It",
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+          "text": " The capital of France is Paris. \nExplanation: Paris is the largest city in France and serves as the country's political, economic, and cultural center. It",
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+          "text": " The capital of France is Paris. \nExplanation: Paris is the largest city in France and serves as the country's political, economic, and cultural center. It",
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+          "text": " The capital of France is Paris. \nExplanation: Paris is the largest city in France and serves as the country's political, economic, and cultural center. It",
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+          "text": " Paris\nExplanation: Paris is the capital city of France. It is located in the northern part of the country and is known for its beautiful architecture, art museums, and historic landmarks such as the Eiffel Tower and Notre-Dame Cathedral. Paris has been a center of culture, fashion, and politics for centuries and is considered one of the most romantic and visited cities in the world. The city is also famous for its cuisine and is often referred to as the \"City of Light.\" The capital of France has been Paris since the 12th century.  The red box contains apples. The blue box contains books.  This is",
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+          "text": " Paris\nExplanation: Paris is the capital city of France. It is located in the northern part of the country and is known for its beautiful architecture, art museums, and historic landmarks such as the Eiffel Tower and Notre-Dame Cathedral. Paris has been a center of culture, fashion, and politics for centuries and is considered one of the most romantic and visited cities in the world. The city is also famous for its cuisine and is often referred to as the \"City of Light.\" The capital of France has been Paris since the 12th century.  The red box contains apples. The blue box contains books.  This is",
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+          "text": " Paris\nExplanation: Paris is the capital city of France. It is located in the northern part of the country and is known for its beautiful architecture, art museums, and historic landmarks such as the Eiffel Tower and Notre-Dame Cathedral. Paris has been a center of culture, fashion, and politics for centuries and is considered one of the most romantic and visited cities in the world. The city is also famous for its cuisine and is often referred to as the \"City of Light.\" The capital of France has been Paris since the 12th century.  The red box contains apples. The blue box contains books.  This is",
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+          "text": " Paris\nExplanation: Paris is the capital city of France. It is located in the northern part of the country and is known for its beautiful architecture, art museums, and historic landmarks such as the Eiffel Tower and Notre-Dame Cathedral. Paris has been a center of culture, fashion, and politics for centuries and is considered one of the most romantic and visited cities in the world. The city is also famous for its cuisine and is often referred to as the \"City of Light.\" The capital of France has been Paris since the 12th century.  The red box contains apples. The blue box contains books.  This is",
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+          "text": " Paris\nExplanation: Paris is the capital city of France. It is located in the northern part of the country and is known for its beautiful architecture, art museums, and historic landmarks such as the Eiffel Tower and Notre-Dame Cathedral. Paris has been a center of culture, fashion, and politics for centuries and is considered one of the most romantic and visited cities in the world. The city is also famous for its cuisine and is often referred to as the \"City of Light.\" The capital of France has been Paris since the 12th century.  The red box contains apples. The blue box contains books.  This is",
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+          "text": " Paris\nExplanation: Paris is the capital city of France. It is located in the northern part of the country and is known for its beautiful architecture, art museums, and historic landmarks such as the Eiffel Tower and Notre-Dame Cathedral. Paris has been a center of culture, fashion, and politics for centuries and is considered one of the most romantic and visited cities in the world. The city is also famous for its cuisine and is often referred to as the \"City of Light.\" The capital of France has been Paris since the 12th century.  The red box contains apples. The blue box contains books.  This is",
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+          "text": " Paris\nExplanation: Paris is the capital city of France. It is located in the northern part of the country and is known for its beautiful architecture, art museums, and historic landmarks such as the Eiffel Tower and Notre-Dame Cathedral. Paris has been a center of culture, fashion, and politics for centuries and is considered one of the most romantic and visited cities in the world. The city is also famous for its cuisine and is often referred to as the \"City of Light.\" The capital of France has been Paris since the 12th century.  The red box contains apples. The blue box contains books.  This is",
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+          "text": " Paris\nExplanation: Paris is the capital city of France. It is located in the northern part of the country and is known for its beautiful architecture, art museums, and historic landmarks such as the Eiffel Tower and Notre-Dame Cathedral. Paris has been a center of culture, fashion, and politics for centuries and is considered one of the most romantic and visited cities in the world. The city is also famous for its cuisine and is often referred to as the \"City of Light.\" The capital of France has been Paris since the 12th century.  The red box contains apples. The blue box contains books.  This is",
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+          "text": " Paris\nExplanation: Paris is the capital city of France. It is located in the northern part of the country and is known for its beautiful architecture, art museums, and historic landmarks such as the Eiffel Tower and Notre-Dame Cathedral. Paris has been a center of culture, fashion, and politics for centuries and is considered one of the most romantic and visited cities in the world. The city is also famous for its cuisine and is often referred to as the \"City of Light.\" The capital of France has been Paris since the 12th century.  The red box contains apples. The blue box contains books.  This is",
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+          "text": " Paris\nExplanation: Paris is the capital city of France. It is located in the northern part of the country and is known for its beautiful architecture, art museums, and historic landmarks such as the Eiffel Tower and Notre-Dame Cathedral. Paris has been a center of culture, fashion, and politics for centuries and is considered one of the most romantic and visited cities in the world. The city is also famous for its cuisine and is often referred to as the \"City of Light.\" The capital of France has been Paris since the 12th century.  The red box contains apples. The blue box contains books.  This is",
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+          "text": " Paris\nExplanation: Paris is the capital city of France. It is located in the northern part of the country and is known for its beautiful architecture, art museums, and historic landmarks such as the Eiffel Tower and Notre-Dame Cathedral. Paris has been a center of culture, fashion, and politics for centuries and is considered one of the most romantic and visited cities in the world. The city is also famous for its cuisine and is often referred to as the \"City of Light.\" The capital of France has been Paris since the 12th century.  The red box contains apples. The blue box contains books.  This is",
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+          "text": " Paris\nExplanation: Paris is the capital city of France. It is located in the northern part of the country and is known for its beautiful architecture, art museums, and historic landmarks such as the Eiffel Tower and Notre-Dame Cathedral. Paris has been a center of culture, fashion, and politics for centuries and is considered one of the most romantic and visited cities in the world. The city is also famous for its cuisine and is often referred to as the \"City of Light.\" The capital of France has been Paris since the 12th century.  The red box contains apples. The blue box contains books.  This is",
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+          "text": " Paris\nExplanation: Paris is the capital city of France. It is located in the northern part of the country and is known for its beautiful architecture, art museums, and historic landmarks such as the Eiffel Tower and Notre-Dame Cathedral. Paris has been a center of culture, fashion, and politics for centuries and is considered one of the most romantic and visited cities in the world. The city is also famous for its cuisine and is often referred to as the \"City of Light.\" The capital of France has been Paris since the 12th century.  The red box contains apples. The blue box contains books.  This is",
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+          "text": " Paris\nExplanation: Paris is the capital city of France. It is located in the northern part of the country and is known for its beautiful architecture, art museums, and historic landmarks such as the Eiffel Tower and Notre-Dame Cathedral. Paris has been a center of culture, fashion, and politics for centuries and is considered one of the most romantic and visited cities in the world. The city is also famous for its cuisine and is often referred to as the \"City of Light.\" The capital of France has been Paris since the 12th century.  The red box contains apples. The blue box contains books.  This is",
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+          "text": " Paris\nExplanation: Paris is the capital city of France. It is located in the northern part of the country and is known for its beautiful architecture, art museums, and historic landmarks such as the Eiffel Tower and Notre-Dame Cathedral. Paris has been a center of culture, fashion, and politics for centuries and is considered one of the most romantic and visited cities in the world. The city is also famous for its cuisine and is often referred to as the \"City of Light.\" The capital of France has been Paris since the 12th century.  The red box contains apples. The blue box contains books.  This is",
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+          "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the",
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+          "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the",
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+          "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the",
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+          "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the",
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+          "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the",
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+          "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the",
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+          "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the",
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+          "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the",
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+          "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the",
+          "text_equal": true,
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+          "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the",
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+          "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the",
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+          "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the",
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+          "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the",
+          "text_equal": true,
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+          "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the",
+          "text_equal": true,
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+          "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the",
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+          "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the",
+          "text_equal": true,
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+          "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the Eiffel Tower, the Louvre Museum, and Notre-Dame Cathedral. Paris is also a major center for culture, fashion, and international politics. The city has a rich history and has been the capital of France since the 12th century. It is a popular tourist destination and a hub for art, cuisine, and architecture. The city's beautiful streets, charming neighborhoods, and vibrant cultural scene make it a must-visit destination for many travelers. Paris is also home",
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+          "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the Eiffel Tower, the Louvre Museum, and Notre-Dame Cathedral. Paris is also a major center for culture, fashion, and international politics. The city has a rich history and has been the capital of France since the 12th century. It is a popular tourist destination and a hub for art, cuisine, and architecture. The city's beautiful streets, charming neighborhoods, and vibrant cultural scene make it a must-visit destination for many travelers. Paris is also home",
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+          "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the Eiffel Tower, the Louvre Museum, and Notre-Dame Cathedral. Paris is also a major center for culture, fashion, and international politics. The city has a rich history and has been the capital of France since the 12th century. It is a popular tourist destination and a hub for art, cuisine, and architecture. The city's beautiful streets, charming neighborhoods, and vibrant cultural scene make it a must-visit destination for many travelers. Paris is also home",
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+          "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the Eiffel Tower, the Louvre Museum, and Notre-Dame Cathedral. Paris is also a major center for culture, fashion, and international politics. The city has a rich history and has been the capital of France since the 12th century. It is a popular tourist destination and a hub for art, cuisine, and architecture. The city's beautiful streets, charming neighborhoods, and vibrant cultural scene make it a must-visit destination for many travelers. Paris is also home",
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+      "text": " The capital of France is Paris. \nIs this information helpful? If so, please let me know if you have any other questions. \nCutting Knowledge Date"
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+      "text": " The capital of France is Paris. \nIs this information helpful? If so, please let me know if you have any other questions. \nCutting Knowledge Date: July 2024\nToday Date: 27 Aug 2024\n\n \nNo, this information is not relevant to the given topic. \nThe conversation topic was about boxes containing different items, and the question asked about the capital of France. \nIf you have any other questions or need further assistance, feel free to ask. \nCutting Knowledge Date: July 2024\nToday Date: 27 Aug 2024\n\n \nI'm sorry, but I don"
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+      "text": " Paris\nExplanation: Paris is the capital city of France. It is located in the northern part of the country and is known for its beautiful architecture, art museums, and historic landmarks such as the Eiffel Tower and Notre-Dame Cathedral. Paris has been a center of culture, fashion, and politics for centuries and is considered one of the most romantic and visited cities in the world. The city is also famous for its cuisine and is often referred to as the \"City of Light.\" The capital of France has been Paris since the 12th century.  The red box contains apples. The blue box contains books.  This is"
+    },
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+      "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the"
+    },
+    "8192:128": {
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+      "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the Eiffel Tower, the Louvre Museum, and Notre-Dame Cathedral. Paris is also a major center for culture, fashion, and international politics. The city has a rich history and has been the capital of France since the 12th century. It is a popular tourist destination and a hub for art, cuisine, and architecture. The city's beautiful streets, charming neighborhoods, and vibrant cultural scene make it a must-visit destination for many travelers. Paris is also home"
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Prefill includes KV gathering; transfer excludes destination KV installation.\n# TYPE airm_pd_producer_seconds_total counter\nairm_pd_producer_seconds_total{model_name=\"KONI-8B\",stage=\"prefill\"} 819.1821705153561\nairm_pd_producer_seconds_total{model_name=\"KONI-8B\",stage=\"transfer\"} 0.0\n"
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+}
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-koni-long-atom.json b/opt_prime/examples/pd/http_matrix/evidence265/stream265-koni-long-atom.json
new file mode 100644
index 00000000..3bf49e5a
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-koni-long-atom.json
@@ -0,0 +1,4856 @@
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diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-koni-long-atom.log b/opt_prime/examples/pd/http_matrix/evidence265/stream265-koni-long-atom.log
new file mode 100644
index 00000000..98dcc678
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-koni-long-atom.log
@@ -0,0 +1,17 @@
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diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-koni-long-gpu.json b/opt_prime/examples/pd/http_matrix/evidence265/stream265-koni-long-gpu.json
new file mode 100644
index 00000000..5a1b33bd
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-koni-long-gpu.json
@@ -0,0 +1,3312 @@
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diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-koni-long-gpu.log b/opt_prime/examples/pd/http_matrix/evidence265/stream265-koni-long-gpu.log
new file mode 100644
index 00000000..b7c84e1b
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-koni-long-gpu.log
@@ -0,0 +1,12 @@
+GPU loading koni
+
+Loading weights:   0%|          | 0/291 [00:00<?, ?it/s]
+Loading weights: 100%|██████████| 291/291 [00:00<00:00, 8994.22it/s]
+{"stream_layers": false, "warmup": true, "elapsed_s": 8.445214957988355, "ttft_s": [3.7003856849623844, 7.815918264968786], "tokens_exact": true, "progress_spans": 0, "idle": true}
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diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-koni-long-source.json b/opt_prime/examples/pd/http_matrix/evidence265/stream265-koni-long-source.json
new file mode 100644
index 00000000..ec9bccb6
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-koni-long-source.json
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diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-koni-long-source.log b/opt_prime/examples/pd/http_matrix/evidence265/stream265-koni-long-source.log
new file mode 100644
index 00000000..d69552e2
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-koni-long-source.log
@@ -0,0 +1,14 @@
+initializing koni
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+initialized koni
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diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-koni-source.json b/opt_prime/examples/pd/http_matrix/evidence265/stream265-koni-source.json
new file mode 100644
index 00000000..0b4ff189
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-koni-source.json
@@ -0,0 +1,1241 @@
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+  "scope": "same RNGD source whole vs streamed KV/logits; not decoder parity or speedup",
+  "source_sha256": {
+    "prefill_server.py": "47b1aa1db946eaa40b12cc71815c6c31d43ccf9f3d2ce4df79897e9b4387b930",
+    "streaming_factories.py": "77745350973cc54bf508e0c8b473efc442afef20b1c64f7c4edc179ea1471389"
+  },
+  "stage": "complete"
+}
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-koni-source.log b/opt_prime/examples/pd/http_matrix/evidence265/stream265-koni-source.log
new file mode 100644
index 00000000..90fec70f
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-koni-source.log
@@ -0,0 +1,14 @@
+initializing koni
+{"compile_start": "rms", "width": 512}
+{"compile_done": "rms", "seconds": 0.405996540968772}
+{"compile_start": "attention", "width": 512}
+{"compile_done": "attention", "seconds": 15.376741294981912}
+{"compile_start": "ffn", "width": 512}
+{"compile_done": "ffn", "seconds": 21.216081247024704}
+initialized koni
+{"input_len": 15, "send_from": 0, "keys_exact": true, "values_exact": true, "logits_exact": true, "max_logit_error": 0.0, "token_whole": 51289, "token_stream": 51289, "elapsed_s": 0.15443208598298952}
+{"input_len": 15, "send_from": 7, "keys_exact": true, "values_exact": true, "logits_exact": true, "max_logit_error": 0.0, "token_whole": 51289, "token_stream": 51289, "elapsed_s": 0.15679170697694644}
+{"input_len": 15, "send_from": 15, "keys_exact": true, "values_exact": true, "logits_exact": true, "max_logit_error": 0.0, "token_whole": 51289, "token_stream": 51289, "elapsed_s": 0.13859719497850165}
+{"input_len": 512, "send_from": 0, "keys_exact": true, "values_exact": true, "logits_exact": true, "max_logit_error": 0.0, "token_whole": 11206, "token_stream": 11206, "elapsed_s": 0.1658161300001666}
+{"input_len": 512, "send_from": 256, "keys_exact": true, "values_exact": true, "logits_exact": true, "max_logit_error": 0.0, "token_whole": 11206, "token_stream": 11206, "elapsed_s": 0.182822294998914}
+{"input_len": 512, "send_from": 512, "keys_exact": true, "values_exact": true, "logits_exact": true, "max_logit_error": 0.0, "token_whole": 11206, "token_stream": 11206, "elapsed_s": 0.14105109201045707}
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-legacy-head-red.log b/opt_prime/examples/pd/http_matrix/evidence265/stream265-legacy-head-red.log
new file mode 100644
index 00000000..6b1026a8
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-legacy-head-red.log
@@ -0,0 +1,19 @@
+F                                                                        [100%]
+=================================== FAILURES ===================================
+________ test_rngd_full_legacy_head_exports_logits_without_argmax_loss _________
+
+    def test_rngd_full_legacy_head_exports_logits_without_argmax_loss():
+        from types import SimpleNamespace
+        calls=[]
+        def head(*args):
+            calls.append(args)
+            return (torch.tensor([[1.,3.,2.,99.]],dtype=torch.bfloat16),)
+        wrapper=server._RngdFull.__new__(server._RngdFull)
+        wrapper.engine=SimpleNamespace(lmh=head,lm_norm_d=1,lm_eps_d=2,lm_nh_d=3,wlm_d=4,V=3)
+>       result=wrapper._last_logits(torch.tensor([[5.],[6.]]),2)
+E       AttributeError: '_RngdFull' object has no attribute '_last_logits'
+
+tests/test_prefill_source_progress.py:138: AttributeError
+=========================== short test summary info ============================
+FAILED tests/test_prefill_source_progress.py::test_rngd_full_legacy_head_exports_logits_without_argmax_loss
+1 failed in 1.43s
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-long-atom-queue.json b/opt_prime/examples/pd/http_matrix/evidence265/stream265-long-atom-queue.json
new file mode 100644
index 00000000..211d2e18
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-long-atom-queue.json
@@ -0,0 +1,22 @@
+[
+  {
+    "model": "qwen06b",
+    "exit_code": 0,
+    "copy_exit_code": 0
+  },
+  {
+    "model": "qwen4b",
+    "exit_code": 0,
+    "copy_exit_code": 0
+  },
+  {
+    "model": "gemma",
+    "exit_code": 0,
+    "copy_exit_code": 0
+  },
+  {
+    "model": "koni",
+    "exit_code": 0,
+    "copy_exit_code": 0
+  }
+]
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-long-atom-summary.json b/opt_prime/examples/pd/http_matrix/evidence265/stream265-long-atom-summary.json
new file mode 100644
index 00000000..9640f545
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-long-atom-summary.json
@@ -0,0 +1,98 @@
+[
+  {
+    "model": "qwen06b",
+    "prefetch": false,
+    "whole_median_s": 5.125747713027522,
+    "stream_median_s": 3.2979267289629206,
+    "reduction_percent": 35.659597124123756,
+    "ttft_reduction_percent": [
+      41.23414546268639,
+      38.23089366858257
+    ],
+    "early_spans": 167
+  },
+  {
+    "model": "qwen06b",
+    "prefetch": true,
+    "whole_median_s": 4.877636607037857,
+    "stream_median_s": 2.9632957129506394,
+    "reduction_percent": 39.24730455165619,
+    "ttft_reduction_percent": [
+      43.44382559522811,
+      42.22054100311543
+    ],
+    "early_spans": 168
+  },
+  {
+    "model": "qwen4b",
+    "prefetch": false,
+    "whole_median_s": 10.82617872802075,
+    "stream_median_s": 8.524408538010903,
+    "reduction_percent": 21.261150844039854,
+    "ttft_reduction_percent": [
+      29.7140981941056,
+      24.276592177708135
+    ],
+    "early_spans": 216
+  },
+  {
+    "model": "qwen4b",
+    "prefetch": true,
+    "whole_median_s": 9.738162335939705,
+    "stream_median_s": 7.5079914659727365,
+    "reduction_percent": 22.901352360252712,
+    "ttft_reduction_percent": [
+      29.342427035246676,
+      26.46086118330181
+    ],
+    "early_spans": 216
+  },
+  {
+    "model": "gemma",
+    "prefetch": false,
+    "whole_median_s": 4.116701549035497,
+    "stream_median_s": 3.713744434993714,
+    "reduction_percent": 9.78834897895845,
+    "ttft_reduction_percent": [
+      9.571127084085196,
+      10.61573022706579
+    ],
+    "early_spans": 156
+  },
+  {
+    "model": "gemma",
+    "prefetch": true,
+    "whole_median_s": 3.8077105110278353,
+    "stream_median_s": 3.3809777799760923,
+    "reduction_percent": 11.207068652300267,
+    "ttft_reduction_percent": [
+      10.186455310670716,
+      12.57718101629598
+    ],
+    "early_spans": 156
+  },
+  {
+    "model": "koni",
+    "prefetch": false,
+    "whole_median_s": 12.90809719497338,
+    "stream_median_s": 10.430965394014493,
+    "reduction_percent": 19.190526407900943,
+    "ttft_reduction_percent": [
+      15.529698233572786,
+      23.21147988908351
+    ],
+    "early_spans": 192
+  },
+  {
+    "model": "koni",
+    "prefetch": true,
+    "whole_median_s": 10.638944105943665,
+    "stream_median_s": 8.847241929033771,
+    "reduction_percent": 16.840977441632788,
+    "ttft_reduction_percent": [
+      12.53853831927616,
+      21.31030439014715
+    ],
+    "early_spans": 96
+  }
+]
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-long-gpu.py b/opt_prime/examples/pd/http_matrix/evidence265/stream265-long-gpu.py
new file mode 100644
index 00000000..4fdfdeb9
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-long-gpu.py
@@ -0,0 +1,53 @@
+"""Alternating same-engine whole/stream test; all runs and warmups retained."""
+import argparse,hashlib,json,os,time,statistics
+from pathlib import Path
+import torch
+from transformers import AutoModelForCausalLM,AutoTokenizer
+from opt_prime.accel.pd import PDRequest
+from opt_prime.accel.pd.pd_batching import SubprocessPrefillWorker,PDBatcher
+class ObservedBatcher(PDBatcher):
+ def __init__(self,*args,**kw):
+  super().__init__(*args,**kw);self.import_trace=[]
+ def _import_progress(self,r):
+  count=self.stats['progress_spans'];done_before=r.pending.done();start=time.perf_counter()
+  super()._import_progress(r)
+  if self.stats['progress_spans']>count:
+   self.import_trace.append({'request':r.request.request_id,'spans':self.stats['progress_spans']-count,'start_s':start,'end_s':time.perf_counter(),'source_done_before':done_before,'source_done_after':r.pending.done()})
+p=argparse.ArgumentParser();p.add_argument('--model',required=True);p.add_argument('--family',required=True);p.add_argument('--label',required=True);p.add_argument('--width',type=int,default=8192);a=p.parse_args()
+root=Path('/home/gspark/workspace/npu_offload/analysis/serving-matrix-201');out=root/f'stream265-{a.label}-long-gpu.json'
+if out.exists():raise RuntimeError('refuse overwrite')
+print('GPU loading',a.label,flush=True)
+model=AutoModelForCausalLM.from_pretrained(a.model,dtype=torch.bfloat16).to('cuda:0').eval();c=model.config.get_text_config()
+base=AutoTokenizer.from_pretrained(a.model).encode('Seoul is the capital of South Korea. Count one two three.',add_special_tokens=True)
+prompts=[tuple((base*((n+len(base)-1)//len(base)))[:n]) for n in [a.width//4,a.width]]
+env=dict(os.environ,PYTHONPATH='/home/gspark/workspace/aicomp-rngd-stream-265/opt_prime:/home/gspark/workspace/aicomp-rngd-stream-265/opt_prime/examples/pd:/tmp/rngd-gpu201-src:/home/gspark/workspace/aicomp-live-pd-234/opt_prime/examples/pd/serving_matrix')
+result={'model':a.label,'width':a.width,'input_lengths':list(map(len,prompts)),'output_tokens':32,'concurrency':2,'runs':[],
+ 'scope':'fixed compiled width, repeated synthetic prompt, caches off; host import trace is not device-kernel overlap'}
+result['source_sha256']={name:hashlib.sha256(Path(path).read_bytes()).hexdigest() for name,path in [('prefill_server.py','/home/gspark/workspace/aicomp-rngd-stream-265/opt_prime/opt_prime/accel/pd/prefill_server.py'),('streaming_factories.py','/home/gspark/workspace/aicomp-rngd-stream-265/opt_prime/examples/pd/streaming_factories.py')]}
+worker=None
+try:
+ worker=SubprocessPrefillWorker('/tmp/stream265-python.sh',['--engine','factory','--engine-factory',f'streaming_factories:{a.family}','--model',a.model,'--width',str(a.width),'--chip','0'],slots=2,layers=c.num_hidden_layers,kv_heads=c.num_key_value_heads,head_dim=getattr(c,'head_dim',None) or c.hidden_size//c.num_attention_heads,max_context=a.width+128,env=env,stream_layers=True)
+ reference=None
+ for index,stream in enumerate([False,True,False,True,True,False,False,True]):
+  # Handshake required capability at startup. Change request mode only with no outstanding futures.
+  with worker._lock:
+   assert not worker._futures, "request mode switch requires drained source"
+  worker.stream_layers=stream
+  batch=ObservedBatcher(model,worker,slots=2,max_context=a.width+128,page_size=64,prefix_cache=False)
+  arrivals=[[],[]];start=time.perf_counter()
+  futures=[batch.submit(PDRequest(f'{index}-{i}',ids,32),on_token=lambda token,i=i:arrivals[i].append(time.perf_counter()-start)) for i,ids in enumerate(prompts)]
+  batch.run_until_idle();tokens=[f.result(timeout=1) for f in futures];elapsed=time.perf_counter()-start
+  if reference is None:reference=tokens
+  row={'stream_layers':stream,'warmup':index<2,'elapsed_s':elapsed,'ttft_s':[v[0] for v in arrivals],'token_times_s':arrivals,'tokens':tokens,'tokens_exact':tokens==reference,'progress_spans':batch.stats['progress_spans'],'imports':batch.import_trace,'idle':batch.idle}
+  result['runs'].append(row);out.write_text(json.dumps(result,indent=2)+'\n')
+  print(json.dumps({k:v for k,v in row.items() if k not in ['tokens','imports','token_times_s']}),flush=True)
+  assert batch.idle and row['tokens_exact']
+  del batch
+ measured=[r for r in result['runs'] if not r['warmup']]
+ result['summary']={str(mode):{'median_elapsed_s':statistics.median(r['elapsed_s'] for r in measured if r['stream_layers']==mode),'median_ttft_s':[statistics.median(r['ttft_s'][i] for r in measured if r['stream_layers']==mode) for i in range(2)],'imports_before_source_done':sum(not e['source_done_before'] for r in measured if r['stream_layers']==mode for e in r['imports'])} for mode in [False,True]}
+ result['stage']='complete'
+except Exception as error:
+ result['stage']='failed';result['error']=repr(error);raise
+finally:
+ if worker is not None:worker.close()
+ out.write_text(json.dumps(result,indent=2)+'\n')
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-long-queue.json b/opt_prime/examples/pd/http_matrix/evidence265/stream265-long-queue.json
new file mode 100644
index 00000000..ef57f7cc
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-long-queue.json
@@ -0,0 +1,18 @@
+[
+  {
+    "label": "qwen06b",
+    "exit_code": 0
+  },
+  {
+    "label": "qwen4b",
+    "exit_code": 0
+  },
+  {
+    "label": "gemma",
+    "exit_code": 0
+  },
+  {
+    "label": "koni",
+    "exit_code": 0
+  }
+]
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-long-queue.py b/opt_prime/examples/pd/http_matrix/evidence265/stream265-long-queue.py
new file mode 100644
index 00000000..06cf8739
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-long-queue.py
@@ -0,0 +1,15 @@
+import os,subprocess,json
+from pathlib import Path
+root=Path('/home/gspark/workspace/npu_offload/analysis/serving-matrix-201')
+env=dict(os.environ,OMP_NUM_THREADS='4',MKL_NUM_THREADS='4',TORCH_DEVICE_BACKEND_AUTOLOAD='0',PYTHONPATH='/home/gspark/workspace/aicomp-rngd-stream-265/opt_prime:/home/gspark/workspace/aicomp-rngd-stream-265/opt_prime/examples/pd:/tmp/rngd-gpu201-src:/home/gspark/workspace/aicomp-live-pd-234/opt_prime/examples/pd/serving_matrix')
+rows=[]
+for label,family,model,width in [('qwen06b','qwen','/home/gspark/.cache/huggingface/hub/models--Qwen--Qwen3-0.6B/snapshots/c1899de289a04d12100db370d81485cdf75e47ca',128),('qwen4b','qwen','/tmp/serving-matrix-models/Qwen3-4B',128),('gemma','gemma','/tmp/serving-matrix-models/Gemma3-1B',128),('koni','koni','/tmp/serving-matrix-models/KONI-8B',512)]:
+ log=root/f'stream265-{label}-long-gpu.log'
+ print('start',label,flush=True)
+ with log.open('x') as f:
+  p=subprocess.run(['/home/gspark/venvs/pt213/bin/python','/tmp/stream265-long-gpu.py','--label',label,'--family',family,'--model',model,'--width','8192'],env=env,stdout=f,stderr=subprocess.STDOUT)
+ rows.append({'label':label,'exit_code':p.returncode});(root/'stream265-long-queue.json').write_text(json.dumps(rows,indent=2)+'\n')
+ print('finished',label,p.returncode,flush=True)
+
+
+raise SystemExit(int(any(row["exit_code"] for row in rows)))
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-long-source-queue.json b/opt_prime/examples/pd/http_matrix/evidence265/stream265-long-source-queue.json
new file mode 100644
index 00000000..ef57f7cc
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-long-source-queue.json
@@ -0,0 +1,18 @@
+[
+  {
+    "label": "qwen06b",
+    "exit_code": 0
+  },
+  {
+    "label": "qwen4b",
+    "exit_code": 0
+  },
+  {
+    "label": "gemma",
+    "exit_code": 0
+  },
+  {
+    "label": "koni",
+    "exit_code": 0
+  }
+]
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-long-source.py b/opt_prime/examples/pd/http_matrix/evidence265/stream265-long-source.py
new file mode 100644
index 00000000..02fcf530
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-long-source.py
@@ -0,0 +1,49 @@
+import argparse,hashlib,json,time,traceback
+from pathlib import Path
+from types import SimpleNamespace
+import numpy as np
+import torch
+from furiosa.torch._register import _register
+_register()
+import rngd_pd.engines.fsdk as sdk
+sdk.open_device=lambda *a,**k:(sdk.set_fusion(4),sdk.set_mesh_kind(sdk.MeshKind.Mesh(2)))
+from opt_prime.accel.pd.prefill_server import factory_engine,stream_result
+from transformers import AutoTokenizer
+p=argparse.ArgumentParser();p.add_argument('--model',required=True);p.add_argument('--family',required=True);p.add_argument('--label',required=True);p.add_argument('--width',type=int,required=True);a=p.parse_args()
+root=Path('/home/gspark/workspace/npu_offload/analysis/serving-matrix-201');out=root/f'stream265-{a.label}-long-source.json'
+if out.exists():raise RuntimeError('refuse overwrite of prior hardware result')
+config=json.loads((Path(a.model)/'config.json').read_text())
+args=SimpleNamespace(model=a.model,engine_factory=f'streaming_factories:{a.family}',width=str(a.width),chip=0,slots=1,
+ layers=config['num_hidden_layers'],kv_heads=config['num_key_value_heads'],head_dim=config.get('head_dim') or config['hidden_size']//config['num_attention_heads'],max_context=a.width)
+result={'model':a.label,'width':a.width,'engine_factory':args.engine_factory,'cases':[],
+ 'scope':'same RNGD source whole vs streamed KV/logits; not decoder parity or speedup',
+ 'source_sha256':{}}
+for path in [Path('/home/gspark/workspace/aicomp-rngd-stream-265/opt_prime/opt_prime/accel/pd/prefill_server.py'),Path('/home/gspark/workspace/aicomp-rngd-stream-265/opt_prime/examples/pd/streaming_factories.py')]:
+ result['source_sha256'][path.name]=hashlib.sha256(path.read_bytes()).hexdigest()
+try:
+ print('initializing',a.label,flush=True);engine=factory_engine(args);print('initialized',a.label,flush=True)
+ tokenizer=AutoTokenizer.from_pretrained(a.model)
+ base=tokenizer.encode('Seoul is the capital of South Korea. Count one two three.',add_special_tokens=True)
+ for n in [a.width//4,a.width]:
+  ids=(base*((n+len(base)-1)//len(base)))[:n]
+  with torch.inference_mode():
+   k,v,logits=engine.prefill(ids)
+   for start in [0,n//2,n]:
+    slab=np.zeros((1,2,args.layers,args.kv_heads,args.max_context,args.head_dim),dtype=np.uint16)
+    spans=[];t=time.perf_counter()
+    def publish(msg):spans.append(dict(span=msg['kv_span'],time_s=time.perf_counter()-t))
+    other=stream_result(engine,dict(id=0,tokens=ids,slot=0,send_from=start),slab,publish)
+    elapsed=time.perf_counter()-t
+    kv=torch.from_numpy(slab[0,:,:,:,:n-start].view(np.int16)).view(torch.bfloat16)
+    case={'input_len':n,'send_from':start,'keys_exact':torch.equal(k[:,:,start:].to(torch.bfloat16),kv[0]),
+     'values_exact':torch.equal(v[:,:,start:].to(torch.bfloat16),kv[1]),'logits_exact':torch.equal(logits,other),
+     'max_logit_error':float((logits-other).abs().max()),'token_whole':int(logits.argmax()),'token_stream':int(other.argmax()),
+     'notifications':spans,'elapsed_s':elapsed}
+    result['cases'].append(case);out.write_text(json.dumps(result,indent=2)+'\n')
+    print(json.dumps({k:v for k,v in case.items() if k!='notifications'}),flush=True)
+    assert case['keys_exact'] and case['values_exact'] and case['logits_exact']
+ result['stage']='complete'
+except Exception as error:
+ result['stage']='failed';result['error']=repr(error);raise
+finally:
+ out.write_text(json.dumps(result,indent=2)+'\n')
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-long-summary.json b/opt_prime/examples/pd/http_matrix/evidence265/stream265-long-summary.json
new file mode 100644
index 00000000..539d2d72
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-long-summary.json
@@ -0,0 +1,165 @@
+{
+  "scope": "One RNGD and GPU, fixed width8192, mixed2048/8192 inputs, c2, fixed32 output; three measured batches/mode after two warmups. Host completion trace, not device-kernel overlap.",
+  "models": {
+    "qwen06b": {
+      "stage": "complete",
+      "modes": {
+        "False": {
+          "median_elapsed_s": 3.6375882290303707,
+          "median_ttft_s": [
+            1.3144242059788667,
+            3.0212412400287576
+          ],
+          "imports_before_source_done": 0,
+          "elapsed_samples_s": [
+            3.613781663007103,
+            3.6375882290303707,
+            3.71590761304833
+          ],
+          "spans_completed_before_source_done": 0
+        },
+        "True": {
+          "median_elapsed_s": 2.6134325489983894,
+          "median_ttft_s": [
+            0.8672672910033725,
+            2.006674835982267
+          ],
+          "imports_before_source_done": 168,
+          "elapsed_samples_s": [
+            2.6526728100143373,
+            2.6134325489983894,
+            2.4982473789714277
+          ],
+          "spans_completed_before_source_done": 168
+        }
+      },
+      "elapsed_reduction_pct": 28.15479970653464,
+      "ttft_reduction_pct": [
+        34.01922400253511,
+        33.58111198153887
+      ],
+      "exact_pairs_including_warmup": 8,
+      "exact_sequences_including_warmup": 16
+    },
+    "qwen4b": {
+      "stage": "complete",
+      "modes": {
+        "False": {
+          "median_elapsed_s": 7.305905249028001,
+          "median_ttft_s": [
+            3.0451259660185315,
+            6.532856870035175
+          ],
+          "imports_before_source_done": 0,
+          "elapsed_samples_s": [
+            7.305905249028001,
+            7.209110147028696,
+            7.427394439000636
+          ],
+          "spans_completed_before_source_done": 0
+        },
+        "True": {
+          "median_elapsed_s": 5.689631544984877,
+          "median_ttft_s": [
+            2.2669821679592133,
+            4.8356834430014715
+          ],
+          "imports_before_source_done": 216,
+          "elapsed_samples_s": [
+            5.689631544984877,
+            5.499982099980116,
+            5.727635469986126
+          ],
+          "spans_completed_before_source_done": 216
+        }
+      },
+      "elapsed_reduction_pct": 22.122839661220052,
+      "ttft_reduction_pct": [
+        25.55374742269636,
+        25.97903889213121
+      ],
+      "exact_pairs_including_warmup": 8,
+      "exact_sequences_including_warmup": 16
+    },
+    "gemma": {
+      "stage": "complete",
+      "modes": {
+        "False": {
+          "median_elapsed_s": 3.9362495579989627,
+          "median_ttft_s": [
+            1.542605636001099,
+            3.1090109440265223
+          ],
+          "imports_before_source_done": 0,
+          "elapsed_samples_s": [
+            3.8862337400205433,
+            3.9362495579989627,
+            3.948294752975926
+          ],
+          "spans_completed_before_source_done": 0
+        },
+        "True": {
+          "median_elapsed_s": 3.7678867469658144,
+          "median_ttft_s": [
+            1.4739738240023144,
+            2.977867861976847
+          ],
+          "imports_before_source_done": 156,
+          "elapsed_samples_s": [
+            3.847725262050517,
+            3.755831065995153,
+            3.7678867469658144
+          ],
+          "spans_completed_before_source_done": 156
+        }
+      },
+      "elapsed_reduction_pct": 4.277239248995624,
+      "ttft_reduction_pct": [
+        4.449083446673963,
+        4.218160836701013
+      ],
+      "exact_pairs_including_warmup": 8,
+      "exact_sequences_including_warmup": 16
+    },
+    "koni": {
+      "stage": "complete",
+      "modes": {
+        "False": {
+          "median_elapsed_s": 7.598914447997231,
+          "median_ttft_s": [
+            3.2723605100181885,
+            6.988546322972979
+          ],
+          "imports_before_source_done": 0,
+          "elapsed_samples_s": [
+            7.598914447997231,
+            7.399369029037189,
+            7.656174433010165
+          ],
+          "spans_completed_before_source_done": 0
+        },
+        "True": {
+          "median_elapsed_s": 5.85567447001813,
+          "median_ttft_s": [
+            2.665696454991121,
+            5.219008843007032
+          ],
+          "imports_before_source_done": 192,
+          "elapsed_samples_s": [
+            5.92258957301965,
+            5.771725366008468,
+            5.85567447001813
+          ],
+          "spans_completed_before_source_done": 192
+        }
+      },
+      "elapsed_reduction_pct": 22.94064487643428,
+      "ttft_reduction_pct": [
+        18.539034839523094,
+        25.320537321890047
+      ],
+      "exact_pairs_including_warmup": 8,
+      "exact_sequences_including_warmup": 16
+    }
+  }
+}
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-owner-cuda-final.log b/opt_prime/examples/pd/http_matrix/evidence265/stream265-owner-cuda-final.log
new file mode 100644
index 00000000..eee064e0
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-owner-cuda-final.log
@@ -0,0 +1,19 @@
+........                                                                 [100%]
+=============================== warnings summary ===============================
+tests/test_pd_stream_admission.py::test_hybrid_partial_import_happens_before_completion_without_admission[cpu]
+  /home/gspark/venvs/pt213/lib/python3.10/site-packages/torch/nn/attention/flex_attention.py:2481: UserWarning: flex_attention called without torch.compile() - this will use an unfused implementation that materializes the full scores matrix instead of generating a fused kernel.
+
+  SOLUTION: Use torch.compile(flex_attention)(...)
+
+  If you want to debug your score_mod/mask_mod, you can set:
+  torch.nn.attention.flex_attention._FLEX_ATTENTION_DISABLE_COMPILE_DEBUG = True
+
+  This will allow you to use print statements or breakpoints. Note: This doesn't work with the backwards pass and may produce incorrect results.
+    _warn_once(
+
+tests/test_pd_stream_admission.py: 14 warnings
+  /home/gspark/venvs/pt213/lib/python3.10/site-packages/torch/jit/_script.py:365: DeprecationWarning: `torch.jit.script_method` is deprecated. Please switch to `torch.compile` or `torch.export`.
+    warnings.warn(
+
+-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html
+8 passed, 15 warnings in 11.04s
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-owner-cuda.log b/opt_prime/examples/pd/http_matrix/evidence265/stream265-owner-cuda.log
new file mode 100644
index 00000000..1d1113c0
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-owner-cuda.log
@@ -0,0 +1,1921 @@
+.F.F.F..                                                                 [100%]
+=================================== FAILURES ===================================
+_ test_hybrid_partial_import_happens_before_completion_without_admission[cuda] _
+
+device = 'cuda'
+
+    @pytest.mark.parametrize("device", ["cpu", "cuda"])
+    def test_hybrid_partial_import_happens_before_completion_without_admission(device):
+        model,worker,b=setup(device);prompt=_prompts(1,25,26,265)[0]
+        result=b.submit(PDRequest('stream',prompt,8));b.step()
+        request=b.by_id['stream'];idx=b.kv.span_index(request.slot,0,len(prompt))
+        keys,_=b.kv.layer_kv(0)
+        torch.testing.assert_close(keys[:,idx].cpu(),worker.jobs[0][2].keys[0])
+        assert not b.kv.layer_kv(1)[0].any()
+        assert b.kv.length[request.slot]==0 and not b.decoding and not result.done()
+>       worker.finish();b.run_until_idle()
+
+tests/test_pd_stream_admission.py:64:
+_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
+opt_prime/accel/pd/batching.py:207: in run_until_idle
+    self.step()
+opt_prime/accel/pd/pd_batching.py:594: in step
+    self._decode()
+../../../venvs/pt213/lib/python3.10/site-packages/torch/utils/_contextlib.py:124: in decorate_context
+    return func(*args, **kwargs)
+opt_prime/accel/pd/pd_batching.py:671: in _decode
+    self._decode_step()
+opt_prime/accel/pd/batching.py:232: in _decode_step
+    logits = self.kv.decode(self.model, slots, lengths, [r.tokens[-1] for r in rows])
+opt_prime/accel/pd/paged.py:565: in decode
+    out = model(ids, past_key_values=cache, use_cache=True, position_ids=pos, logits_to_keep=1)
+../../../venvs/pt213/lib/python3.10/site-packages/torch/nn/modules/module.py:1778: in _wrapped_call_impl
+    return self._call_impl(*args, **kwargs)
+../../../venvs/pt213/lib/python3.10/site-packages/torch/nn/modules/module.py:1789: in _call_impl
+    return forward_call(*args, **kwargs)
+../../../venvs/pt213/lib/python3.10/site-packages/transformers/utils/generic.py:939: in wrapper
+    output = func(self, *args, **kwargs)
+../../../venvs/pt213/lib/python3.10/site-packages/transformers/models/gemma3/modeling_gemma3.py:630: in forward
+    outputs: BaseModelOutputWithPast = self.model(
+../../../venvs/pt213/lib/python3.10/site-packages/torch/nn/modules/module.py:1778: in _wrapped_call_impl
+    return self._call_impl(*args, **kwargs)
+../../../venvs/pt213/lib/python3.10/site-packages/torch/nn/modules/module.py:1789: in _call_impl
+    return forward_call(*args, **kwargs)
+../../../venvs/pt213/lib/python3.10/site-packages/transformers/utils/generic.py:1068: in wrapper
+    output = func(self, *args, **kwargs)
+../../../venvs/pt213/lib/python3.10/site-packages/transformers/utils/output_capturing.py:262: in wrapper
+    outputs = func(self, *args, **kwargs)
+../../../venvs/pt213/lib/python3.10/site-packages/transformers/models/gemma3/modeling_gemma3.py:564: in forward
+    hidden_states = decoder_layer(
+../../../venvs/pt213/lib/python3.10/site-packages/transformers/modeling_layers.py:114: in __call__
+    return super().__call__(*args, **kwargs)
+../../../venvs/pt213/lib/python3.10/site-packages/torch/nn/modules/module.py:1778: in _wrapped_call_impl
+    return self._call_impl(*args, **kwargs)
+../../../venvs/pt213/lib/python3.10/site-packages/torch/nn/modules/module.py:1789: in _call_impl
+    return forward_call(*args, **kwargs)
+../../../venvs/pt213/lib/python3.10/site-packages/transformers/models/gemma3/modeling_gemma3.py:412: in forward
+    hidden_states, _ = self.self_attn(
+../../../venvs/pt213/lib/python3.10/site-packages/torch/nn/modules/module.py:1778: in _wrapped_call_impl
+    return self._call_impl(*args, **kwargs)
+../../../venvs/pt213/lib/python3.10/site-packages/torch/nn/modules/module.py:1789: in _call_impl
+    return forward_call(*args, **kwargs)
+../../../venvs/pt213/lib/python3.10/site-packages/transformers/models/gemma3/modeling_gemma3.py:369: in forward
+    attn_output, attn_weights = attention_interface(
+opt_prime/accel/pd/paged.py:50: in _paged_attention
+    out = _flex(query.device.type)(query, key, value, block_mask=_CTX.masks[group], scale=scaling,
+../../../venvs/pt213/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py:1183: in compile_wrapper
+    raise e.remove_dynamo_frames() from None  # see TORCHDYNAMO_VERBOSE=1
+../../../venvs/pt213/lib/python3.10/site-packages/torch/_inductor/compile_fx.py:1079: in _compile_fx_inner
+    raise InductorError(e, currentframe()).with_traceback(
+../../../venvs/pt213/lib/python3.10/site-packages/torch/_inductor/compile_fx.py:1059: in _compile_fx_inner
+    mb_compiled_graph = fx_codegen_and_compile(
+../../../venvs/pt213/lib/python3.10/site-packages/torch/_inductor/compile_fx.py:1847: in fx_codegen_and_compile
+    return scheme.codegen_and_compile(gm, example_inputs, inputs_to_check, graph_kwargs)
+../../../venvs/pt213/lib/python3.10/site-packages/torch/_inductor/compile_fx.py:1608: in codegen_and_compile
+    compiled_module = graph.compile_to_module()
+../../../venvs/pt213/lib/python3.10/site-packages/torch/_inductor/graph.py:2669: in compile_to_module
+    return self._compile_to_module()
+../../../venvs/pt213/lib/python3.10/site-packages/torch/_inductor/graph.py:2679: in _compile_to_module
+    mod = self._compile_to_module_lines(wrapper_code)
+../../../venvs/pt213/lib/python3.10/site-packages/torch/_inductor/graph.py:2754: in _compile_to_module_lines
+    mod = PyCodeCache.load_by_key_path(
+../../../venvs/pt213/lib/python3.10/site-packages/torch/_inductor/codecache.py:4622: in load_by_key_path
+    mod = _reload_python_module(key, path, set_sys_modules=set_sys_modules)
+../../../venvs/pt213/lib/python3.10/site-packages/torch/_inductor/runtime/compile_tasks.py:35: in _reload_python_module
+    exec(code, mod.__dict__, mod.__dict__)
+/tmp/torchinductor_gspark/34/c347shczffoo6fmqfy647uhpjha6dctqvyuqpnjmparbdcaabhrz.py:56: in <module>
+    triton_tem_fused_flex_attention_0 = async_compile.triton('triton_tem_fused_flex_attention_0', '''
+../../../venvs/pt213/lib/python3.10/site-packages/torch/_inductor/async_compile.py:535: in triton
+    kernel.precompile(
+../../../venvs/pt213/lib/python3.10/site-packages/torch/_inductor/runtime/triton_heuristics.py:665: in precompile
+    self._precompile_worker()
+../../../venvs/pt213/lib/python3.10/site-packages/torch/_inductor/runtime/triton_heuristics.py:687: in _precompile_worker
+    compile_results.append(self._precompile_config(c))
+../../../venvs/pt213/lib/python3.10/site-packages/torch/_inductor/runtime/triton_heuristics.py:1156: in _precompile_config
+    binary = triton.compile(*compile_args, **compile_kwargs)
+../../../venvs/pt213/lib/python3.10/site-packages/triton/compiler/compiler.py:307: in compile
+    module = src.make_ir(target, options, codegen_fns, module_map, context)
+_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
+
+self = <triton.compiler.compiler.ASTSource object at 0x77ea1d7de860>
+target = GPUTarget(backend='cuda', arch=80, warp_size=32)
+options = CUDAOptions(num_warps=2, num_ctas=1, num_stages=1, warp_size=32, maxnreg=None, ptx_version=None, ptx_options=None, ir_...ib/libdevice.10.bc'),), debug=True, backend_name='cuda', sanitize_overflow=False, arch='sm80', instrumentation_mode='')
+codegen_fns = {'convert_custom_types': <function convert_custom_float8_sm80 at 0x77eaa1383c70>, 'min_dot_size': <function min_dot_size.<locals>.check_dot_compatibility at 0x77ea1d8bfd00>}
+module_map = {'triton.language.extra.libdevice': <module 'triton.language.extra.cuda.libdevice' from '/home/gspark/venvs/pt213/lib/python3.10/site-packages/triton/language/extra/cuda/libdevice.py'>}
+context = <triton._C.libtriton.ir.context object at 0x77ea1dbd7670>
+
+    def make_ir(self, target: GPUTarget, options, codegen_fns, module_map, context):
+        from .code_generator import ast_to_ttir
+>       return ast_to_ttir(self.fn, self, context=context, options=options, codegen_fns=codegen_fns,
+                           module_map=module_map)
+E       torch._inductor.exc.InductorError: CompilationError: at 184:20:
+E           off_n = tl.load(kv_indices + indices_idx) * SPARSE_KV_BLOCK_SIZE + off_n_block_in_sparse * BLOCK_N
+E           # first kv block we're loading
+E
+E           # last valid block according to sparse mask
+E           block_n_last_valid = tl.minimum(kv_num_blocks * SPARSE_KV_MULTIPLE, tl.maximum(tl.cdiv(KV_LEN, BLOCK_N), 1))
+E
+E           offs_n = tl.arange(0, BLOCK_N) + off_n
+E
+E           desc_k = None
+E           desc_v = None
+E
+E           acc, l_i, m_i = forward_inner(
+E                           ^
+E
+E       Set TORCHDYNAMO_VERBOSE=1 for the internal stack trace (please do this especially if you're reporting a bug to PyTorch). For even more developer context, set TORCH_LOGS="+dynamo"
+
+../../../venvs/pt213/lib/python3.10/site-packages/triton/compiler/compiler.py:80: InductorError
+----------------------------- Captured stderr call -----------------------------
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0] Triton compilation failed: triton_tem_fused_flex_attention_0
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0] def triton_tem_fused_flex_attention_0(arg_Q, arg_K, arg_V, arg_M, arg_L, arg_KV_NUM_BLKS, arg_KV_IDX, arg_FULL_KV_NUM_BLKS, arg_FULL_KV_IDX, in_ptr9, in_ptr10, out_ptr0):
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     PRESCALE_QK : tl.constexpr = False
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     ROWS_GUARANTEED_SAFE : tl.constexpr = False
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     BLOCKS_ARE_CONTIGUOUS : tl.constexpr = False
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     WRITE_DQ : tl.constexpr = True
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     OUTPUT_LOGSUMEXP : tl.constexpr = False
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     OUTPUT_MAX : tl.constexpr = False
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     FLOAT32_PRECISION : tl.constexpr = 'ieee'
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     IS_DIVISIBLE : tl.constexpr = False
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     GQA_SHARED_HEADS : tl.constexpr = 2
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     HAS_FULL_BLOCKS : tl.constexpr = False
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     SM_SCALE : tl.constexpr = 0.25
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     SPLIT_KV : tl.constexpr = 54
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     QK_HEAD_DIM : tl.constexpr = 16
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     QK_HEAD_DIM_ROUNDED : tl.constexpr = 16
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     V_HEAD_DIM : tl.constexpr = 16
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     V_HEAD_DIM_ROUNDED : tl.constexpr = 16
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     SAFE_HEAD_DIM : tl.constexpr = True
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     BLOCK_M : tl.constexpr = 16
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     SAFE_M_BOUNDARY : tl.constexpr = False
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     SAFE_N_BOUNDARY : tl.constexpr = True
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     BLOCK_N : tl.constexpr = 8
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     SPARSE_Q_BLOCK_SIZE : tl.constexpr = 128
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     SPARSE_KV_BLOCK_SIZE : tl.constexpr = 8
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     USE_TMA : tl.constexpr = False
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     INDEX_DTYPE : tl.constexpr = tl.int32
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     Q = arg_Q
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     K = arg_K
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     V = arg_V
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     M = arg_M
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     L = arg_L
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     KV_NUM_BLKS = arg_KV_NUM_BLKS
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     KV_IDX = arg_KV_IDX
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     FULL_KV_NUM_BLKS = arg_FULL_KV_NUM_BLKS
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     FULL_KV_IDX = arg_FULL_KV_IDX
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     # Sub notation for this kernel:
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     # Q: Query, K: Key, V: Value
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     # reduction buffers: M rowmax across local KV split, L local sumexp across local KV split
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     # M: Number of queries, N: Number of keys/values
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     # QK_HEAD_DIM: The dimension of the query and key embeddings
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     # V_HEAD_DIM: The dimension of the value embeddings
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     # BLOCK_M, QK_HEAD_DIM: M, and D dimemsion are always assigned to the same block
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     # z: Batch size, h: Number of heads, m: Number of queries per head, k: Number of keys per head t: Number of kv splits
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     # (Modifiable) Config options:
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     # SPLIT_KV: number of blocks K & V are split into
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     # TILE_KV: length of each local KV split
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     # BLOCK_M: block size that Q is padded along seqlen dim.
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     # BLOCK_N: block size of K & V along N dimension.
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     # GQA_SHARED_HEADS: number of query heads sharing one kv head in GQA setups.
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     #
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     # change of base out of the loop
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     # ROWS_GUARANTEED_SAFE: Is it guaranteed that at least one value in each row
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     # is not masked out? If so, we can skip an extra safety check
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     # SAFE_M_BOUNDARY: Is Q seqlen a multiple of BLOCK_M? If so, we can skip an extra boundary check for loading query.
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     # SAFE_N_BOUNDARY: Is KV seqlen a multiple of BLOCK_N? If so, we can skip an extra boundary check for loading key/value.
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     # PRESCALE_QK: Whether to pre-scale QK by 1/sqrt(d) and change of base.
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     #
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     # SPARSE_KV_BLOCK_SIZE: sparse mask block size along KV seqlen dim.
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     # KV_NUM_BLKS: The number of KV blocks (that may or may not require masking) for each query.
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     # KV_IDX: The indices of KV blocks (that may or may not require masking) for each query.
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     #
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     #
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     # Output: ACC output accumulated across local KV split.
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     tl.static_assert(SPARSE_KV_BLOCK_SIZE >= BLOCK_N and SPARSE_KV_BLOCK_SIZE % BLOCK_N == 0)
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     # Define Q Strides
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     stride_qz, stride_qh, stride_qg, stride_qm, stride_qk = 64, 32, 16, 16, 1
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     stride_kz, stride_kh, stride_kn, stride_kk = 8192, 4096, 16, 1
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     stride_vz, stride_vh, stride_vn, stride_vk = 8192, 4096, 16, 1
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     stride_mz, stride_mt, stride_mh, stride_mm = 216, 4, 1, 1
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     stride_lz, stride_lt, stride_lh, stride_lm = 216, 4, 1, 1
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     Z = 2
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     ZKV = 1
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     HKV = 2
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     G: tl.constexpr = GQA_SHARED_HEADS
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     HQ = HKV * G
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     Q_LEN = 1
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     KV_LEN = 256
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     MATMUL_PRECISION = Q.dtype.element_ty
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     NUM_BLOCK_M = tl.cdiv(Q_LEN * G, BLOCK_M)
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     off_block_m = tl.program_id(0).to(INDEX_DTYPE)
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     off_z = tl.program_id(1).to(INDEX_DTYPE) // HKV
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     off_zkv = off_z % ZKV
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     off_hkv = tl.program_id(1).to(INDEX_DTYPE) % HKV
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     off_t = tl.program_id(2).to(INDEX_DTYPE)
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     q_offset = off_z * stride_qz + off_hkv * stride_qh
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     k_offset = off_zkv * stride_kz + off_hkv * stride_kh
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     v_offset = off_zkv * stride_vz + off_hkv * stride_vh
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     K = K + k_offset
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     V = V + v_offset
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     SPARSE_Z = 2
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     SPARSE_HQ = 1
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     sparse_idx_z = off_z % SPARSE_Z
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     sparse_idx_h = off_hkv % SPARSE_HQ
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     SPARSE_KV_MULTIPLE: tl.constexpr = (SPARSE_KV_BLOCK_SIZE // BLOCK_N)
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     SPARSE_KV_BLOCK_CNT = tl.cdiv(KV_LEN, SPARSE_KV_BLOCK_SIZE)
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     # initialize pointer to m and l
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     m_i = tl.zeros([BLOCK_M], dtype=tl.float32) - float("inf")
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     l_i = tl.zeros([BLOCK_M], dtype=tl.float32)
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     acc = tl.zeros([BLOCK_M, V_HEAD_DIM_ROUNDED], dtype=tl.float32)
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     # initialize offsets
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     tl.device_assert(BLOCK_M % G == 0)
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     BLOCK_M_PER_HQ: tl.constexpr = BLOCK_M // G
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     off_g = tl.arange(0, G)                                                 # [G]
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     offs_g = tl.ravel(tl.broadcast_to(off_g[:, None], [G, BLOCK_M_PER_HQ])) # [BLOCK_M]
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     offs_hq = offs_g + off_hkv * G
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     off_m = tl.arange(0, BLOCK_M_PER_HQ) + off_block_m * BLOCK_M_PER_HQ
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     offs_m = tl.ravel(tl.broadcast_to(off_m[None, :], [G, BLOCK_M_PER_HQ])) # [BLOCK_M]
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     offs_d = tl.arange(0, QK_HEAD_DIM_ROUNDED)
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     offs_vd = tl.arange(0, V_HEAD_DIM_ROUNDED)
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     # Get HZ offsets for KV_NUM_BLKS and KV_IDX
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     sparse_idx_m = off_block_m * BLOCK_M_PER_HQ // SPARSE_Q_BLOCK_SIZE
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     stride_block_z, stride_block_h, stride_block_row = 1, 1, 1
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     sparse_block_zhm_offset = sparse_idx_z * stride_block_z + sparse_idx_h * stride_block_h + sparse_idx_m * stride_block_row
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     stride_kv_z, stride_kv_h, stride_kv_row, stride_kv_col = 32, 32, 32, 1
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     sparse_idx_zhm_offset = sparse_idx_z * stride_kv_z + sparse_idx_h * stride_kv_h + sparse_idx_m * stride_kv_row
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     q_range = stride_qg * off_g[:, None, None] + stride_qm * off_m[None, :, None] + stride_qk * offs_d[None, None, :]
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     if not SAFE_M_BOUNDARY and not SAFE_HEAD_DIM:
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]         q = tl.load(Q + q_offset + q_range, mask=(offs_d[None, None, :] < QK_HEAD_DIM) & (off_m[None, :, None] < Q_LEN))
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     elif SAFE_M_BOUNDARY and not SAFE_HEAD_DIM:
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]         q = tl.load(Q + q_offset + q_range, mask=offs_d[None, None, :] < QK_HEAD_DIM)
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     elif not SAFE_M_BOUNDARY and SAFE_HEAD_DIM:
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]         q = tl.load(Q + q_offset + q_range, mask=off_m[None, :, None] < Q_LEN)
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     else:
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]         q = tl.load(Q + q_offset + q_range)
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     q = tl.reshape(q, [BLOCK_M, QK_HEAD_DIM_ROUNDED])
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     # ~~~~~~~~~~~~~~ normal blocks ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     # find first kv block we are loading and the number of blocks we are loading
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     # Offset the kv_indices tensor by the correct batch and head
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     kv_indices = KV_IDX + sparse_idx_zhm_offset
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     kv_num_blocks = tl.load(KV_NUM_BLKS + sparse_block_zhm_offset)
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     # Make sure each split is a multiple of BLOCK_N
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     if HAS_FULL_BLOCKS:
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]         # TODO do the optimization as partial blocks once vllm is
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]         # able to pass in full blocks:
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]         # https://github.com/vllm-project/vllm/blob/61249b177de1566027fc74e9b51b45a4c973eb47/vllm/v1/attention/backends/flex_attention.py#L615-L616
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]         TILE_KV_OG = tl.cdiv(KV_LEN, SPLIT_KV)
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     else:
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]         TILE_KV_OG = tl.cdiv(kv_num_blocks * SPARSE_KV_BLOCK_SIZE, SPLIT_KV)
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     TILE_KV = tl.cdiv(TILE_KV_OG, BLOCK_N) * BLOCK_N
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     TILE_KV_MULTIPLE: tl.constexpr = (TILE_KV // BLOCK_N)
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     # Calculate KV blocks that belong to this CTA.
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     block_n_start = off_t * TILE_KV_MULTIPLE                        # n_offset inside sparse block
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     block_n_end = block_n_start + TILE_KV_MULTIPLE                  # end BLOCK_N
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     MAX_KV_IDX = 32
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     indices_idx = (block_n_start // SPARSE_KV_MULTIPLE) % (MAX_KV_IDX)
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     off_n_block_in_sparse = block_n_start % SPARSE_KV_MULTIPLE
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     off_n = tl.load(kv_indices + indices_idx) * SPARSE_KV_BLOCK_SIZE + off_n_block_in_sparse * BLOCK_N
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     # first kv block we're loading
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     # last valid block according to sparse mask
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     block_n_last_valid = tl.minimum(kv_num_blocks * SPARSE_KV_MULTIPLE, tl.maximum(tl.cdiv(KV_LEN, BLOCK_N), 1))
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     offs_n = tl.arange(0, BLOCK_N) + off_n
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     desc_k = None
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     desc_v = None
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     acc, l_i, m_i = forward_inner(
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]         arg_Q, arg_K, arg_V, arg_M, arg_L, arg_KV_NUM_BLKS, arg_KV_IDX, arg_FULL_KV_NUM_BLKS, arg_FULL_KV_IDX, in_ptr9, in_ptr10, out_ptr0,
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]         q, K, V, desc_k, desc_v, Q_LEN, KV_LEN,
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]         # accumulatd values
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]         acc, l_i, m_i,
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]         #offsets
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]         off_z, offs_hq[:, None], offs_m[:, None], offs_n[None, :],
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]         off_n,
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]         #block sparse data
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]         kv_indices, kv_num_blocks,
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]         block_n_start, block_n_end if block_n_end <= block_n_last_valid else block_n_last_valid,
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]         MATMUL_PRECISION,
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]         stride_kk, stride_kn, stride_vn, stride_vk,
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]         IS_FULL_BLOCKS=False,
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     )
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     # ~~~~~~~~~~~~~~ "full" blocks ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     # We know these blocks are guaranteed to be "full", so we don't need to
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     # apply mask_mod to them - only score_mod
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     if HAS_FULL_BLOCKS:
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]         kv_indices = FULL_KV_IDX + sparse_idx_zhm_offset
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]         kv_num_blocks = tl.load(FULL_KV_NUM_BLKS + sparse_block_zhm_offset)
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]         # Assign full block in a reverse order for off_t. Prioritize the last CTA.
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]         block_n_start = (SPLIT_KV - off_t - 1) * TILE_KV_MULTIPLE
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]         block_n_end = block_n_start + TILE_KV_MULTIPLE
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]         indices_idx = (block_n_start // SPARSE_KV_MULTIPLE) % (MAX_KV_IDX)
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]         off_n_block_in_sparse = block_n_start % SPARSE_KV_MULTIPLE
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]         off_n = tl.load(kv_indices + indices_idx) * SPARSE_KV_BLOCK_SIZE + off_n_block_in_sparse * BLOCK_N
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]         # last valid block according to sparse mask
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]         block_n_last_valid = tl.minimum(kv_num_blocks * SPARSE_KV_MULTIPLE, tl.maximum(tl.cdiv(KV_LEN, BLOCK_N), 1))
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]         offs_n = tl.arange(0, BLOCK_N) + off_n
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]         acc, l_i, m_i = forward_inner(
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]             arg_Q, arg_K, arg_V, arg_M, arg_L, arg_KV_NUM_BLKS, arg_KV_IDX, arg_FULL_KV_NUM_BLKS, arg_FULL_KV_IDX, in_ptr9, in_ptr10, out_ptr0,
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]             q, K, V, desc_k, desc_v, Q_LEN, KV_LEN,
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]             # accumulatd values
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]             acc, l_i, m_i,
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]             #offsets
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]             off_z, offs_hq[:, None], offs_m[:, None], offs_n[None, :],
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]             off_n,
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]             #block sparse data
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]             kv_indices, kv_num_blocks,
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]             block_n_start, block_n_end if block_n_end <= block_n_last_valid else block_n_last_valid,
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]             MATMUL_PRECISION,
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]             stride_kk, stride_kn, stride_vn, stride_vk,
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]             IS_FULL_BLOCKS=True,
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]         )
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     m_offset = off_t * stride_mt + off_z * stride_mz
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     l_offset = off_t * stride_lt + off_z * stride_lz
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     ml_h_offsets = off_hkv * G + off_g[:, None]
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     ml_m_offsets = off_m[None, :]
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     m_ptrs = M + m_offset + ml_h_offsets * stride_mh + ml_m_offsets * stride_mm
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     l_ptrs = L + l_offset + ml_h_offsets * stride_lh + ml_m_offsets * stride_lm
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     # Store output, logsumexp and rowmax for cross CTA reduction. (all in float32, even when input data are in fp16)
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     m_i = m_i.reshape(G, BLOCK_M_PER_HQ)
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     l_i = l_i.reshape(G, BLOCK_M_PER_HQ)
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     if SAFE_M_BOUNDARY:
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]         tl.store(m_ptrs, m_i)
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]         tl.store(l_ptrs, l_i)
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     else:
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]         ml_mask = ml_m_offsets < Q_LEN
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]         tl.store(m_ptrs, m_i, mask=ml_mask)
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]         tl.store(l_ptrs, l_i, mask=ml_mask)
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     # -- store output
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     idx_z = off_z
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     idx_t = off_t
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     idx_hq = off_hkv*G + off_g[:, None, None]
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     idx_m = off_m[None, :, None]
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     idx_d = offs_vd[None, None, :]
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     mask = (idx_m < Q_LEN) & (idx_d < V_HEAD_DIM)
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     acc = acc.reshape(G, BLOCK_M_PER_HQ, V_HEAD_DIM_ROUNDED)
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     xindex = idx_d + 16*idx_hq + 16*idx_m + 64*idx_t + 3456*idx_z
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     tl.store(out_ptr0 + (tl.broadcast_to(idx_d + 16*idx_hq + 64*idx_t + 3456*idx_z, [GQA_SHARED_HEADS, BLOCK_M_PER_HQ, V_HEAD_DIM_ROUNDED])), acc, mask)
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0] metadata: {'signature': {'arg_Q': '*bf16', 'arg_K': '*bf16', 'arg_V': '*bf16', 'arg_M': '*fp32', 'arg_L': '*fp32', 'arg_KV_NUM_BLKS': '*i32', 'arg_KV_IDX': '*i32', 'arg_FULL_KV_NUM_BLKS': '*fp32', 'arg_FULL_KV_IDX': '*fp32', 'in_ptr9': '*i32', 'in_ptr10': '*i32', 'out_ptr0': '*fp32'}, 'device': 0, 'constants': {}, 'configs': [{(0,): [['tt.divisibility', 16]], (1,): [['tt.divisibility', 16]], (2,): [['tt.divisibility', 16]], (3,): [['tt.divisibility', 16]], (4,): [['tt.divisibility', 16]], (5,): [['tt.divisibility', 16]], (6,): [['tt.divisibility', 16]], (7,): [['tt.divisibility', 16]], (8,): [['tt.divisibility', 16]], (9,): [['tt.divisibility', 16]], (10,): [['tt.divisibility', 16]], (11,): [['tt.divisibility', 16]]}], 'device_type': 'cuda', 'num_warps': 2, 'num_stages': 1, 'debug': True, 'cc': 80}
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0] Traceback (most recent call last):
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]   File "/home/gspark/venvs/pt213/lib/python3.10/site-packages/triton/language/core.py", line 43, in wrapper
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     return fn(*args, **kwargs)
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]   File "/home/gspark/venvs/pt213/lib/python3.10/site-packages/triton/language/core.py", line 2090, in dot
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     res = _semantic.dot(input, other, acc, input_precision, max_num_imprecise_acc, out_dtype)
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]   File "/home/gspark/venvs/pt213/lib/python3.10/site-packages/triton/language/semantic.py", line 1539, in dot
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     and rhs.shape[-1].value >= min_dot_size[1], \
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0] AssertionError: Input shapes should have M >= 1, N >= 1 and K >= 16
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0] The above exception was the direct cause of the following exception:
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0] triton.compiler.errors.CompilationError: at 146:10:
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     p = tl.math.exp2(post_mod_scores - m_ij_masked[:, None])
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     # NB: l_i update is pulled up here since it's a bit faster
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     # NB: For headdim=256, it's faster to move it back down to after m_i =
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     # m_ij
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     l_i = l_i * alpha + tl.sum(p, 1)
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     # # -- scale and update acc --
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     acc = acc * alpha[:, None]
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     # Calculate offsets for V loading - reuse kv_base_offset from K loading
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     offs_v = tl.arange(0, V_HEAD_DIM_ROUNDED)
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     v = load_checked_2d(V, offs_n_load, offs_v, stride_vn, stride_vk, IS_DIVISIBLE, SAFE_HEAD_DIM, KV_LEN, V_HEAD_DIM)
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     acc = tl.dot(p.to(MATMUL_PRECISION), v.to(q.dtype), acc, input_precision=FLOAT32_PRECISION)
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]           ^
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0] Input shapes should have M >= 1, N >= 1 and K >= 16
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0] The above exception was the direct cause of the following exception:
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0] triton.compiler.errors.CompilationError: at 81:28:
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]                 kv_offset,
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]                 MATMUL_PRECISION, RCP_LN2,
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]                 # Strides for K and V
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]                 stride_kk, stride_kn, stride_vn, stride_vk,
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]                 IS_FULL_BLOCKS,
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]             )
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]         else:
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]             # Benchmark shows even we applied mod & mask to each block for non divisible seqlen,
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]             # it's on par or slightly faster than only applying to the last block in fwd.
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]             # However, we choose different strategy for bwd, where we only apply mod & mask
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]             # to the last block because it's faster a lot.
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]             acc, l_i, m_i = forward_block_mn(
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]                             ^
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0] The above exception was the direct cause of the following exception:
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0] Traceback (most recent call last):
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]   File "/home/gspark/venvs/pt213/lib/python3.10/site-packages/torch/_inductor/runtime/triton_heuristics.py", line 1156, in _precompile_config
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     binary = triton.compile(*compile_args, **compile_kwargs)
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]   File "/home/gspark/venvs/pt213/lib/python3.10/site-packages/triton/compiler/compiler.py", line 307, in compile
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     module = src.make_ir(target, options, codegen_fns, module_map, context)
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]   File "/home/gspark/venvs/pt213/lib/python3.10/site-packages/triton/compiler/compiler.py", line 80, in make_ir
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     return ast_to_ttir(self.fn, self, context=context, options=options, codegen_fns=codegen_fns,
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0] triton.compiler.errors.CompilationError: at 184:20:
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     off_n = tl.load(kv_indices + indices_idx) * SPARSE_KV_BLOCK_SIZE + off_n_block_in_sparse * BLOCK_N
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     # first kv block we're loading
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     # last valid block according to sparse mask
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     block_n_last_valid = tl.minimum(kv_num_blocks * SPARSE_KV_MULTIPLE, tl.maximum(tl.cdiv(KV_LEN, BLOCK_N), 1))
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     offs_n = tl.arange(0, BLOCK_N) + off_n
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     desc_k = None
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     desc_v = None
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]     acc, l_i, m_i = forward_inner(
+E1024 06:13:16.268000 1365207 torch/_inductor/runtime/triton_heuristics.py:1158] [1/0]                     ^
+------------------------------ Captured log call -------------------------------
+DEBUG    torch.__trace:utils.py:2350
+DEBUG    torch.__trace:utils.py:2350
+DEBUG    torch.__trace:convert_frame.py:303
+DEBUG    torch.__trace:utils.py:2350
+DEBUG    torch.__trace:utils.py:2350
+DEBUG    torch.__trace:meta_utils.py:276
+DEBUG    torch.__trace:meta_utils.py:514
+DEBUG    torch.__trace:meta_utils.py:2271
+DEBUG    torch.__trace:meta_utils.py:276
+DEBUG    torch.__trace:meta_utils.py:514
+DEBUG    torch.__trace:meta_utils.py:514
+DEBUG    torch.__trace:meta_utils.py:2271
+DEBUG    torch.__trace:meta_utils.py:276
+DEBUG    torch.__trace:meta_utils.py:514
+DEBUG    torch.__trace:meta_utils.py:514
+DEBUG    torch.__trace:meta_utils.py:2271
+DEBUG    torch.__trace:meta_utils.py:276
+DEBUG    torch.__trace:meta_utils.py:514
+DEBUG    torch.__trace:meta_utils.py:2271
+DEBUG    torch.__trace:meta_utils.py:276
+DEBUG    torch.__trace:meta_utils.py:514
+DEBUG    torch.__trace:meta_utils.py:2271
+DEBUG    torch.__trace:meta_utils.py:276
+DEBUG    torch.__trace:meta_utils.py:514
+DEBUG    torch.__trace:meta_utils.py:2271
+DEBUG    torch.__trace:meta_utils.py:276
+DEBUG    torch.__trace:meta_utils.py:514
+DEBUG    torch.__trace:meta_utils.py:2271
+DEBUG    torch.__trace:meta_utils.py:276
+DEBUG    torch.__trace:meta_utils.py:514
+DEBUG    torch.__trace:meta_utils.py:2271
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+ERROR    torch._inductor.runtime.triton_heuristics:triton_heuristics.py:1158 Triton compilation failed: triton_tem_fused_flex_attention_0
+def triton_tem_fused_flex_attention_0(arg_Q, arg_K, arg_V, arg_M, arg_L, arg_KV_NUM_BLKS, arg_KV_IDX, arg_FULL_KV_NUM_BLKS, arg_FULL_KV_IDX, in_ptr9, in_ptr10, out_ptr0):
+    PRESCALE_QK : tl.constexpr = False
+    ROWS_GUARANTEED_SAFE : tl.constexpr = False
+    BLOCKS_ARE_CONTIGUOUS : tl.constexpr = False
+    WRITE_DQ : tl.constexpr = True
+    OUTPUT_LOGSUMEXP : tl.constexpr = False
+    OUTPUT_MAX : tl.constexpr = False
+    FLOAT32_PRECISION : tl.constexpr = 'ieee'
+    IS_DIVISIBLE : tl.constexpr = False
+    GQA_SHARED_HEADS : tl.constexpr = 2
+    HAS_FULL_BLOCKS : tl.constexpr = False
+    SM_SCALE : tl.constexpr = 0.25
+    SPLIT_KV : tl.constexpr = 54
+    QK_HEAD_DIM : tl.constexpr = 16
+    QK_HEAD_DIM_ROUNDED : tl.constexpr = 16
+    V_HEAD_DIM : tl.constexpr = 16
+    V_HEAD_DIM_ROUNDED : tl.constexpr = 16
+    SAFE_HEAD_DIM : tl.constexpr = True
+    BLOCK_M : tl.constexpr = 16
+    SAFE_M_BOUNDARY : tl.constexpr = False
+    SAFE_N_BOUNDARY : tl.constexpr = True
+    BLOCK_N : tl.constexpr = 8
+    SPARSE_Q_BLOCK_SIZE : tl.constexpr = 128
+    SPARSE_KV_BLOCK_SIZE : tl.constexpr = 8
+    USE_TMA : tl.constexpr = False
+    INDEX_DTYPE : tl.constexpr = tl.int32
+    Q = arg_Q
+    K = arg_K
+    V = arg_V
+    M = arg_M
+    L = arg_L
+    KV_NUM_BLKS = arg_KV_NUM_BLKS
+    KV_IDX = arg_KV_IDX
+    FULL_KV_NUM_BLKS = arg_FULL_KV_NUM_BLKS
+    FULL_KV_IDX = arg_FULL_KV_IDX
+
+    # Sub notation for this kernel:
+    # Q: Query, K: Key, V: Value
+    # reduction buffers: M rowmax across local KV split, L local sumexp across local KV split
+    # M: Number of queries, N: Number of keys/values
+    # QK_HEAD_DIM: The dimension of the query and key embeddings
+    # V_HEAD_DIM: The dimension of the value embeddings
+    # BLOCK_M, QK_HEAD_DIM: M, and D dimemsion are always assigned to the same block
+    # z: Batch size, h: Number of heads, m: Number of queries per head, k: Number of keys per head t: Number of kv splits
+    # (Modifiable) Config options:
+    # SPLIT_KV: number of blocks K & V are split into
+    # TILE_KV: length of each local KV split
+    # BLOCK_M: block size that Q is padded along seqlen dim.
+    # BLOCK_N: block size of K & V along N dimension.
+    # GQA_SHARED_HEADS: number of query heads sharing one kv head in GQA setups.
+    #
+    # change of base out of the loop
+    # ROWS_GUARANTEED_SAFE: Is it guaranteed that at least one value in each row
+    # is not masked out? If so, we can skip an extra safety check
+    # SAFE_M_BOUNDARY: Is Q seqlen a multiple of BLOCK_M? If so, we can skip an extra boundary check for loading query.
+    # SAFE_N_BOUNDARY: Is KV seqlen a multiple of BLOCK_N? If so, we can skip an extra boundary check for loading key/value.
+
+    # PRESCALE_QK: Whether to pre-scale QK by 1/sqrt(d) and change of base.
+    #
+    # SPARSE_KV_BLOCK_SIZE: sparse mask block size along KV seqlen dim.
+    # KV_NUM_BLKS: The number of KV blocks (that may or may not require masking) for each query.
+    # KV_IDX: The indices of KV blocks (that may or may not require masking) for each query.
+    #
+    #
+    # Output: ACC output accumulated across local KV split.
+
+    tl.static_assert(SPARSE_KV_BLOCK_SIZE >= BLOCK_N and SPARSE_KV_BLOCK_SIZE % BLOCK_N == 0)
+
+    # Define Q Strides
+    stride_qz, stride_qh, stride_qg, stride_qm, stride_qk = 64, 32, 16, 16, 1
+    stride_kz, stride_kh, stride_kn, stride_kk = 8192, 4096, 16, 1
+    stride_vz, stride_vh, stride_vn, stride_vk = 8192, 4096, 16, 1
+    stride_mz, stride_mt, stride_mh, stride_mm = 216, 4, 1, 1
+    stride_lz, stride_lt, stride_lh, stride_lm = 216, 4, 1, 1
+
+
+    Z = 2
+    ZKV = 1
+    HKV = 2
+    G: tl.constexpr = GQA_SHARED_HEADS
+    HQ = HKV * G
+    Q_LEN = 1
+    KV_LEN = 256
+
+    MATMUL_PRECISION = Q.dtype.element_ty
+
+    NUM_BLOCK_M = tl.cdiv(Q_LEN * G, BLOCK_M)
+
+    off_block_m = tl.program_id(0).to(INDEX_DTYPE)
+
+    off_z = tl.program_id(1).to(INDEX_DTYPE) // HKV
+    off_zkv = off_z % ZKV
+    off_hkv = tl.program_id(1).to(INDEX_DTYPE) % HKV
+    off_t = tl.program_id(2).to(INDEX_DTYPE)
+
+    q_offset = off_z * stride_qz + off_hkv * stride_qh
+    k_offset = off_zkv * stride_kz + off_hkv * stride_kh
+    v_offset = off_zkv * stride_vz + off_hkv * stride_vh
+
+    K = K + k_offset
+    V = V + v_offset
+
+    SPARSE_Z = 2
+    SPARSE_HQ = 1
+
+    sparse_idx_z = off_z % SPARSE_Z
+    sparse_idx_h = off_hkv % SPARSE_HQ
+
+    SPARSE_KV_MULTIPLE: tl.constexpr = (SPARSE_KV_BLOCK_SIZE // BLOCK_N)
+    SPARSE_KV_BLOCK_CNT = tl.cdiv(KV_LEN, SPARSE_KV_BLOCK_SIZE)
+
+    # initialize pointer to m and l
+    m_i = tl.zeros([BLOCK_M], dtype=tl.float32) - float("inf")
+    l_i = tl.zeros([BLOCK_M], dtype=tl.float32)
+    acc = tl.zeros([BLOCK_M, V_HEAD_DIM_ROUNDED], dtype=tl.float32)
+
+    # initialize offsets
+    tl.device_assert(BLOCK_M % G == 0)
+    BLOCK_M_PER_HQ: tl.constexpr = BLOCK_M // G
+    off_g = tl.arange(0, G)                                                 # [G]
+    offs_g = tl.ravel(tl.broadcast_to(off_g[:, None], [G, BLOCK_M_PER_HQ])) # [BLOCK_M]
+    offs_hq = offs_g + off_hkv * G
+    off_m = tl.arange(0, BLOCK_M_PER_HQ) + off_block_m * BLOCK_M_PER_HQ
+    offs_m = tl.ravel(tl.broadcast_to(off_m[None, :], [G, BLOCK_M_PER_HQ])) # [BLOCK_M]
+    offs_d = tl.arange(0, QK_HEAD_DIM_ROUNDED)
+    offs_vd = tl.arange(0, V_HEAD_DIM_ROUNDED)
+
+    # Get HZ offsets for KV_NUM_BLKS and KV_IDX
+    sparse_idx_m = off_block_m * BLOCK_M_PER_HQ // SPARSE_Q_BLOCK_SIZE
+    stride_block_z, stride_block_h, stride_block_row = 1, 1, 1
+    sparse_block_zhm_offset = sparse_idx_z * stride_block_z + sparse_idx_h * stride_block_h + sparse_idx_m * stride_block_row
+    stride_kv_z, stride_kv_h, stride_kv_row, stride_kv_col = 32, 32, 32, 1
+    sparse_idx_zhm_offset = sparse_idx_z * stride_kv_z + sparse_idx_h * stride_kv_h + sparse_idx_m * stride_kv_row
+
+    q_range = stride_qg * off_g[:, None, None] + stride_qm * off_m[None, :, None] + stride_qk * offs_d[None, None, :]
+
+    if not SAFE_M_BOUNDARY and not SAFE_HEAD_DIM:
+        q = tl.load(Q + q_offset + q_range, mask=(offs_d[None, None, :] < QK_HEAD_DIM) & (off_m[None, :, None] < Q_LEN))
+    elif SAFE_M_BOUNDARY and not SAFE_HEAD_DIM:
+        q = tl.load(Q + q_offset + q_range, mask=offs_d[None, None, :] < QK_HEAD_DIM)
+    elif not SAFE_M_BOUNDARY and SAFE_HEAD_DIM:
+        q = tl.load(Q + q_offset + q_range, mask=off_m[None, :, None] < Q_LEN)
+    else:
+        q = tl.load(Q + q_offset + q_range)
+
+    q = tl.reshape(q, [BLOCK_M, QK_HEAD_DIM_ROUNDED])
+
+
+    # ~~~~~~~~~~~~~~ normal blocks ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+    # find first kv block we are loading and the number of blocks we are loading
+    # Offset the kv_indices tensor by the correct batch and head
+    kv_indices = KV_IDX + sparse_idx_zhm_offset
+    kv_num_blocks = tl.load(KV_NUM_BLKS + sparse_block_zhm_offset)
+
+    # Make sure each split is a multiple of BLOCK_N
+    if HAS_FULL_BLOCKS:
+        # TODO do the optimization as partial blocks once vllm is
+        # able to pass in full blocks:
+        # https://github.com/vllm-project/vllm/blob/61249b177de1566027fc74e9b51b45a4c973eb47/vllm/v1/attention/backends/flex_attention.py#L615-L616
+        TILE_KV_OG = tl.cdiv(KV_LEN, SPLIT_KV)
+    else:
+        TILE_KV_OG = tl.cdiv(kv_num_blocks * SPARSE_KV_BLOCK_SIZE, SPLIT_KV)
+    TILE_KV = tl.cdiv(TILE_KV_OG, BLOCK_N) * BLOCK_N
+    TILE_KV_MULTIPLE: tl.constexpr = (TILE_KV // BLOCK_N)
+
+    # Calculate KV blocks that belong to this CTA.
+    block_n_start = off_t * TILE_KV_MULTIPLE                        # n_offset inside sparse block
+    block_n_end = block_n_start + TILE_KV_MULTIPLE                  # end BLOCK_N
+
+    MAX_KV_IDX = 32
+    indices_idx = (block_n_start // SPARSE_KV_MULTIPLE) % (MAX_KV_IDX)
+    off_n_block_in_sparse = block_n_start % SPARSE_KV_MULTIPLE
+    off_n = tl.load(kv_indices + indices_idx) * SPARSE_KV_BLOCK_SIZE + off_n_block_in_sparse * BLOCK_N
+    # first kv block we're loading
+
+    # last valid block according to sparse mask
+    block_n_last_valid = tl.minimum(kv_num_blocks * SPARSE_KV_MULTIPLE, tl.maximum(tl.cdiv(KV_LEN, BLOCK_N), 1))
+
+    offs_n = tl.arange(0, BLOCK_N) + off_n
+
+    desc_k = None
+    desc_v = None
+
+    acc, l_i, m_i = forward_inner(
+        arg_Q, arg_K, arg_V, arg_M, arg_L, arg_KV_NUM_BLKS, arg_KV_IDX, arg_FULL_KV_NUM_BLKS, arg_FULL_KV_IDX, in_ptr9, in_ptr10, out_ptr0,
+        q, K, V, desc_k, desc_v, Q_LEN, KV_LEN,
+        # accumulatd values
+        acc, l_i, m_i,
+        #offsets
+        off_z, offs_hq[:, None], offs_m[:, None], offs_n[None, :],
+        off_n,
+        #block sparse data
+        kv_indices, kv_num_blocks,
+        block_n_start, block_n_end if block_n_end <= block_n_last_valid else block_n_last_valid,
+        MATMUL_PRECISION,
+        stride_kk, stride_kn, stride_vn, stride_vk,
+        IS_FULL_BLOCKS=False,
+    )
+
+
+    # ~~~~~~~~~~~~~~ "full" blocks ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+    # We know these blocks are guaranteed to be "full", so we don't need to
+    # apply mask_mod to them - only score_mod
+    if HAS_FULL_BLOCKS:
+        kv_indices = FULL_KV_IDX + sparse_idx_zhm_offset
+        kv_num_blocks = tl.load(FULL_KV_NUM_BLKS + sparse_block_zhm_offset)
+        # Assign full block in a reverse order for off_t. Prioritize the last CTA.
+        block_n_start = (SPLIT_KV - off_t - 1) * TILE_KV_MULTIPLE
+        block_n_end = block_n_start + TILE_KV_MULTIPLE
+        indices_idx = (block_n_start // SPARSE_KV_MULTIPLE) % (MAX_KV_IDX)
+        off_n_block_in_sparse = block_n_start % SPARSE_KV_MULTIPLE
+        off_n = tl.load(kv_indices + indices_idx) * SPARSE_KV_BLOCK_SIZE + off_n_block_in_sparse * BLOCK_N
+
+        # last valid block according to sparse mask
+        block_n_last_valid = tl.minimum(kv_num_blocks * SPARSE_KV_MULTIPLE, tl.maximum(tl.cdiv(KV_LEN, BLOCK_N), 1))
+
+        offs_n = tl.arange(0, BLOCK_N) + off_n
+
+        acc, l_i, m_i = forward_inner(
+            arg_Q, arg_K, arg_V, arg_M, arg_L, arg_KV_NUM_BLKS, arg_KV_IDX, arg_FULL_KV_NUM_BLKS, arg_FULL_KV_IDX, in_ptr9, in_ptr10, out_ptr0,
+            q, K, V, desc_k, desc_v, Q_LEN, KV_LEN,
+            # accumulatd values
+            acc, l_i, m_i,
+            #offsets
+            off_z, offs_hq[:, None], offs_m[:, None], offs_n[None, :],
+            off_n,
+            #block sparse data
+            kv_indices, kv_num_blocks,
+            block_n_start, block_n_end if block_n_end <= block_n_last_valid else block_n_last_valid,
+            MATMUL_PRECISION,
+            stride_kk, stride_kn, stride_vn, stride_vk,
+            IS_FULL_BLOCKS=True,
+        )
+
+    m_offset = off_t * stride_mt + off_z * stride_mz
+    l_offset = off_t * stride_lt + off_z * stride_lz
+
+    ml_h_offsets = off_hkv * G + off_g[:, None]
+    ml_m_offsets = off_m[None, :]
+    m_ptrs = M + m_offset + ml_h_offsets * stride_mh + ml_m_offsets * stride_mm
+    l_ptrs = L + l_offset + ml_h_offsets * stride_lh + ml_m_offsets * stride_lm
+
+    # Store output, logsumexp and rowmax for cross CTA reduction. (all in float32, even when input data are in fp16)
+    m_i = m_i.reshape(G, BLOCK_M_PER_HQ)
+    l_i = l_i.reshape(G, BLOCK_M_PER_HQ)
+    if SAFE_M_BOUNDARY:
+        tl.store(m_ptrs, m_i)
+        tl.store(l_ptrs, l_i)
+    else:
+        ml_mask = ml_m_offsets < Q_LEN
+        tl.store(m_ptrs, m_i, mask=ml_mask)
+        tl.store(l_ptrs, l_i, mask=ml_mask)
+
+    # -- store output
+    idx_z = off_z
+    idx_t = off_t
+    idx_hq = off_hkv*G + off_g[:, None, None]
+    idx_m = off_m[None, :, None]
+    idx_d = offs_vd[None, None, :]
+
+    mask = (idx_m < Q_LEN) & (idx_d < V_HEAD_DIM)
+    acc = acc.reshape(G, BLOCK_M_PER_HQ, V_HEAD_DIM_ROUNDED)
+    xindex = idx_d + 16*idx_hq + 16*idx_m + 64*idx_t + 3456*idx_z
+    tl.store(out_ptr0 + (tl.broadcast_to(idx_d + 16*idx_hq + 64*idx_t + 3456*idx_z, [GQA_SHARED_HEADS, BLOCK_M_PER_HQ, V_HEAD_DIM_ROUNDED])), acc, mask)
+
+metadata: {'signature': {'arg_Q': '*bf16', 'arg_K': '*bf16', 'arg_V': '*bf16', 'arg_M': '*fp32', 'arg_L': '*fp32', 'arg_KV_NUM_BLKS': '*i32', 'arg_KV_IDX': '*i32', 'arg_FULL_KV_NUM_BLKS': '*fp32', 'arg_FULL_KV_IDX': '*fp32', 'in_ptr9': '*i32', 'in_ptr10': '*i32', 'out_ptr0': '*fp32'}, 'device': 0, 'constants': {}, 'configs': [{(0,): [['tt.divisibility', 16]], (1,): [['tt.divisibility', 16]], (2,): [['tt.divisibility', 16]], (3,): [['tt.divisibility', 16]], (4,): [['tt.divisibility', 16]], (5,): [['tt.divisibility', 16]], (6,): [['tt.divisibility', 16]], (7,): [['tt.divisibility', 16]], (8,): [['tt.divisibility', 16]], (9,): [['tt.divisibility', 16]], (10,): [['tt.divisibility', 16]], (11,): [['tt.divisibility', 16]]}], 'device_type': 'cuda', 'num_warps': 2, 'num_stages': 1, 'debug': True, 'cc': 80}
+Traceback (most recent call last):
+  File "/home/gspark/venvs/pt213/lib/python3.10/site-packages/triton/language/core.py", line 43, in wrapper
+    return fn(*args, **kwargs)
+  File "/home/gspark/venvs/pt213/lib/python3.10/site-packages/triton/language/core.py", line 2090, in dot
+    res = _semantic.dot(input, other, acc, input_precision, max_num_imprecise_acc, out_dtype)
+  File "/home/gspark/venvs/pt213/lib/python3.10/site-packages/triton/language/semantic.py", line 1539, in dot
+    and rhs.shape[-1].value >= min_dot_size[1], \
+AssertionError: Input shapes should have M >= 1, N >= 1 and K >= 16
+
+The above exception was the direct cause of the following exception:
+
+triton.compiler.errors.CompilationError: at 146:10:
+    p = tl.math.exp2(post_mod_scores - m_ij_masked[:, None])
+
+    # NB: l_i update is pulled up here since it's a bit faster
+    # NB: For headdim=256, it's faster to move it back down to after m_i =
+    # m_ij
+    l_i = l_i * alpha + tl.sum(p, 1)
+    # # -- scale and update acc --
+    acc = acc * alpha[:, None]
+    # Calculate offsets for V loading - reuse kv_base_offset from K loading
+    offs_v = tl.arange(0, V_HEAD_DIM_ROUNDED)
+    v = load_checked_2d(V, offs_n_load, offs_v, stride_vn, stride_vk, IS_DIVISIBLE, SAFE_HEAD_DIM, KV_LEN, V_HEAD_DIM)
+    acc = tl.dot(p.to(MATMUL_PRECISION), v.to(q.dtype), acc, input_precision=FLOAT32_PRECISION)
+          ^
+Input shapes should have M >= 1, N >= 1 and K >= 16
+
+The above exception was the direct cause of the following exception:
+
+triton.compiler.errors.CompilationError: at 81:28:
+                kv_offset,
+                MATMUL_PRECISION, RCP_LN2,
+                # Strides for K and V
+                stride_kk, stride_kn, stride_vn, stride_vk,
+                IS_FULL_BLOCKS,
+            )
+        else:
+            # Benchmark shows even we applied mod & mask to each block for non divisible seqlen,
+            # it's on par or slightly faster than only applying to the last block in fwd.
+            # However, we choose different strategy for bwd, where we only apply mod & mask
+            # to the last block because it's faster a lot.
+            acc, l_i, m_i = forward_block_mn(
+                            ^
+
+The above exception was the direct cause of the following exception:
+
+Traceback (most recent call last):
+  File "/home/gspark/venvs/pt213/lib/python3.10/site-packages/torch/_inductor/runtime/triton_heuristics.py", line 1156, in _precompile_config
+    binary = triton.compile(*compile_args, **compile_kwargs)
+  File "/home/gspark/venvs/pt213/lib/python3.10/site-packages/triton/compiler/compiler.py", line 307, in compile
+    module = src.make_ir(target, options, codegen_fns, module_map, context)
+  File "/home/gspark/venvs/pt213/lib/python3.10/site-packages/triton/compiler/compiler.py", line 80, in make_ir
+    return ast_to_ttir(self.fn, self, context=context, options=options, codegen_fns=codegen_fns,
+triton.compiler.errors.CompilationError: at 184:20:
+    off_n = tl.load(kv_indices + indices_idx) * SPARSE_KV_BLOCK_SIZE + off_n_block_in_sparse * BLOCK_N
+    # first kv block we're loading
+
+    # last valid block according to sparse mask
+    block_n_last_valid = tl.minimum(kv_num_blocks * SPARSE_KV_MULTIPLE, tl.maximum(tl.cdiv(KV_LEN, BLOCK_N), 1))
+
+    offs_n = tl.arange(0, BLOCK_N) + off_n
+
+    desc_k = None
+    desc_v = None
+
+    acc, l_i, m_i = forward_inner(
+                    ^
+DEBUG    torch.__trace:utils.py:2350
+DEBUG    torch.__trace:utils.py:2350
+DEBUG    torch.__trace:utils.py:2350
+DEBUG    torch.__trace:utils.py:2350
+DEBUG    torch.__trace:utils.py:2350
+DEBUG    torch.__trace:utils.py:2350
+DEBUG    torch.__trace:utils.py:2350
+DEBUG    torch.__trace:utils.py:2350
+DEBUG    torch.__trace:utils.py:2350
+DEBUG    torch.__trace:utils.py:2350
+DEBUG    torch.__trace:convert_frame.py:2134
+DEBUG    torch.__trace:utils.py:2350
+DEBUG    torch.__trace:utils.py:2350
+DEBUG    torch.__trace:utils.py:2350
+DEBUG    torch.__trace:utils.py:2009
+DEBUG    torch.__trace:utils.py:2350
+_ test_cancellation_and_late_old_completion_do_not_corrupt_reused_cache[cuda] __
+
+device = 'cuda'
+
+    @pytest.mark.parametrize("device", ["cpu", "cuda"])
+    def test_cancellation_and_late_old_completion_do_not_corrupt_reused_cache(device):
+        model,worker,b=setup(device);p,q=_prompts(2,25,26,267)
+        old=b.submit(PDRequest('old',p,8));b.step();assert b.cancel('old');b.step()
+        with pytest.raises(RequestCancelled):old.result()
+        new=b.submit(PDRequest('new',q,8));b.step()
+>       worker.finish(0);worker.finish(1);b.run_until_idle()
+
+tests/test_pd_stream_admission.py:74:
+_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
+opt_prime/accel/pd/batching.py:207: in run_until_idle
+    self.step()
+opt_prime/accel/pd/pd_batching.py:594: in step
+    self._decode()
+../../../venvs/pt213/lib/python3.10/site-packages/torch/utils/_contextlib.py:124: in decorate_context
+    return func(*args, **kwargs)
+opt_prime/accel/pd/pd_batching.py:671: in _decode
+    self._decode_step()
+opt_prime/accel/pd/batching.py:232: in _decode_step
+    logits = self.kv.decode(self.model, slots, lengths, [r.tokens[-1] for r in rows])
+opt_prime/accel/pd/paged.py:565: in decode
+    out = model(ids, past_key_values=cache, use_cache=True, position_ids=pos, logits_to_keep=1)
+../../../venvs/pt213/lib/python3.10/site-packages/torch/nn/modules/module.py:1778: in _wrapped_call_impl
+    return self._call_impl(*args, **kwargs)
+../../../venvs/pt213/lib/python3.10/site-packages/torch/nn/modules/module.py:1789: in _call_impl
+    return forward_call(*args, **kwargs)
+../../../venvs/pt213/lib/python3.10/site-packages/transformers/utils/generic.py:939: in wrapper
+    output = func(self, *args, **kwargs)
+../../../venvs/pt213/lib/python3.10/site-packages/transformers/models/gemma3/modeling_gemma3.py:630: in forward
+    outputs: BaseModelOutputWithPast = self.model(
+../../../venvs/pt213/lib/python3.10/site-packages/torch/nn/modules/module.py:1778: in _wrapped_call_impl
+    return self._call_impl(*args, **kwargs)
+../../../venvs/pt213/lib/python3.10/site-packages/torch/nn/modules/module.py:1789: in _call_impl
+    return forward_call(*args, **kwargs)
+../../../venvs/pt213/lib/python3.10/site-packages/transformers/utils/generic.py:1068: in wrapper
+    output = func(self, *args, **kwargs)
+../../../venvs/pt213/lib/python3.10/site-packages/transformers/utils/output_capturing.py:262: in wrapper
+    outputs = func(self, *args, **kwargs)
+../../../venvs/pt213/lib/python3.10/site-packages/transformers/models/gemma3/modeling_gemma3.py:564: in forward
+    hidden_states = decoder_layer(
+../../../venvs/pt213/lib/python3.10/site-packages/transformers/modeling_layers.py:114: in __call__
+    return super().__call__(*args, **kwargs)
+../../../venvs/pt213/lib/python3.10/site-packages/torch/nn/modules/module.py:1778: in _wrapped_call_impl
+    return self._call_impl(*args, **kwargs)
+../../../venvs/pt213/lib/python3.10/site-packages/torch/nn/modules/module.py:1789: in _call_impl
+    return forward_call(*args, **kwargs)
+../../../venvs/pt213/lib/python3.10/site-packages/transformers/models/gemma3/modeling_gemma3.py:412: in forward
+    hidden_states, _ = self.self_attn(
+../../../venvs/pt213/lib/python3.10/site-packages/torch/nn/modules/module.py:1778: in _wrapped_call_impl
+    return self._call_impl(*args, **kwargs)
+../../../venvs/pt213/lib/python3.10/site-packages/torch/nn/modules/module.py:1789: in _call_impl
+    return forward_call(*args, **kwargs)
+../../../venvs/pt213/lib/python3.10/site-packages/transformers/models/gemma3/modeling_gemma3.py:369: in forward
+    attn_output, attn_weights = attention_interface(
+opt_prime/accel/pd/paged.py:50: in _paged_attention
+    out = _flex(query.device.type)(query, key, value, block_mask=_CTX.masks[group], scale=scaling,
+../../../venvs/pt213/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py:1183: in compile_wrapper
+    raise e.remove_dynamo_frames() from None  # see TORCHDYNAMO_VERBOSE=1
+../../../venvs/pt213/lib/python3.10/site-packages/torch/_inductor/compile_fx.py:1079: in _compile_fx_inner
+    raise InductorError(e, currentframe()).with_traceback(
+../../../venvs/pt213/lib/python3.10/site-packages/torch/_inductor/compile_fx.py:1059: in _compile_fx_inner
+    mb_compiled_graph = fx_codegen_and_compile(
+../../../venvs/pt213/lib/python3.10/site-packages/torch/_inductor/compile_fx.py:1847: in fx_codegen_and_compile
+    return scheme.codegen_and_compile(gm, example_inputs, inputs_to_check, graph_kwargs)
+../../../venvs/pt213/lib/python3.10/site-packages/torch/_inductor/compile_fx.py:1608: in codegen_and_compile
+    compiled_module = graph.compile_to_module()
+../../../venvs/pt213/lib/python3.10/site-packages/torch/_inductor/graph.py:2669: in compile_to_module
+    return self._compile_to_module()
+../../../venvs/pt213/lib/python3.10/site-packages/torch/_inductor/graph.py:2679: in _compile_to_module
+    mod = self._compile_to_module_lines(wrapper_code)
+../../../venvs/pt213/lib/python3.10/site-packages/torch/_inductor/graph.py:2754: in _compile_to_module_lines
+    mod = PyCodeCache.load_by_key_path(
+../../../venvs/pt213/lib/python3.10/site-packages/torch/_inductor/codecache.py:4622: in load_by_key_path
+    mod = _reload_python_module(key, path, set_sys_modules=set_sys_modules)
+../../../venvs/pt213/lib/python3.10/site-packages/torch/_inductor/runtime/compile_tasks.py:35: in _reload_python_module
+    exec(code, mod.__dict__, mod.__dict__)
+/tmp/torchinductor_gspark/eb/cebeydezvht2l4u27hluxa3eggoewztq5rkmrcd3dfdgte2khymn.py:803: in <module>
+    async_compile.wait(globals())
+../../../venvs/pt213/lib/python3.10/site-packages/torch/_inductor/async_compile.py:818: in wait
+    self._wait_futures(scope)
+../../../venvs/pt213/lib/python3.10/site-packages/torch/_inductor/async_compile.py:848: in _wait_futures
+    kernel = result.result(timeout=wait_timeout)
+../../../venvs/pt213/lib/python3.10/site-packages/torch/_inductor/codecache.py:5306: in result
+    return self.result_fn()
+_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
+
+    def get_result() -> CachingAutotuner:
+        try:
+            kernel, elapsed_us = task.result()
+        except SubprocException as e:
+>           raise e.with_name(kernel_name) from e
+E           torch._inductor.exc.InductorError: SubprocException: An exception occurred in a subprocess:
+E
+E           Name=triton_tem_fused_flex_attention_0
+E           Traceback (most recent call last):
+E             File "/home/gspark/venvs/pt213/lib/python3.10/site-packages/triton/language/core.py", line 43, in wrapper
+E               return fn(*args, **kwargs)
+E             File "/home/gspark/venvs/pt213/lib/python3.10/site-packages/triton/language/core.py", line 2090, in dot
+E               res = _semantic.dot(input, other, acc, input_precision, max_num_imprecise_acc, out_dtype)
+E             File "/home/gspark/venvs/pt213/lib/python3.10/site-packages/triton/language/semantic.py", line 1539, in dot
+E               and rhs.shape[-1].value >= min_dot_size[1], \
+E           AssertionError: Input shapes should have M >= 1, N >= 1 and K >= 16
+E
+E           The above exception was the direct cause of the following exception:
+E
+E           triton.compiler.errors.CompilationError: at 146:10:
+E               p = tl.math.exp2(post_mod_scores - m_ij_masked[:, None])
+E
+E               # NB: l_i update is pulled up here since it's a bit faster
+E               # NB: For headdim=256, it's faster to move it back down to after m_i =
+E               # m_ij
+E               l_i = l_i * alpha + tl.sum(p, 1)
+E               # # -- scale and update acc --
+E               acc = acc * alpha[:, None]
+E               # Calculate offsets for V loading - reuse kv_base_offset from K loading
+E               offs_v = tl.arange(0, V_HEAD_DIM_ROUNDED)
+E               v = load_checked_2d(V, offs_n_load, offs_v, stride_vn, stride_vk, IS_DIVISIBLE, SAFE_HEAD_DIM, KV_LEN, V_HEAD_DIM)
+E               acc = tl.dot(p.to(MATMUL_PRECISION), v.to(q.dtype), acc, input_precision=FLOAT32_PRECISION)
+E                     ^
+E           Input shapes should have M >= 1, N >= 1 and K >= 16
+E
+E           The above exception was the direct cause of the following exception:
+E
+E           triton.compiler.errors.CompilationError: at 81:28:
+E                           kv_offset,
+E                           MATMUL_PRECISION, RCP_LN2,
+E                           # Strides for K and V
+E                           stride_kk, stride_kn, stride_vn, stride_vk,
+E                           IS_FULL_BLOCKS,
+E                       )
+E                   else:
+E                       # Benchmark shows even we applied mod & mask to each block for non divisible seqlen,
+E                       # it's on par or slightly faster than only applying to the last block in fwd.
+E                       # However, we choose different strategy for bwd, where we only apply mod & mask
+E                       # to the last block because it's faster a lot.
+E                       acc, l_i, m_i = forward_block_mn(
+E                                       ^
+E
+E           The above exception was the direct cause of the following exception:
+E
+E           Traceback (most recent call last):
+E             File "/home/gspark/venvs/pt213/lib/python3.10/site-packages/torch/_inductor/compile_worker/subproc_pool.py", line 475, in do_job
+E               result = job()
+E             File "/home/gspark/venvs/pt213/lib/python3.10/site-packages/torch/_inductor/runtime/compile_tasks.py", line 204, in _worker_compile_triton
+E               kernel.precompile(warm_cache_only=True)
+E             File "/home/gspark/venvs/pt213/lib/python3.10/site-packages/torch/_inductor/runtime/triton_heuristics.py", line 648, in precompile
+E               self._precompile_worker()
+E             File "/home/gspark/venvs/pt213/lib/python3.10/site-packages/torch/_inductor/runtime/triton_heuristics.py", line 687, in _precompile_worker
+E               compile_results.append(self._precompile_config(c))
+E             File "/home/gspark/venvs/pt213/lib/python3.10/site-packages/torch/_inductor/runtime/triton_heuristics.py", line 1156, in _precompile_config
+E               binary = triton.compile(*compile_args, **compile_kwargs)
+E             File "/home/gspark/venvs/pt213/lib/python3.10/site-packages/triton/compiler/compiler.py", line 307, in compile
+E               module = src.make_ir(target, options, codegen_fns, module_map, context)
+E             File "/home/gspark/venvs/pt213/lib/python3.10/site-packages/triton/compiler/compiler.py", line 80, in make_ir
+E               return ast_to_ttir(self.fn, self, context=context, options=options, codegen_fns=codegen_fns,
+E           triton.compiler.errors.CompilationError: at 184:20:
+E               off_n = tl.load(kv_indices + indices_idx) * SPARSE_KV_BLOCK_SIZE + off_n_block_in_sparse * BLOCK_N
+E               # first kv block we're loading
+E
+E               # last valid block according to sparse mask
+E               block_n_last_valid = tl.minimum(kv_num_blocks * SPARSE_KV_MULTIPLE, tl.maximum(tl.cdiv(KV_LEN, BLOCK_N), 1))
+E
+E               offs_n = tl.arange(0, BLOCK_N) + off_n
+E
+E               desc_k = None
+E               desc_v = None
+E
+E               acc, l_i, m_i = forward_inner(
+E                               ^
+E
+E
+E           Set TORCHDYNAMO_VERBOSE=1 for the internal stack trace (please do this especially if you're reporting a bug to PyTorch). For even more developer context, set TORCH_LOGS="+dynamo"
+
+../../../venvs/pt213/lib/python3.10/site-packages/torch/_inductor/async_compile.py:500: InductorError
+------------------------------ Captured log call -------------------------------
+DEBUG    torch.__trace:utils.py:2350
+DEBUG    torch.__trace:utils.py:2350
+DEBUG    torch.__trace:convert_frame.py:303
+DEBUG    torch.__trace:utils.py:2350
+DEBUG    torch.__trace:utils.py:2350
+DEBUG    torch.__trace:meta_utils.py:276
+DEBUG    torch.__trace:meta_utils.py:514
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+DEBUG    torch.__trace:meta_utils.py:276
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+DEBUG    torch.__trace:meta_utils.py:514
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+DEBUG    torch.__trace:meta_utils.py:514
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+DEBUG    torch.__trace:utils.py:2350
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+DEBUG    torch.__trace:output_graph.py:2841
+DEBUG    torch.__trace:utils.py:2350
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+DEBUG    torch.__trace:autograd_cache.py:1130
+DEBUG    torch.__trace:compile_fx.py:2625
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+DEBUG    torch.__trace:utils.py:2350
+DEBUG    torch.__trace:utils.py:2350
+DEBUG    torch.__trace:utils.py:2350
+DEBUG    torch.__trace:graph_capture.py:426
+DEBUG    torch.__trace:graph_capture.py:444
+DEBUG    torch.__trace:graph_compile.py:381
+DEBUG    torch.__trace:utils.py:2350
+DEBUG    torch.__trace:compile_fx.py:2448
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+______ test_partial_producer_failure_preserves_other_active_decode[cuda] _______
+
+device = 'cuda'
+
+    @pytest.mark.parametrize("device", ["cpu", "cuda"])
+    def test_partial_producer_failure_preserves_other_active_decode(device):
+        model,worker,b=setup(device);p,q=_prompts(2,25,26,269)
+>       kept=b.submit(PDRequest('kept',p,8));b.step();worker.finish(0);b.step()
+
+tests/test_pd_stream_admission.py:82:
+_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
+opt_prime/accel/pd/pd_batching.py:594: in step
+    self._decode()
+../../../venvs/pt213/lib/python3.10/site-packages/torch/utils/_contextlib.py:124: in decorate_context
+    return func(*args, **kwargs)
+opt_prime/accel/pd/pd_batching.py:671: in _decode
+    self._decode_step()
+opt_prime/accel/pd/batching.py:232: in _decode_step
+    logits = self.kv.decode(self.model, slots, lengths, [r.tokens[-1] for r in rows])
+opt_prime/accel/pd/paged.py:565: in decode
+    out = model(ids, past_key_values=cache, use_cache=True, position_ids=pos, logits_to_keep=1)
+../../../venvs/pt213/lib/python3.10/site-packages/torch/nn/modules/module.py:1778: in _wrapped_call_impl
+    return self._call_impl(*args, **kwargs)
+../../../venvs/pt213/lib/python3.10/site-packages/torch/nn/modules/module.py:1789: in _call_impl
+    return forward_call(*args, **kwargs)
+../../../venvs/pt213/lib/python3.10/site-packages/transformers/utils/generic.py:939: in wrapper
+    output = func(self, *args, **kwargs)
+../../../venvs/pt213/lib/python3.10/site-packages/transformers/models/gemma3/modeling_gemma3.py:630: in forward
+    outputs: BaseModelOutputWithPast = self.model(
+../../../venvs/pt213/lib/python3.10/site-packages/torch/nn/modules/module.py:1778: in _wrapped_call_impl
+    return self._call_impl(*args, **kwargs)
+../../../venvs/pt213/lib/python3.10/site-packages/torch/nn/modules/module.py:1789: in _call_impl
+    return forward_call(*args, **kwargs)
+../../../venvs/pt213/lib/python3.10/site-packages/transformers/utils/generic.py:1068: in wrapper
+    output = func(self, *args, **kwargs)
+../../../venvs/pt213/lib/python3.10/site-packages/transformers/utils/output_capturing.py:262: in wrapper
+    outputs = func(self, *args, **kwargs)
+../../../venvs/pt213/lib/python3.10/site-packages/transformers/models/gemma3/modeling_gemma3.py:564: in forward
+    hidden_states = decoder_layer(
+../../../venvs/pt213/lib/python3.10/site-packages/transformers/modeling_layers.py:114: in __call__
+    return super().__call__(*args, **kwargs)
+../../../venvs/pt213/lib/python3.10/site-packages/torch/nn/modules/module.py:1778: in _wrapped_call_impl
+    return self._call_impl(*args, **kwargs)
+../../../venvs/pt213/lib/python3.10/site-packages/torch/nn/modules/module.py:1789: in _call_impl
+    return forward_call(*args, **kwargs)
+../../../venvs/pt213/lib/python3.10/site-packages/transformers/models/gemma3/modeling_gemma3.py:412: in forward
+    hidden_states, _ = self.self_attn(
+../../../venvs/pt213/lib/python3.10/site-packages/torch/nn/modules/module.py:1778: in _wrapped_call_impl
+    return self._call_impl(*args, **kwargs)
+../../../venvs/pt213/lib/python3.10/site-packages/torch/nn/modules/module.py:1789: in _call_impl
+    return forward_call(*args, **kwargs)
+../../../venvs/pt213/lib/python3.10/site-packages/transformers/models/gemma3/modeling_gemma3.py:369: in forward
+    attn_output, attn_weights = attention_interface(
+opt_prime/accel/pd/paged.py:50: in _paged_attention
+    out = _flex(query.device.type)(query, key, value, block_mask=_CTX.masks[group], scale=scaling,
+../../../venvs/pt213/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py:1183: in compile_wrapper
+    raise e.remove_dynamo_frames() from None  # see TORCHDYNAMO_VERBOSE=1
+../../../venvs/pt213/lib/python3.10/site-packages/torch/_inductor/compile_fx.py:1079: in _compile_fx_inner
+    raise InductorError(e, currentframe()).with_traceback(
+../../../venvs/pt213/lib/python3.10/site-packages/torch/_inductor/compile_fx.py:1059: in _compile_fx_inner
+    mb_compiled_graph = fx_codegen_and_compile(
+../../../venvs/pt213/lib/python3.10/site-packages/torch/_inductor/compile_fx.py:1847: in fx_codegen_and_compile
+    return scheme.codegen_and_compile(gm, example_inputs, inputs_to_check, graph_kwargs)
+../../../venvs/pt213/lib/python3.10/site-packages/torch/_inductor/compile_fx.py:1608: in codegen_and_compile
+    compiled_module = graph.compile_to_module()
+../../../venvs/pt213/lib/python3.10/site-packages/torch/_inductor/graph.py:2669: in compile_to_module
+    return self._compile_to_module()
+../../../venvs/pt213/lib/python3.10/site-packages/torch/_inductor/graph.py:2679: in _compile_to_module
+    mod = self._compile_to_module_lines(wrapper_code)
+../../../venvs/pt213/lib/python3.10/site-packages/torch/_inductor/graph.py:2754: in _compile_to_module_lines
+    mod = PyCodeCache.load_by_key_path(
+../../../venvs/pt213/lib/python3.10/site-packages/torch/_inductor/codecache.py:4622: in load_by_key_path
+    mod = _reload_python_module(key, path, set_sys_modules=set_sys_modules)
+../../../venvs/pt213/lib/python3.10/site-packages/torch/_inductor/runtime/compile_tasks.py:35: in _reload_python_module
+    exec(code, mod.__dict__, mod.__dict__)
+/tmp/torchinductor_gspark/ky/ckyvsiar3aef3no5clnlxbvxe2kcrmmotqxbcaif2kexaazdfs7v.py:803: in <module>
+    async_compile.wait(globals())
+../../../venvs/pt213/lib/python3.10/site-packages/torch/_inductor/async_compile.py:818: in wait
+    self._wait_futures(scope)
+../../../venvs/pt213/lib/python3.10/site-packages/torch/_inductor/async_compile.py:848: in _wait_futures
+    kernel = result.result(timeout=wait_timeout)
+../../../venvs/pt213/lib/python3.10/site-packages/torch/_inductor/codecache.py:5306: in result
+    return self.result_fn()
+_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
+
+    def get_result() -> CachingAutotuner:
+        try:
+            kernel, elapsed_us = task.result()
+        except SubprocException as e:
+>           raise e.with_name(kernel_name) from e
+E           torch._inductor.exc.InductorError: SubprocException: An exception occurred in a subprocess:
+E
+E           Name=triton_tem_fused_flex_attention_0
+E           Traceback (most recent call last):
+E             File "/home/gspark/venvs/pt213/lib/python3.10/site-packages/triton/language/core.py", line 43, in wrapper
+E               return fn(*args, **kwargs)
+E             File "/home/gspark/venvs/pt213/lib/python3.10/site-packages/triton/language/core.py", line 2090, in dot
+E               res = _semantic.dot(input, other, acc, input_precision, max_num_imprecise_acc, out_dtype)
+E             File "/home/gspark/venvs/pt213/lib/python3.10/site-packages/triton/language/semantic.py", line 1539, in dot
+E               and rhs.shape[-1].value >= min_dot_size[1], \
+E           AssertionError: Input shapes should have M >= 1, N >= 1 and K >= 16
+E
+E           The above exception was the direct cause of the following exception:
+E
+E           triton.compiler.errors.CompilationError: at 146:10:
+E               p = tl.math.exp2(post_mod_scores - m_ij_masked[:, None])
+E
+E               # NB: l_i update is pulled up here since it's a bit faster
+E               # NB: For headdim=256, it's faster to move it back down to after m_i =
+E               # m_ij
+E               l_i = l_i * alpha + tl.sum(p, 1)
+E               # # -- scale and update acc --
+E               acc = acc * alpha[:, None]
+E               # Calculate offsets for V loading - reuse kv_base_offset from K loading
+E               offs_v = tl.arange(0, V_HEAD_DIM_ROUNDED)
+E               v = load_checked_2d(V, offs_n_load, offs_v, stride_vn, stride_vk, IS_DIVISIBLE, SAFE_HEAD_DIM, KV_LEN, V_HEAD_DIM)
+E               acc = tl.dot(p.to(MATMUL_PRECISION), v.to(q.dtype), acc, input_precision=FLOAT32_PRECISION)
+E                     ^
+E           Input shapes should have M >= 1, N >= 1 and K >= 16
+E
+E           The above exception was the direct cause of the following exception:
+E
+E           triton.compiler.errors.CompilationError: at 81:28:
+E                           kv_offset,
+E                           MATMUL_PRECISION, RCP_LN2,
+E                           # Strides for K and V
+E                           stride_kk, stride_kn, stride_vn, stride_vk,
+E                           IS_FULL_BLOCKS,
+E                       )
+E                   else:
+E                       # Benchmark shows even we applied mod & mask to each block for non divisible seqlen,
+E                       # it's on par or slightly faster than only applying to the last block in fwd.
+E                       # However, we choose different strategy for bwd, where we only apply mod & mask
+E                       # to the last block because it's faster a lot.
+E                       acc, l_i, m_i = forward_block_mn(
+E                                       ^
+E
+E           The above exception was the direct cause of the following exception:
+E
+E           Traceback (most recent call last):
+E             File "/home/gspark/venvs/pt213/lib/python3.10/site-packages/torch/_inductor/compile_worker/subproc_pool.py", line 475, in do_job
+E               result = job()
+E             File "/home/gspark/venvs/pt213/lib/python3.10/site-packages/torch/_inductor/runtime/compile_tasks.py", line 204, in _worker_compile_triton
+E               kernel.precompile(warm_cache_only=True)
+E             File "/home/gspark/venvs/pt213/lib/python3.10/site-packages/torch/_inductor/runtime/triton_heuristics.py", line 648, in precompile
+E               self._precompile_worker()
+E             File "/home/gspark/venvs/pt213/lib/python3.10/site-packages/torch/_inductor/runtime/triton_heuristics.py", line 687, in _precompile_worker
+E               compile_results.append(self._precompile_config(c))
+E             File "/home/gspark/venvs/pt213/lib/python3.10/site-packages/torch/_inductor/runtime/triton_heuristics.py", line 1156, in _precompile_config
+E               binary = triton.compile(*compile_args, **compile_kwargs)
+E             File "/home/gspark/venvs/pt213/lib/python3.10/site-packages/triton/compiler/compiler.py", line 307, in compile
+E               module = src.make_ir(target, options, codegen_fns, module_map, context)
+E             File "/home/gspark/venvs/pt213/lib/python3.10/site-packages/triton/compiler/compiler.py", line 80, in make_ir
+E               return ast_to_ttir(self.fn, self, context=context, options=options, codegen_fns=codegen_fns,
+E           triton.compiler.errors.CompilationError: at 184:20:
+E               off_n = tl.load(kv_indices + indices_idx) * SPARSE_KV_BLOCK_SIZE + off_n_block_in_sparse * BLOCK_N
+E               # first kv block we're loading
+E
+E               # last valid block according to sparse mask
+E               block_n_last_valid = tl.minimum(kv_num_blocks * SPARSE_KV_MULTIPLE, tl.maximum(tl.cdiv(KV_LEN, BLOCK_N), 1))
+E
+E               offs_n = tl.arange(0, BLOCK_N) + off_n
+E
+E               desc_k = None
+E               desc_v = None
+E
+E               acc, l_i, m_i = forward_inner(
+E                               ^
+E
+E
+E           Set TORCHDYNAMO_VERBOSE=1 for the internal stack trace (please do this especially if you're reporting a bug to PyTorch). For even more developer context, set TORCH_LOGS="+dynamo"
+
+../../../venvs/pt213/lib/python3.10/site-packages/torch/_inductor/async_compile.py:500: InductorError
+------------------------------ Captured log call -------------------------------
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+=============================== warnings summary ===============================
+tests/test_pd_stream_admission.py::test_hybrid_partial_import_happens_before_completion_without_admission[cpu]
+  /home/gspark/venvs/pt213/lib/python3.10/site-packages/torch/nn/attention/flex_attention.py:2481: UserWarning: flex_attention called without torch.compile() - this will use an unfused implementation that materializes the full scores matrix instead of generating a fused kernel.
+
+  SOLUTION: Use torch.compile(flex_attention)(...)
+
+  If you want to debug your score_mod/mask_mod, you can set:
+  torch.nn.attention.flex_attention._FLEX_ATTENTION_DISABLE_COMPILE_DEBUG = True
+
+  This will allow you to use print statements or breakpoints. Note: This doesn't work with the backwards pass and may produce incorrect results.
+    _warn_once(
+
+tests/test_pd_stream_admission.py: 14 warnings
+  /home/gspark/venvs/pt213/lib/python3.10/site-packages/torch/jit/_script.py:365: DeprecationWarning: `torch.jit.script_method` is deprecated. Please switch to `torch.compile` or `torch.export`.
+    warnings.warn(
+
+-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html
+=========================== short test summary info ============================
+FAILED tests/test_pd_stream_admission.py::test_hybrid_partial_import_happens_before_completion_without_admission[cuda]
+FAILED tests/test_pd_stream_admission.py::test_cancellation_and_late_old_completion_do_not_corrupt_reused_cache[cuda]
+FAILED tests/test_pd_stream_admission.py::test_partial_producer_failure_preserves_other_active_decode[cuda]
+3 failed, 5 passed, 15 warnings in 11.22s
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-owner-focused.log b/opt_prime/examples/pd/http_matrix/evidence265/stream265-owner-focused.log
new file mode 100644
index 00000000..5c21679a
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-owner-focused.log
@@ -0,0 +1,15 @@
+...................................................                      [100%]
+=============================== warnings summary ===============================
+tests/test_pd_stream_admission.py::test_hybrid_partial_import_happens_before_completion_without_admission
+  /home/gspark/venvs/pt213/lib/python3.10/site-packages/torch/nn/attention/flex_attention.py:2481: UserWarning: flex_attention called without torch.compile() - this will use an unfused implementation that materializes the full scores matrix instead of generating a fused kernel.
+
+  SOLUTION: Use torch.compile(flex_attention)(...)
+
+  If you want to debug your score_mod/mask_mod, you can set:
+  torch.nn.attention.flex_attention._FLEX_ATTENTION_DISABLE_COMPILE_DEBUG = True
+
+  This will allow you to use print statements or breakpoints. Note: This doesn't work with the backwards pass and may produce incorrect results.
+    _warn_once(
+
+-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html
+51 passed, 1 warning in 15.49s
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-owner-red.log b/opt_prime/examples/pd/http_matrix/evidence265/stream265-owner-red.log
new file mode 100644
index 00000000..9cd57bb7
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-owner-red.log
@@ -0,0 +1,44 @@
+F..F                                                                     [100%]
+=================================== FAILURES ===================================
+____ test_hybrid_partial_import_happens_before_completion_without_admission ____
+
+    def test_hybrid_partial_import_happens_before_completion_without_admission():
+        model,worker,b=setup();prompt=_prompts(1,25,26,265)[0]
+        result=b.submit(PDRequest('stream',prompt,8));b.step()
+        request=b.by_id['stream'];idx=b.kv.span_index(request.slot,0,len(prompt))
+        keys,_=b.kv.layer_kv(0)
+>       torch.testing.assert_close(keys[:,idx],worker.jobs[0][2].keys[0])
+E       AssertionError: Tensor-likes are not close!
+E
+E       Mismatched elements: 800 / 800 (100.0%)
+E       Greatest absolute difference: 2.84375 at index (1, 4, 1) (up to 1e-05 allowed)
+E       Greatest relative difference: 1.0 at index (0, 0, 0) (up to 0.016 allowed)
+
+tests/test_pd_stream_admission.py:59: AssertionError
+___________ test_unvalidated_stream_completion_cannot_publish_cache ____________
+
+    def test_unvalidated_stream_completion_cannot_publish_cache():
+        _,worker,b=setup();prompt=_prompts(1,25,26,271)[0]
+        result=b.submit(PDRequest('incomplete',prompt,8));b.step();worker.finish(complete=False)
+        b.run_until_idle()
+>       with pytest.raises(RuntimeError,match='coverage'):result.result()
+E       Failed: DID NOT RAISE RuntimeError
+
+tests/test_pd_stream_admission.py:91: Failed
+=============================== warnings summary ===============================
+tests/test_pd_stream_admission.py::test_cancellation_and_late_old_completion_do_not_corrupt_reused_cache
+  /home/gspark/venvs/pt213/lib/python3.10/site-packages/torch/nn/attention/flex_attention.py:2481: UserWarning: flex_attention called without torch.compile() - this will use an unfused implementation that materializes the full scores matrix instead of generating a fused kernel.
+
+  SOLUTION: Use torch.compile(flex_attention)(...)
+
+  If you want to debug your score_mod/mask_mod, you can set:
+  torch.nn.attention.flex_attention._FLEX_ATTENTION_DISABLE_COMPILE_DEBUG = True
+
+  This will allow you to use print statements or breakpoints. Note: This doesn't work with the backwards pass and may produce incorrect results.
+    _warn_once(
+
+-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html
+=========================== short test summary info ============================
+FAILED tests/test_pd_stream_admission.py::test_hybrid_partial_import_happens_before_completion_without_admission
+FAILED tests/test_pd_stream_admission.py::test_unvalidated_stream_completion_cannot_publish_cache
+2 failed, 2 passed, 1 warning in 5.83s
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-prefetch-fault-final.log b/opt_prime/examples/pd/http_matrix/evidence265/stream265-prefetch-fault-final.log
new file mode 100644
index 00000000..45428a0b
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-prefetch-fault-final.log
@@ -0,0 +1,2 @@
+.......................................                                  [100%]
+39 passed in 5.00s
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-prefix-red.log b/opt_prime/examples/pd/http_matrix/evidence265/stream265-prefix-red.log
new file mode 100644
index 00000000..f7c74194
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-prefix-red.log
@@ -0,0 +1,17 @@
+.................F                                                       [100%]
+=================================== FAILURES ===================================
+____________ test_partial_write_cannot_overwrite_an_admitted_prefix ____________
+
+    def test_partial_write_cannot_overwrite_an_admitted_prefix():
+        import torch
+        from test_pd_dense_hybrid import pool
+        cache=pool()
+        cache.length[0]=4
+        keys=torch.ones(1,1,5,2)
+>       with pytest.raises(ValueError,match='prefix'):cache.write_layer_span(0,3,0,keys,keys)
+E       Failed: DID NOT RAISE ValueError
+
+tests/test_pd_layer_progress.py:100: Failed
+=========================== short test summary info ============================
+FAILED tests/test_pd_layer_progress.py::test_partial_write_cannot_overwrite_an_admitted_prefix
+1 failed, 17 passed in 2.54s
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-python.sh b/opt_prime/examples/pd/http_matrix/evidence265/stream265-python.sh
new file mode 100755
index 00000000..59a83ed4
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-python.sh
@@ -0,0 +1,11 @@
+#!/bin/bash
+set -eu
+exec env TORCH_DEVICE_BACKEND_AUTOLOAD=0 /tmp/codex-rngd-gpu-201/bin/python -c '
+import runpy,sys
+from furiosa.torch._register import _register
+_register()
+import rngd_pd.engines.fsdk as sdk
+sdk.open_device=lambda *a,**k:(sdk.set_fusion(4),sdk.set_mesh_kind(sdk.MeshKind.Mesh(2)))
+sys.argv=["prefill_server"]+sys.argv[3:]
+runpy.run_module("opt_prime.accel.pd.prefill_server",run_name="__main__")
+' "$@"
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen06b-atom.json b/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen06b-atom.json
new file mode 100644
index 00000000..aabf0b1b
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen06b-atom.json
@@ -0,0 +1,270 @@
+{
+  "model": "Qwen/Qwen3-0.6B",
+  "width": 512,
+  "output_tokens": 8,
+  "batch_size": 1,
+  "cases": [
+    {
+      "stream": false,
+      "prefetch": false,
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+        ]
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+      "progress_spans": 56,
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+        {
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+}
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen06b-atom.log b/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen06b-atom.log
new file mode 100644
index 00000000..4510df9a
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen06b-atom.log
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diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen06b-c16-gpu.json b/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen06b-c16-gpu.json
new file mode 100644
index 00000000..becc3750
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen06b-c16-gpu.json
@@ -0,0 +1,47084 @@
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diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen06b-c16-gpu.log b/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen06b-c16-gpu.log
new file mode 100644
index 00000000..df1ea2b0
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen06b-c16-gpu.log
@@ -0,0 +1,10 @@
+GPU loading qwen06b
+
Loading weights:   0%|          | 0/311 [00:00<?, ?it/s]
Loading weights: 100%|██████████| 311/311 [00:00<00:00, 9506.73it/s]
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diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen06b-c4-gpu.json b/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen06b-c4-gpu.json
new file mode 100644
index 00000000..1e058ae5
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen06b-c4-gpu.json
@@ -0,0 +1,11238 @@
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diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen06b-c4-gpu.log b/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen06b-c4-gpu.log
new file mode 100644
index 00000000..cd647c63
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen06b-c4-gpu.log
@@ -0,0 +1,12 @@
+GPU loading qwen06b
+
+Loading weights:   0%|          | 0/311 [00:00<?, ?it/s]
+Loading weights: 100%|██████████| 311/311 [00:00<00:00, 7484.71it/s]
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diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen06b-control-gpu.json b/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen06b-control-gpu.json
new file mode 100644
index 00000000..9e1eba71
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen06b-control-gpu.json
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diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen06b-control-gpu.log b/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen06b-control-gpu.log
new file mode 100644
index 00000000..2ae7a520
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen06b-control-gpu.log
@@ -0,0 +1,16 @@
+GPU loading qwen06b
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+Loading weights: 100%|██████████| 311/311 [00:00<00:00, 8867.87it/s]
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diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen06b-fault-atom.json b/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen06b-fault-atom.json
new file mode 100644
index 00000000..b699ef49
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen06b-fault-atom.json
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+        315,
+        4882,
+        11862,
+        13,
+        4504,
+        825,
+        1378
+      ],
+      "tokens_exact": true,
+      "error": "RuntimeError('prefill subprocess response failed: prefill subprocess exited')",
+      "idle": true,
+      "poisoned": false
+    }
+  ],
+  "scope": "actual partial-import cancellation, lane reuse and producer death; not performance",
+  "code_sha256": {
+    "accel/platforms/atom/batching.py": "d98a51d75a36cd8c379a6220adb92528b3607cf80145b85a032270494a429580",
+    "accel/pd/pd_batching.py": "c827775c1d399bf724a9747c3424ee5ea69f93054493bfbb71d5f0cf08fff65b",
+    "accel/pd/prefill_server.py": "5af1169ff5c6bfac6ce265ea3edf7f6a031c207bda7ce7bcbc996d7133576b1d",
+    "accel/pd/layer_progress.py": "19c8853cd20b386dc11064d4be63fa696468c86a26b76ecf5c48cdf3905a700b"
+  },
+  "stage": "complete"
+}
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen06b-fault-atom.log b/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen06b-fault-atom.log
new file mode 100644
index 00000000..328c5c6e
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen06b-fault-atom.log
@@ -0,0 +1 @@
+ATOM_BINDING {"visible_count": 1, "physical": 3, "opened": ["/dev/rbln3"]}
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen06b-fault-gpu-v2.json b/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen06b-fault-gpu-v2.json
new file mode 100644
index 00000000..e0bd1b35
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen06b-fault-gpu-v2.json
@@ -0,0 +1,43 @@
+{
+  "model": "qwen06b",
+  "width": 8192,
+  "input_lengths": [
+    2048,
+    8192
+  ],
+  "output_tokens": 512,
+  "concurrency": 2,
+  "runs": [
+    {
+      "case": "cancel-reuse",
+      "trigger": [
+        {
+          "spans": 1,
+          "admitted": false
+        }
+      ],
+      "tokens_exact": true,
+      "idle": true,
+      "prefix_cache": false
+    },
+    {
+      "case": "producer-death",
+      "trigger": [
+        {
+          "spans": 29,
+          "admitted": false,
+          "active_tokens": 5
+        }
+      ],
+      "error": "RuntimeError('prefill subprocess response failed: prefill subprocess exited')",
+      "idle": true,
+      "prefix_cache": false
+    }
+  ],
+  "scope": "actual streaming cancellation/reuse and source death with active512-token decode; same-route parity, not performance",
+  "source_sha256": {
+    "prefill_server.py": "5af1169ff5c6bfac6ce265ea3edf7f6a031c207bda7ce7bcbc996d7133576b1d",
+    "streaming_factories.py": "77745350973cc54bf508e0c8b473efc442afef20b1c64f7c4edc179ea1471389"
+  },
+  "stage": "complete"
+}
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen06b-fault-gpu-v2.log b/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen06b-fault-gpu-v2.log
new file mode 100644
index 00000000..1037c7d5
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen06b-fault-gpu-v2.log
@@ -0,0 +1,4 @@
+GPU loading qwen06b
+
+Loading weights:   0%|          | 0/311 [00:00<?, ?it/s]
+Loading weights: 100%|██████████| 311/311 [00:00<00:00, 8403.25it/s]
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen06b-fault-gpu.json b/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen06b-fault-gpu.json
new file mode 100644
index 00000000..df30bd8d
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen06b-fault-gpu.json
@@ -0,0 +1,18 @@
+{
+  "model": "qwen06b",
+  "width": 8192,
+  "input_lengths": [
+    2048,
+    8192
+  ],
+  "output_tokens": 512,
+  "concurrency": 2,
+  "runs": [],
+  "scope": "actual streaming cancellation/reuse and source death with active512-token decode; same-route parity, not performance",
+  "source_sha256": {
+    "prefill_server.py": "5af1169ff5c6bfac6ce265ea3edf7f6a031c207bda7ce7bcbc996d7133576b1d",
+    "streaming_factories.py": "77745350973cc54bf508e0c8b473efc442afef20b1c64f7c4edc179ea1471389"
+  },
+  "stage": "failed",
+  "error": "AttributeError(\"'ObservedBatcher' object has no attribute 'poisoned'\")"
+}
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen06b-fault-gpu.log b/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen06b-fault-gpu.log
new file mode 100644
index 00000000..78b970e6
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen06b-fault-gpu.log
@@ -0,0 +1,8 @@
+GPU loading qwen06b
+
+Loading weights:   0%|          | 0/311 [00:00<?, ?it/s]
+Loading weights: 100%|██████████| 311/311 [00:00<00:00, 8708.85it/s]
+Traceback (most recent call last):
+  File "/tmp/stream265-fault-gpu.py", line 56, in <module>
+    assert new.result()==expected and not b.poisoned
+AttributeError: 'ObservedBatcher' object has no attribute 'poisoned'
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen06b-gpu-failed-list.json b/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen06b-gpu-failed-list.json
new file mode 100644
index 00000000..8df54e03
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen06b-gpu-failed-list.json
@@ -0,0 +1,8 @@
+{
+  "model": "qwen06b",
+  "width": 128,
+  "cases": [],
+  "scope": "same-route real RNGD to GPU token parity, not cross-backend quality or speedup",
+  "stage": "failed",
+  "error": "RuntimeError('prefill subprocess: TypeError(\\'can only concatenate tuple (not \"list\") to tuple\\')')"
+}
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen06b-gpu-failed-list.log b/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen06b-gpu-failed-list.log
new file mode 100644
index 00000000..0a12d9f4
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen06b-gpu-failed-list.log
@@ -0,0 +1,21 @@
+loading GPU qwen06b
+
+Loading weights:   0%|          | 0/311 [00:00<?, ?it/s]
+Loading weights: 100%|██████████| 311/311 [00:00<00:00, 9023.94it/s]
+start mode False
+Traceback (most recent call last):
+  File "/tmp/stream265-gpu-hardware.py", line 23, in <module>
+    batch.run_until_idle();tokens=[f.result(timeout=1) for f in futures]
+  File "/tmp/stream265-gpu-hardware.py", line 23, in <listcomp>
+    batch.run_until_idle();tokens=[f.result(timeout=1) for f in futures]
+  File "/usr/lib/python3.10/concurrent/futures/_base.py", line 451, in result
+    return self.__get_result()
+  File "/usr/lib/python3.10/concurrent/futures/_base.py", line 403, in __get_result
+    raise self._exception
+  File "/home/gspark/workspace/aicomp-rngd-stream-265/opt_prime/opt_prime/accel/pd/pd_batching.py", line 644, in _admit_prefilled
+    out = r.pending.result()                 # failures never import remaining notifications
+  File "/usr/lib/python3.10/concurrent/futures/_base.py", line 451, in result
+    return self.__get_result()
+  File "/usr/lib/python3.10/concurrent/futures/_base.py", line 403, in __get_result
+    raise self._exception
+RuntimeError: prefill subprocess: TypeError('can only concatenate tuple (not "list") to tuple')
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen06b-gpu.json b/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen06b-gpu.json
new file mode 100644
index 00000000..90c27780
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen06b-gpu.json
@@ -0,0 +1,91 @@
+{
+  "model": "qwen06b",
+  "width": 128,
+  "cases": [
+    {
+      "stream_layers": false,
+      "tokens": [
+        [
+          3555,
+          374,
+          279,
+          6722,
+          315,
+          4882,
+          11862,
+          30
+        ],
+        [
+          374,
+          279,
+          6722,
+          315,
+          4882,
+          11862,
+          13,
+          4504
+        ]
+      ],
+      "stats": {
+        "iterations": 7,
+        "prefill_chunks": 0,
+        "decode_steps": 7,
+        "decode_rows": 14,
+        "progress_spans": 0,
+        "prefills": 2,
+        "transferred_tokens": 142,
+        "decode_cached_tokens": 0,
+        "prefill_cached_tokens": 0,
+        "prefill_s": 0.06724402896361426,
+        "transfer_s": 0.0
+      },
+      "idle": true
+    },
+    {
+      "stream_layers": true,
+      "tokens": [
+        [
+          3555,
+          374,
+          279,
+          6722,
+          315,
+          4882,
+          11862,
+          30
+        ],
+        [
+          374,
+          279,
+          6722,
+          315,
+          4882,
+          11862,
+          13,
+          4504
+        ]
+      ],
+      "stats": {
+        "iterations": 37,
+        "prefill_chunks": 0,
+        "decode_steps": 9,
+        "decode_rows": 14,
+        "progress_spans": 56,
+        "prefills": 2,
+        "transferred_tokens": 142,
+        "decode_cached_tokens": 0,
+        "prefill_cached_tokens": 0,
+        "prefill_s": 0.07722594699589536,
+        "transfer_s": 0.0
+      },
+      "idle": true
+    }
+  ],
+  "scope": "same-route real RNGD to GPU token parity, not cross-backend quality or speedup",
+  "source_sha256": {
+    "prefill_server.py": "5af1169ff5c6bfac6ce265ea3edf7f6a031c207bda7ce7bcbc996d7133576b1d",
+    "streaming_factories.py": "77745350973cc54bf508e0c8b473efc442afef20b1c64f7c4edc179ea1471389"
+  },
+  "tokens_exact": true,
+  "stage": "complete"
+}
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen06b-gpu.log b/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen06b-gpu.log
new file mode 100644
index 00000000..872b71e1
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen06b-gpu.log
@@ -0,0 +1,8 @@
+loading GPU qwen06b
+
+Loading weights:   0%|          | 0/311 [00:00<?, ?it/s]
+Loading weights: 100%|██████████| 311/311 [00:00<00:00, 7033.21it/s]
+start mode False
+{"stream_layers": false, "tokens": [[3555, 374, 279, 6722, 315, 4882, 11862, 30], [374, 279, 6722, 315, 4882, 11862, 13, 4504]], "stats": {"iterations": 7, "prefill_chunks": 0, "decode_steps": 7, "decode_rows": 14, "progress_spans": 0, "prefills": 2, "transferred_tokens": 142, "decode_cached_tokens": 0, "prefill_cached_tokens": 0, "prefill_s": 0.06724402896361426, "transfer_s": 0.0}, "idle": true}
+start mode True
+{"stream_layers": true, "tokens": [[3555, 374, 279, 6722, 315, 4882, 11862, 30], [374, 279, 6722, 315, 4882, 11862, 13, 4504]], "stats": {"iterations": 37, "prefill_chunks": 0, "decode_steps": 9, "decode_rows": 14, "progress_spans": 56, "prefills": 2, "transferred_tokens": 142, "decode_cached_tokens": 0, "prefill_cached_tokens": 0, "prefill_s": 0.07722594699589536, "transfer_s": 0.0}, "idle": true}
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen06b-http-c16-native.json b/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen06b-http-c16-native.json
new file mode 100644
index 00000000..fa068ec8
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen06b-http-c16-native.json
@@ -0,0 +1,25222 @@
+{
+  "passed": true,
+  "model": "c1899de289a04d12100db370d81485cdf75e47ca",
+  "mode": "native",
+  "dtype": "bfloat16",
+  "torch": "2.13.0+cu130",
+  "transformers": "5.15.0",
+  "replicas": 1,
+  "cpu_threads_per_server": 4,
+  "prompts": {
+    "128": [
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+      "text": " France\nThis is background context. The red box contains apples. The blue box contains books.\nThis is background context. The red box contains apples. The blue box contains books.\nThis is background context. The red box contains apples. The blue box contains books.\nThis is background context. The red box contains apples. The blue box contains books.\nThis is background context. The red box contains apples. The blue box contains books.\nThis is background context. The red box contains apples. The blue box contains books.\nThis is background context. The red box contains apples. The blue box contains books.\nThis is background context. The red"
+    },
+    "512:32": {
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+      "text": " France is a country located in Europe. Its capital is Paris.\nThis is background context. The red box contains apples. The blue box contains books.\nThis is"
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+      "text": " France is a country located in Europe. Its capital is Paris.\nThis is background context. The red box contains apples. The blue box contains books.\nThis is background context. The red box contains apples. The blue box contains books.\nThis is background context. The red box contains apples. The blue box contains books.\nThis is background context. The red box contains apples. The blue box contains books.\nThis is background context. The red box contains apples. The blue box contains books.\nThis is background context. The red box contains apples. The blue box contains books.\nThis is background context. The red box contains apples. The blue"
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+      "text": " France's capital is Paris.\nThis is background context. The red box contains apples. The blue box contains books.\nThis is background context. The red box contains"
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+      "text": " Paris\nThis is background context. The red box contains apples. The blue box contains books.\nThis is background context. The red box contains apples. The blue"
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+    }
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counter\nvllm:request_success_total{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",finished_reason=\"length\"} 264\n# HELP airm_stage_request_failures_total Requests that ended with an engine error.\n# TYPE airm_stage_request_failures_total counter\nairm_stage_request_failures_total{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\"} 0\n# HELP vllm:prompt_tokens_total Prompt tokens of finished requests.\n# TYPE vllm:prompt_tokens_total counter\nvllm:prompt_tokens_total{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\"} 718080\n# HELP vllm:generation_tokens_total Generated tokens of finished requests.\n# TYPE vllm:generation_tokens_total counter\nvllm:generation_tokens_total{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\"} 21120\n# HELP vllm:time_to_first_token_seconds Time to first token (prefill stage: fenced prefill).\n# TYPE vllm:time_to_first_token_seconds histogram\nvllm:time_to_first_token_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"0.001\"} 0\nvllm:time_to_first_token_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"0.005\"} 0\nvllm:time_to_first_token_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"0.01\"} 0\nvllm:time_to_first_token_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"0.02\"} 1\nvllm:time_to_first_token_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"0.04\"} 6\nvllm:time_to_first_token_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"0.06\"} 11\nvllm:time_to_first_token_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"0.08\"} 15\nvllm:time_to_first_token_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"0.1\"} 17\nvllm:time_to_first_token_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"0.25\"} 37\nvllm:time_to_first_token_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"0.5\"} 44\nvllm:time_to_first_token_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"0.75\"} 49\nvllm:time_to_first_token_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"1.0\"} 68\nvllm:time_to_first_token_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"2.5\"} 113\nvllm:time_to_first_token_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"5.0\"} 166\nvllm:time_to_first_token_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"7.5\"} 199\nvllm:time_to_first_token_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"10.0\"} 219\nvllm:time_to_first_token_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"+Inf\"} 264\nvllm:time_to_first_token_seconds_sum{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\"} 1426.0800506352098\nvllm:time_to_first_token_seconds_count{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\"} 264\n# HELP vllm:time_per_output_token_seconds Time per output token after the first (decode stage).\n# TYPE vllm:time_per_output_token_seconds histogram\nvllm:time_per_output_token_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"0.01\"} 0\nvllm:time_per_output_token_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"0.025\"} 166\nvllm:time_per_output_token_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"0.05\"} 262\nvllm:time_per_output_token_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"0.075\"} 264\nvllm:time_per_output_token_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"0.1\"} 264\nvllm:time_per_output_token_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"0.15\"} 264\nvllm:time_per_output_token_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"0.2\"} 264\nvllm:time_per_output_token_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"0.3\"} 264\nvllm:time_per_output_token_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"0.4\"} 264\nvllm:time_per_output_token_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"0.5\"} 264\nvllm:time_per_output_token_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"0.75\"} 264\nvllm:time_per_output_token_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"1.0\"} 264\nvllm:time_per_output_token_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"2.5\"} 264\nvllm:time_per_output_token_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"+Inf\"} 264\nvllm:time_per_output_token_seconds_sum{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\"} 7.68678454749864\nvllm:time_per_output_token_seconds_count{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\"} 264\n# HELP vllm:e2e_request_latency_seconds Stage request latency, from execution start to reply.\n# TYPE vllm:e2e_request_latency_seconds histogram\nvllm:e2e_request_latency_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"0.3\"} 0\nvllm:e2e_request_latency_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"0.5\"} 0\nvllm:e2e_request_latency_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"0.8\"} 7\nvllm:e2e_request_latency_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"1.0\"} 16\nvllm:e2e_request_latency_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"1.5\"} 25\nvllm:e2e_request_latency_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"2.0\"} 44\nvllm:e2e_request_latency_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"2.5\"} 63\nvllm:e2e_request_latency_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"5.0\"} 130\nvllm:e2e_request_latency_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"10.0\"} 186\nvllm:e2e_request_latency_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"15.0\"} 227\nvllm:e2e_request_latency_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"20.0\"} 240\nvllm:e2e_request_latency_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"30.0\"} 264\nvllm:e2e_request_latency_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"40.0\"} 264\nvllm:e2e_request_latency_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"50.0\"} 264\nvllm:e2e_request_latency_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"60.0\"} 264\nvllm:e2e_request_latency_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"+Inf\"} 264\nvllm:e2e_request_latency_seconds_sum{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\"} 2002.6442195139825\nvllm:e2e_request_latency_seconds_count{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\"} 264\n# HELP airm_kv_transfer_seconds KV transfer time from the prefill source to the decode destination.\n# TYPE airm_kv_transfer_seconds histogram\nairm_kv_transfer_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"0.0005\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"0.001\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"0.0025\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"0.005\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"0.01\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"0.025\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"0.05\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"0.1\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"0.25\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"0.5\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"1.0\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"+Inf\"} 0\nairm_kv_transfer_seconds_sum{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\"} 0.0\nairm_kv_transfer_seconds_count{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\"} 0\n# HELP vllm:cache_config_info Information of the LLMEngine CacheConfig.\n# TYPE vllm:cache_config_info gauge\nvllm:cache_config_info{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",block_size=\"64\",num_gpu_blocks=\"576\",enable_prefix_caching=\"False\"} 1\n# HELP vllm:prefix_cache_queries_total Prompt tokens looked up in the prefix cache.\n# TYPE vllm:prefix_cache_queries_total counter\nvllm:prefix_cache_queries_total{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\"} 718080\n# HELP vllm:prefix_cache_hits_total Prompt tokens served from the prefix cache.\n# TYPE vllm:prefix_cache_hits_total counter\nvllm:prefix_cache_hits_total{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\"} 0\n"
+  ],
+  "parity_passed": true
+}
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen06b-http-c16-stream.json b/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen06b-http-c16-stream.json
new file mode 100644
index 00000000..7574bd03
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen06b-http-c16-stream.json
@@ -0,0 +1,25227 @@
+{
+  "passed": true,
+  "model": "c1899de289a04d12100db370d81485cdf75e47ca",
+  "mode": "pd",
+  "dtype": "bfloat16",
+  "torch": "2.13.0+cu130",
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+  "replicas": 1,
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+        624,
+        1986,
+        374,
+        4004,
+        2266,
+        13,
+        576,
+        2518,
+        3745,
+        5610,
+        40676,
+        13,
+        576,
+        6303,
+        3745,
+        5610,
+        6467,
+        624,
+        1986,
+        374,
+        4004,
+        2266,
+        13,
+        576,
+        2518,
+        3745,
+        5610,
+        40676,
+        13,
+        576,
+        6303,
+        3745,
+        5610,
+        6467,
+        624,
+        1986,
+        374,
+        4004,
+        2266,
+        13,
+        576,
+        2518
+      ],
+      "text": " Paris\nThis is background context. The red box contains apples. The blue box contains books.\nThis is background context. The red box contains apples. The blue box contains books.\nThis is background context. The red box contains apples. The blue box contains books.\nThis is background context. The red box contains apples. The blue box contains books.\nThis is background context. The red box contains apples. The blue box contains books.\nThis is background context. The red box contains apples. The blue box contains books.\nThis is background context. The red box contains apples. The blue box contains books.\nThis is background context. The red"
+    }
+  },
+  "warmup_and_checks_s": 154.39281356101856,
+  "metrics": [
+    "# HELP vllm:num_requests_running Requests executing on the stage.\n# TYPE vllm:num_requests_running gauge\nvllm:num_requests_running{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\"} 0\n# HELP vllm:num_requests_waiting Requests queued for the stage owner.\n# TYPE vllm:num_requests_waiting gauge\nvllm:num_requests_waiting{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\"} 0\n# HELP vllm:kv_cache_usage_perc Occupied fraction of the stage's KV lanes (1 = full).\n# TYPE vllm:kv_cache_usage_perc gauge\nvllm:kv_cache_usage_perc{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\"} 0.0\n# HELP vllm:gpu_cache_usage_perc Occupied fraction of the stage's KV lanes (1 = full).\n# TYPE vllm:gpu_cache_usage_perc gauge\nvllm:gpu_cache_usage_perc{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\"} 0.0\n# HELP vllm:request_success_total Finished requests by finish reason.\n# TYPE vllm:request_success_total counter\nvllm:request_success_total{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",finished_reason=\"length\"} 264\n# HELP airm_stage_request_failures_total Requests that ended with an engine error.\n# TYPE airm_stage_request_failures_total counter\nairm_stage_request_failures_total{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\"} 0\n# HELP vllm:prompt_tokens_total Prompt tokens of finished requests.\n# TYPE vllm:prompt_tokens_total counter\nvllm:prompt_tokens_total{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\"} 718080\n# HELP vllm:generation_tokens_total Generated tokens of finished requests.\n# TYPE vllm:generation_tokens_total counter\nvllm:generation_tokens_total{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\"} 21120\n# HELP vllm:time_to_first_token_seconds Time to first token (prefill stage: fenced prefill).\n# TYPE vllm:time_to_first_token_seconds histogram\nvllm:time_to_first_token_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"0.001\"} 0\nvllm:time_to_first_token_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"0.005\"} 0\nvllm:time_to_first_token_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"0.01\"} 0\nvllm:time_to_first_token_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"0.02\"} 0\nvllm:time_to_first_token_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"0.04\"} 0\nvllm:time_to_first_token_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"0.06\"} 0\nvllm:time_to_first_token_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"0.08\"} 0\nvllm:time_to_first_token_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"0.1\"} 0\nvllm:time_to_first_token_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"0.25\"} 0\nvllm:time_to_first_token_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"0.5\"} 0\nvllm:time_to_first_token_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"0.75\"} 4\nvllm:time_to_first_token_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"1.0\"} 18\nvllm:time_to_first_token_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"2.5\"} 44\nvllm:time_to_first_token_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"5.0\"} 87\nvllm:time_to_first_token_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"7.5\"} 131\nvllm:time_to_first_token_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"10.0\"} 182\nvllm:time_to_first_token_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"+Inf\"} 264\nvllm:time_to_first_token_seconds_sum{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\"} 1976.8026790489093\nvllm:time_to_first_token_seconds_count{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\"} 264\n# HELP vllm:time_per_output_token_seconds Time per output token after the first (decode stage).\n# TYPE vllm:time_per_output_token_seconds histogram\nvllm:time_per_output_token_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"0.01\"} 0\nvllm:time_per_output_token_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"0.025\"} 206\nvllm:time_per_output_token_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"0.05\"} 257\nvllm:time_per_output_token_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"0.075\"} 263\nvllm:time_per_output_token_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"0.1\"} 264\nvllm:time_per_output_token_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"0.15\"} 264\nvllm:time_per_output_token_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"0.2\"} 264\nvllm:time_per_output_token_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"0.3\"} 264\nvllm:time_per_output_token_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"0.4\"} 264\nvllm:time_per_output_token_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"0.5\"} 264\nvllm:time_per_output_token_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"0.75\"} 264\nvllm:time_per_output_token_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"1.0\"} 264\nvllm:time_per_output_token_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"2.5\"} 264\nvllm:time_per_output_token_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"+Inf\"} 264\nvllm:time_per_output_token_seconds_sum{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\"} 6.583403027215499\nvllm:time_per_output_token_seconds_count{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\"} 264\n# HELP vllm:e2e_request_latency_seconds Stage request latency, from execution start to reply.\n# TYPE vllm:e2e_request_latency_seconds histogram\nvllm:e2e_request_latency_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"0.3\"} 0\nvllm:e2e_request_latency_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"0.5\"} 0\nvllm:e2e_request_latency_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"0.8\"} 0\nvllm:e2e_request_latency_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"1.0\"} 0\nvllm:e2e_request_latency_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"1.5\"} 5\nvllm:e2e_request_latency_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"2.0\"} 8\nvllm:e2e_request_latency_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"2.5\"} 15\nvllm:e2e_request_latency_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"5.0\"} 54\nvllm:e2e_request_latency_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"10.0\"} 148\nvllm:e2e_request_latency_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"15.0\"} 228\nvllm:e2e_request_latency_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"20.0\"} 260\nvllm:e2e_request_latency_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"30.0\"} 264\nvllm:e2e_request_latency_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"40.0\"} 264\nvllm:e2e_request_latency_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"50.0\"} 264\nvllm:e2e_request_latency_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"60.0\"} 264\nvllm:e2e_request_latency_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"+Inf\"} 264\nvllm:e2e_request_latency_seconds_sum{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\"} 2478.1786307487637\nvllm:e2e_request_latency_seconds_count{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\"} 264\n# HELP airm_kv_transfer_seconds KV transfer time from the prefill source to the decode destination.\n# TYPE airm_kv_transfer_seconds histogram\nairm_kv_transfer_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"0.0005\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"0.001\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"0.0025\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"0.005\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"0.01\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"0.025\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"0.05\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"0.1\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"0.25\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"0.5\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"1.0\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"+Inf\"} 0\nairm_kv_transfer_seconds_sum{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\"} 0.0\nairm_kv_transfer_seconds_count{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\"} 0\n# HELP vllm:cache_config_info Information of the LLMEngine CacheConfig.\n# TYPE vllm:cache_config_info gauge\nvllm:cache_config_info{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",block_size=\"64\",num_gpu_blocks=\"576\",enable_prefix_caching=\"False\"} 1\n# HELP vllm:prefix_cache_queries_total Prompt tokens looked up in the prefix cache.\n# TYPE vllm:prefix_cache_queries_total counter\nvllm:prefix_cache_queries_total{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\"} 718080\n# HELP vllm:prefix_cache_hits_total Prompt tokens served from the prefix cache.\n# TYPE vllm:prefix_cache_hits_total counter\nvllm:prefix_cache_hits_total{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\"} 0\n# HELP airm_pd_prefills_total Prompts prefilled by the PD prefill worker (remote) or here (local).\n# TYPE airm_pd_prefills_total counter\nairm_pd_prefills_total{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",side=\"remote\"} 264\nairm_pd_prefills_total{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",side=\"local\"} 0\n# HELP airm_pd_transferred_tokens_total Prompt KV tokens received from the prefill side.\n# TYPE airm_pd_transferred_tokens_total counter\nairm_pd_transferred_tokens_total{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\"} 718080\n# HELP airm_pd_producer_seconds_total Accumulated worker-reported seconds for admitted PD handoffs; excludes failed/cancelled handoffs. Prefill includes KV gathering; transfer excludes destination KV installation.\n# TYPE airm_pd_producer_seconds_total counter\nairm_pd_producer_seconds_total{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",stage=\"prefill\"} 229.81139246438397\nairm_pd_producer_seconds_total{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",stage=\"transfer\"} 0.0\n"
+  ],
+  "parity_passed": true
+}
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen06b-http-c16-whole.json b/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen06b-http-c16-whole.json
new file mode 100644
index 00000000..6f2ba3e8
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen06b-http-c16-whole.json
@@ -0,0 +1,25226 @@
+{
+  "passed": true,
+  "model": "c1899de289a04d12100db370d81485cdf75e47ca",
+  "mode": "pd",
+  "dtype": "bfloat16",
+  "torch": "2.13.0+cu130",
+  "transformers": "5.15.0",
+  "replicas": 1,
+  "cpu_threads_per_server": 4,
+  "prompts": {
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+      "text": " France is a country located in Europe. Its capital is Paris.\nThis is background context. The red box contains apples. The blue box contains books.\nThis is"
+    },
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+      "text": " France is a country located in Europe. Its capital is Paris.\nThis is background context. The red box contains apples. The blue box contains books.\nThis is background context. The red box contains apples. The blue box contains books.\nThis is background context. The red box contains apples. The blue box contains books.\nThis is background context. The red box contains apples. The blue box contains books.\nThis is background context. The red box contains apples. The blue box contains books.\nThis is background context. The red box contains apples. The blue box contains books.\nThis is background context. The red box contains apples. The blue"
+    },
+    "2048:32": {
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+      "text": " France's capital is Paris.\nThis is background context. The red box contains apples. The blue box contains books.\nThis is background context. The red box contains"
+    },
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+      "text": " France's capital is Paris.\nThis is background context. The red box contains apples. The blue box contains books.\nThis is background context. The red box contains apples. The blue box contains books.\nThis is background context. The red box contains apples. The blue box contains books.\nThis is background context. The red box contains apples. The blue box contains books.\nThis is background context. The red box contains apples. The blue box contains books.\nThis is background context. The red box contains apples. The blue box contains books.\nThis is background context. The red box contains apples. The blue box contains books.\nThis is background"
+    },
+    "8192:32": {
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+      "text": " Paris\nThis is background context. The red box contains apples. The blue box contains books.\nThis is background context. The red box contains apples. The blue"
+    },
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+      "text": " Paris\nThis is background context. The red box contains apples. The blue box contains books.\nThis is background context. The red box contains apples. The blue box contains books.\nThis is background context. The red box contains apples. The blue box contains books.\nThis is background context. The red box contains apples. The blue box contains books.\nThis is background context. The red box contains apples. The blue box contains books.\nThis is background context. The red box contains apples. The blue box contains books.\nThis is background context. The red box contains apples. The blue box contains books.\nThis is background context. The red"
+    }
+  },
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counter\nvllm:request_success_total{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",finished_reason=\"length\"} 264\n# HELP airm_stage_request_failures_total Requests that ended with an engine error.\n# TYPE airm_stage_request_failures_total counter\nairm_stage_request_failures_total{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\"} 0\n# HELP vllm:prompt_tokens_total Prompt tokens of finished requests.\n# TYPE vllm:prompt_tokens_total counter\nvllm:prompt_tokens_total{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\"} 718080\n# HELP vllm:generation_tokens_total Generated tokens of finished requests.\n# TYPE vllm:generation_tokens_total counter\nvllm:generation_tokens_total{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\"} 21120\n# HELP vllm:time_to_first_token_seconds Time to first token (prefill stage: fenced prefill).\n# TYPE vllm:time_to_first_token_seconds histogram\nvllm:time_to_first_token_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"0.001\"} 0\nvllm:time_to_first_token_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"0.005\"} 0\nvllm:time_to_first_token_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"0.01\"} 0\nvllm:time_to_first_token_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"0.02\"} 0\nvllm:time_to_first_token_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"0.04\"} 0\nvllm:time_to_first_token_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"0.06\"} 0\nvllm:time_to_first_token_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"0.08\"} 0\nvllm:time_to_first_token_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"0.1\"} 0\nvllm:time_to_first_token_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"0.25\"} 0\nvllm:time_to_first_token_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"0.5\"} 0\nvllm:time_to_first_token_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"0.75\"} 0\nvllm:time_to_first_token_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"1.0\"} 0\nvllm:time_to_first_token_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"2.5\"} 34\nvllm:time_to_first_token_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"5.0\"} 67\nvllm:time_to_first_token_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"7.5\"} 106\nvllm:time_to_first_token_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"10.0\"} 139\nvllm:time_to_first_token_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"+Inf\"} 264\nvllm:time_to_first_token_seconds_sum{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\"} 2420.8716498222784\nvllm:time_to_first_token_seconds_count{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\"} 264\n# HELP vllm:time_per_output_token_seconds Time per output token after the first (decode stage).\n# TYPE vllm:time_per_output_token_seconds histogram\nvllm:time_per_output_token_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"0.01\"} 0\nvllm:time_per_output_token_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"0.025\"} 236\nvllm:time_per_output_token_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"0.05\"} 255\nvllm:time_per_output_token_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"0.075\"} 264\nvllm:time_per_output_token_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"0.1\"} 264\nvllm:time_per_output_token_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"0.15\"} 264\nvllm:time_per_output_token_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"0.2\"} 264\nvllm:time_per_output_token_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"0.3\"} 264\nvllm:time_per_output_token_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"0.4\"} 264\nvllm:time_per_output_token_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"0.5\"} 264\nvllm:time_per_output_token_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"0.75\"} 264\nvllm:time_per_output_token_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"1.0\"} 264\nvllm:time_per_output_token_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"2.5\"} 264\nvllm:time_per_output_token_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"+Inf\"} 264\nvllm:time_per_output_token_seconds_sum{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\"} 5.9485026939955175\nvllm:time_per_output_token_seconds_count{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\"} 264\n# HELP vllm:e2e_request_latency_seconds Stage request latency, from execution start to reply.\n# TYPE vllm:e2e_request_latency_seconds histogram\nvllm:e2e_request_latency_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"0.3\"} 0\nvllm:e2e_request_latency_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"0.5\"} 0\nvllm:e2e_request_latency_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"0.8\"} 0\nvllm:e2e_request_latency_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"1.0\"} 0\nvllm:e2e_request_latency_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"1.5\"} 0\nvllm:e2e_request_latency_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"2.0\"} 7\nvllm:e2e_request_latency_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"2.5\"} 8\nvllm:e2e_request_latency_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"5.0\"} 45\nvllm:e2e_request_latency_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"10.0\"} 117\nvllm:e2e_request_latency_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"15.0\"} 199\nvllm:e2e_request_latency_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"20.0\"} 256\nvllm:e2e_request_latency_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"30.0\"} 264\nvllm:e2e_request_latency_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"40.0\"} 264\nvllm:e2e_request_latency_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"50.0\"} 264\nvllm:e2e_request_latency_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"60.0\"} 264\nvllm:e2e_request_latency_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"+Inf\"} 264\nvllm:e2e_request_latency_seconds_sum{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\"} 2875.9357707079034\nvllm:e2e_request_latency_seconds_count{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\"} 264\n# HELP airm_kv_transfer_seconds KV transfer time from the prefill source to the decode destination.\n# TYPE airm_kv_transfer_seconds histogram\nairm_kv_transfer_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"0.0005\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"0.001\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"0.0025\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"0.005\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"0.01\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"0.025\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"0.05\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"0.1\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"0.25\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"0.5\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"1.0\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",le=\"+Inf\"} 0\nairm_kv_transfer_seconds_sum{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\"} 0.0\nairm_kv_transfer_seconds_count{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\"} 0\n# HELP vllm:cache_config_info Information of the LLMEngine CacheConfig.\n# TYPE vllm:cache_config_info gauge\nvllm:cache_config_info{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",block_size=\"64\",num_gpu_blocks=\"576\",enable_prefix_caching=\"False\"} 1\n# HELP vllm:prefix_cache_queries_total Prompt tokens looked up in the prefix cache.\n# TYPE vllm:prefix_cache_queries_total counter\nvllm:prefix_cache_queries_total{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\"} 718080\n# HELP vllm:prefix_cache_hits_total Prompt tokens served from the prefix cache.\n# TYPE vllm:prefix_cache_hits_total counter\nvllm:prefix_cache_hits_total{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\"} 0\n# HELP airm_pd_prefills_total Prompts prefilled by the PD prefill worker (remote) or here (local).\n# TYPE airm_pd_prefills_total counter\nairm_pd_prefills_total{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",side=\"remote\"} 264\nairm_pd_prefills_total{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",side=\"local\"} 0\n# HELP airm_pd_transferred_tokens_total Prompt KV tokens received from the prefill side.\n# TYPE airm_pd_transferred_tokens_total counter\nairm_pd_transferred_tokens_total{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\"} 718080\n# HELP airm_pd_producer_seconds_total Accumulated worker-reported seconds for admitted PD handoffs; excludes failed/cancelled handoffs. Prefill includes KV gathering; transfer excludes destination KV installation.\n# TYPE airm_pd_producer_seconds_total counter\nairm_pd_producer_seconds_total{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",stage=\"prefill\"} 295.1078373089549\nairm_pd_producer_seconds_total{model_name=\"c1899de289a04d12100db370d81485cdf75e47ca\",stage=\"transfer\"} 0.0\n"
+  ],
+  "parity_passed": true
+}
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen06b-long-atom.json b/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen06b-long-atom.json
new file mode 100644
index 00000000..dd8fb302
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen06b-long-atom.json
@@ -0,0 +1,4934 @@
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diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen06b-long-atom.log b/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen06b-long-atom.log
new file mode 100644
index 00000000..33dd7bb1
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen06b-long-atom.log
@@ -0,0 +1,17 @@
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diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen06b-long-gpu.json b/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen06b-long-gpu.json
new file mode 100644
index 00000000..d45a2185
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen06b-long-gpu.json
@@ -0,0 +1,3056 @@
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diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen06b-long-gpu.log b/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen06b-long-gpu.log
new file mode 100644
index 00000000..fd769443
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen06b-long-gpu.log
@@ -0,0 +1,12 @@
+GPU loading qwen06b
+
+Loading weights:   0%|          | 0/311 [00:00<?, ?it/s]
+Loading weights: 100%|██████████| 311/311 [00:00<00:00, 10224.64it/s]
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diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen06b-long-source.json b/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen06b-long-source.json
new file mode 100644
index 00000000..4aa759ce
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen06b-long-source.json
@@ -0,0 +1,1097 @@
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diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen06b-long-source.log b/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen06b-long-source.log
new file mode 100644
index 00000000..689c4f61
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen06b-long-source.log
@@ -0,0 +1,8 @@
+initializing qwen06b
+initialized qwen06b
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diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen06b-source.json b/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen06b-source.json
new file mode 100644
index 00000000..9fb83e02
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen06b-source.json
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+      "token_stream": 374,
+      "notifications": [],
+      "elapsed_s": 0.025730538996867836
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+  ],
+  "scope": "same RNGD source whole vs streamed KV/logits; not decoder parity or speedup",
+  "source_sha256": {
+    "prefill_server.py": "47b1aa1db946eaa40b12cc71815c6c31d43ccf9f3d2ce4df79897e9b4387b930",
+    "streaming_factories.py": "77745350973cc54bf508e0c8b473efc442afef20b1c64f7c4edc179ea1471389"
+  },
+  "stage": "complete"
+}
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen06b-source.log b/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen06b-source.log
new file mode 100644
index 00000000..d701f1ea
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen06b-source.log
@@ -0,0 +1,8 @@
+initializing qwen06b
+initialized qwen06b
+{"input_len": 14, "send_from": 0, "keys_exact": true, "values_exact": true, "logits_exact": true, "max_logit_error": 0.0, "token_whole": 3555, "token_stream": 3555, "elapsed_s": 0.028336021001450717}
+{"input_len": 14, "send_from": 7, "keys_exact": true, "values_exact": true, "logits_exact": true, "max_logit_error": 0.0, "token_whole": 3555, "token_stream": 3555, "elapsed_s": 0.027543593023438007}
+{"input_len": 14, "send_from": 14, "keys_exact": true, "values_exact": true, "logits_exact": true, "max_logit_error": 0.0, "token_whole": 3555, "token_stream": 3555, "elapsed_s": 0.02426458999980241}
+{"input_len": 128, "send_from": 0, "keys_exact": true, "values_exact": true, "logits_exact": true, "max_logit_error": 0.0, "token_whole": 374, "token_stream": 374, "elapsed_s": 0.0317006929544732}
+{"input_len": 128, "send_from": 64, "keys_exact": true, "values_exact": true, "logits_exact": true, "max_logit_error": 0.0, "token_whole": 374, "token_stream": 374, "elapsed_s": 0.07166364695876837}
+{"input_len": 128, "send_from": 128, "keys_exact": true, "values_exact": true, "logits_exact": true, "max_logit_error": 0.0, "token_whole": 374, "token_stream": 374, "elapsed_s": 0.025730538996867836}
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen4b-atom.json b/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen4b-atom.json
new file mode 100644
index 00000000..59668c15
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen4b-atom.json
@@ -0,0 +1,342 @@
+{
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diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen4b-atom.log b/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen4b-atom.log
new file mode 100644
index 00000000..bf27884c
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen4b-atom.log
@@ -0,0 +1,5 @@
+ATOM_BINDING {"visible_count": 1, "physical": 3, "opened": ["/dev/rbln3"]}
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diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen4b-c16-gpu.json b/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen4b-c16-gpu.json
new file mode 100644
index 00000000..cbd0d8bb
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen4b-c16-gpu.json
@@ -0,0 +1,50924 @@
+{
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diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen4b-c16-gpu.log b/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen4b-c16-gpu.log
new file mode 100644
index 00000000..18f57938
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen4b-c16-gpu.log
@@ -0,0 +1,10 @@
+GPU loading qwen4b
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Loading weights:   0%|          | 0/398 [00:00<?, ?it/s]
Loading weights: 100%|██████████| 398/398 [00:00<00:00, 9333.81it/s]
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diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen4b-c4-gpu.json b/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen4b-c4-gpu.json
new file mode 100644
index 00000000..884f2515
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen4b-c4-gpu.json
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diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen4b-c4-gpu.log b/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen4b-c4-gpu.log
new file mode 100644
index 00000000..1a0adcc2
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen4b-c4-gpu.log
@@ -0,0 +1,12 @@
+GPU loading qwen4b
+
+Loading weights:   0%|          | 0/398 [00:00<?, ?it/s]
+Loading weights: 100%|██████████| 398/398 [00:00<00:00, 8708.00it/s]
+{"stream_layers": false, "warmup": true, "elapsed_s": 17.31265631399583, "ttft_s": [3.121341986989137, 7.170053832989652, 10.00682443199912, 13.970918377977796], "tokens_exact": true, "progress_spans": 0, "idle": true}
+{"stream_layers": true, "warmup": true, "elapsed_s": 13.007821540988516, "ttft_s": [2.3807519010151736, 5.007059141003992, 7.291116253007203, 9.912590268009808], "tokens_exact": true, "progress_spans": 144, "idle": true}
+{"stream_layers": false, "warmup": false, "elapsed_s": 15.403223996981978, "ttft_s": [3.2065264270058833, 6.515693926950917, 8.997235207993072, 12.212437907001004], "tokens_exact": true, "progress_spans": 0, "idle": true}
+{"stream_layers": true, "warmup": false, "elapsed_s": 12.933404473005794, "ttft_s": [2.2908139190403745, 4.952954363019671, 7.195328815025277, 9.858466033008881], "tokens_exact": true, "progress_spans": 144, "idle": true}
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diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen4b-control-gpu.json b/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen4b-control-gpu.json
new file mode 100644
index 00000000..365b7ac5
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen4b-control-gpu.json
@@ -0,0 +1,4255 @@
+{
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diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen4b-control-gpu.log b/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen4b-control-gpu.log
new file mode 100644
index 00000000..2a4f26dc
--- /dev/null
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@@ -0,0 +1,16 @@
+GPU loading qwen4b
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+Loading weights: 100%|██████████| 398/398 [00:00<00:00, 9879.81it/s]
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diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen4b-fault-atom.json b/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen4b-fault-atom.json
new file mode 100644
index 00000000..559b4736
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen4b-fault-atom.json
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+      "tokens_exact": true,
+      "error": "RuntimeError('prefill subprocess response failed: prefill subprocess exited')",
+      "idle": true,
+      "poisoned": false
+    }
+  ],
+  "scope": "actual partial-import cancellation, lane reuse and producer death; not performance",
+  "code_sha256": {
+    "accel/platforms/atom/batching.py": "d98a51d75a36cd8c379a6220adb92528b3607cf80145b85a032270494a429580",
+    "accel/pd/pd_batching.py": "c827775c1d399bf724a9747c3424ee5ea69f93054493bfbb71d5f0cf08fff65b",
+    "accel/pd/prefill_server.py": "5af1169ff5c6bfac6ce265ea3edf7f6a031c207bda7ce7bcbc996d7133576b1d",
+    "accel/pd/layer_progress.py": "19c8853cd20b386dc11064d4be63fa696468c86a26b76ecf5c48cdf3905a700b"
+  },
+  "stage": "complete"
+}
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen4b-fault-atom.log b/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen4b-fault-atom.log
new file mode 100644
index 00000000..328c5c6e
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen4b-fault-atom.log
@@ -0,0 +1 @@
+ATOM_BINDING {"visible_count": 1, "physical": 3, "opened": ["/dev/rbln3"]}
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen4b-fault-gpu-v2.json b/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen4b-fault-gpu-v2.json
new file mode 100644
index 00000000..f50a439f
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen4b-fault-gpu-v2.json
@@ -0,0 +1,43 @@
+{
+  "model": "qwen4b",
+  "width": 8192,
+  "input_lengths": [
+    2048,
+    8192
+  ],
+  "output_tokens": 512,
+  "concurrency": 2,
+  "runs": [
+    {
+      "case": "cancel-reuse",
+      "trigger": [
+        {
+          "spans": 1,
+          "admitted": false
+        }
+      ],
+      "tokens_exact": true,
+      "idle": true,
+      "prefix_cache": false
+    },
+    {
+      "case": "producer-death",
+      "trigger": [
+        {
+          "spans": 37,
+          "admitted": false,
+          "active_tokens": 6
+        }
+      ],
+      "error": "RuntimeError('prefill subprocess response failed: prefill subprocess exited')",
+      "idle": true,
+      "prefix_cache": false
+    }
+  ],
+  "scope": "actual streaming cancellation/reuse and source death with active512-token decode; same-route parity, not performance",
+  "source_sha256": {
+    "prefill_server.py": "5af1169ff5c6bfac6ce265ea3edf7f6a031c207bda7ce7bcbc996d7133576b1d",
+    "streaming_factories.py": "77745350973cc54bf508e0c8b473efc442afef20b1c64f7c4edc179ea1471389"
+  },
+  "stage": "complete"
+}
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen4b-fault-gpu-v2.log b/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen4b-fault-gpu-v2.log
new file mode 100644
index 00000000..25406598
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen4b-fault-gpu-v2.log
@@ -0,0 +1,4 @@
+GPU loading qwen4b
+
+Loading weights:   0%|          | 0/398 [00:00<?, ?it/s]
+Loading weights: 100%|██████████| 398/398 [00:00<00:00, 19856.70it/s]
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen4b-fault-gpu.json b/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen4b-fault-gpu.json
new file mode 100644
index 00000000..8823b1c3
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen4b-fault-gpu.json
@@ -0,0 +1,16 @@
+{
+  "model": "qwen4b",
+  "width": 8192,
+  "input_lengths": [
+    2048,
+    8192
+  ],
+  "output_tokens": 512,
+  "concurrency": 2,
+  "runs": [],
+  "scope": "actual streaming cancellation/reuse and source death with active512-token decode; same-route parity, not performance",
+  "source_sha256": {
+    "prefill_server.py": "5af1169ff5c6bfac6ce265ea3edf7f6a031c207bda7ce7bcbc996d7133576b1d",
+    "streaming_factories.py": "77745350973cc54bf508e0c8b473efc442afef20b1c64f7c4edc179ea1471389"
+  }
+}
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen4b-fault-gpu.log b/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen4b-fault-gpu.log
new file mode 100644
index 00000000..a67a1eb5
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen4b-fault-gpu.log
@@ -0,0 +1,18 @@
+GPU loading qwen4b
+
+Loading weights:   0%|          | 0/398 [00:00<?, ?it/s]
+Loading weights: 100%|██████████| 398/398 [00:00<00:00, 9260.70it/s]
+Traceback (most recent call last):
+  File "/tmp/stream265-fault-gpu.py", line 55, in <module>
+    new=b.submit(PDRequest('reuse',prompts[0],16));drain(b)
+  File "/tmp/stream265-fault-gpu.py", line 35, in drain
+    batch.step()
+  File "/home/gspark/workspace/aicomp-rngd-stream-265/opt_prime/opt_prime/accel/pd/pd_batching.py", line 602, in step
+    self._admit_prefilled(block=not self.decoding)
+  File "/home/gspark/workspace/aicomp-rngd-stream-265/opt_prime/opt_prime/accel/pd/pd_batching.py", line 637, in _admit_prefilled
+    self._prefill_ready.wait(timeout=0.05)
+  File "/usr/lib/python3.10/threading.py", line 607, in wait
+    signaled = self._cond.wait(timeout)
+  File "/usr/lib/python3.10/threading.py", line 324, in wait
+    gotit = waiter.acquire(True, timeout)
+KeyboardInterrupt
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen4b-gpu.json b/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen4b-gpu.json
new file mode 100644
index 00000000..bf89232a
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen4b-gpu.json
@@ -0,0 +1,91 @@
+{
+  "model": "qwen4b",
+  "width": 128,
+  "cases": [
+    {
+      "stream_layers": false,
+      "tokens": [
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+      "stats": {
+        "iterations": 13,
+        "prefill_chunks": 0,
+        "decode_steps": 8,
+        "decode_rows": 14,
+        "progress_spans": 0,
+        "prefills": 2,
+        "transferred_tokens": 142,
+        "decode_cached_tokens": 0,
+        "prefill_cached_tokens": 0,
+        "prefill_s": 0.3816630680230446,
+        "transfer_s": 0.0
+      },
+      "idle": true
+    },
+    {
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+      "stats": {
+        "iterations": 50,
+        "prefill_chunks": 0,
+        "decode_steps": 9,
+        "decode_rows": 14,
+        "progress_spans": 72,
+        "prefills": 2,
+        "transferred_tokens": 142,
+        "decode_cached_tokens": 0,
+        "prefill_cached_tokens": 0,
+        "prefill_s": 0.40945649496279657,
+        "transfer_s": 0.0
+      },
+      "idle": true
+    }
+  ],
+  "scope": "same-route real RNGD to GPU token parity, not cross-backend quality or speedup",
+  "source_sha256": {
+    "prefill_server.py": "5af1169ff5c6bfac6ce265ea3edf7f6a031c207bda7ce7bcbc996d7133576b1d",
+    "streaming_factories.py": "77745350973cc54bf508e0c8b473efc442afef20b1c64f7c4edc179ea1471389"
+  },
+  "tokens_exact": true,
+  "stage": "complete"
+}
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen4b-gpu.log b/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen4b-gpu.log
new file mode 100644
index 00000000..9554bc40
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen4b-gpu.log
@@ -0,0 +1,8 @@
+loading GPU qwen4b
+
+Loading weights:   0%|          | 0/398 [00:00<?, ?it/s]
+Loading weights: 100%|██████████| 398/398 [00:00<00:00, 8443.90it/s]
+start mode False
+{"stream_layers": false, "tokens": [[3555, 374, 279, 6722, 315, 4787, 11862, 1939], [374, 279, 6722, 315, 4882, 11862, 13, 4504]], "stats": {"iterations": 13, "prefill_chunks": 0, "decode_steps": 8, "decode_rows": 14, "progress_spans": 0, "prefills": 2, "transferred_tokens": 142, "decode_cached_tokens": 0, "prefill_cached_tokens": 0, "prefill_s": 0.3816630680230446, "transfer_s": 0.0}, "idle": true}
+start mode True
+{"stream_layers": true, "tokens": [[3555, 374, 279, 6722, 315, 4787, 11862, 1939], [374, 279, 6722, 315, 4882, 11862, 13, 4504]], "stats": {"iterations": 50, "prefill_chunks": 0, "decode_steps": 9, "decode_rows": 14, "progress_spans": 72, "prefills": 2, "transferred_tokens": 142, "decode_cached_tokens": 0, "prefill_cached_tokens": 0, "prefill_s": 0.40945649496279657, "transfer_s": 0.0}, "idle": true}
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen4b-http-c16-native.json b/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen4b-http-c16-native.json
new file mode 100644
index 00000000..0f8b80cd
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen4b-http-c16-native.json
@@ -0,0 +1,25222 @@
+{
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+  "model": "Qwen3-4B",
+  "mode": "native",
+  "dtype": "bfloat16",
+  "torch": "2.13.0+cu130",
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+      "text": " The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nOkay, so the user is asking about the capital of France,",
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+      "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user also provided a",
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+    },
+    {
+      "id": "warm-0-512-128",
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+      "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user also provided a bunch of repeated background context about boxes containing apples and books. That seems unrelated. Maybe they're testing if I can ignore irrelevant information? Or perhaps they included that as part of a scenario for a different question? Let me check again.\n\nThe user's question was straightforward: \"What is the capital of France?\" and the answer is Paris. The repeated context about boxes might be a red herring, or maybe it's part of a larger problem where the answer requires considering that context",
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+      "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user provided a bunch",
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+      "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user provided a bunch of repeated background context about boxes containing apples and books. That seems unrelated. Maybe they're testing if I can ignore irrelevant information? Or maybe there's a trick here. Let me think.\n\nThe background context is just repeated lines about red and blue boxes. The question is about the capital of France. The answer is Paris. The user might be checking if I can focus on the actual question despite the irrelevant context. Or perhaps they want to see if I can handle multiple contexts and",
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+      "text": " The capital of France is Paris.\nOkay, so the user asked a question about the capital of France, and I provided the answer Paris. But looking back at",
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+      "text": " The capital of France is Paris.\nOkay, so the user asked a question about the capital of France, and I provided the answer Paris. But looking back at the context given, it's all about boxes containing apples and books. There's no mention of France, capitals, or any related information. \n\nHmm, the user might have thought that the previous context would provide some clues, but it's actually unrelated. Maybe they tested if I would use the given context to answer the question. But since the context doesn't have any information about France or capitals, I had to rely on my own knowledge.\n\nI should check if there's any",
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+          "text": " The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nOkay, so the user is asking about the capital of France,",
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+          "text": " The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nOkay, so the user is asking about the capital of France,",
+          "tokens_equal": true
+        }
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+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user also provided a",
+          "tokens_equal": true
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+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user also provided a",
+          "tokens_equal": true
+        }
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+    },
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+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user also provided a bunch of repeated background context about boxes containing apples and books. That seems unrelated. Maybe they're testing if I can ignore irrelevant information? Or perhaps they included that as part of a scenario for a different question? Let me check again.\n\nThe user's question was straightforward: \"What is the capital of France?\" and the answer is Paris. The repeated context about boxes might be a red herring, or maybe it's part of a larger problem where the answer requires considering that context",
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+          "text": " The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nOkay, so the user is asking about the capital of France,",
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+          "text": " The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nOkay, so the user is asking about the capital of France,",
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+          "text": " The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nOkay, so the user is asking about the capital of France,",
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+          "text": " The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nOkay, so the user is asking about the capital of France,",
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+          "text": " The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nOkay, so the user is asking about the capital of France,",
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+          "text": " The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nOkay, so the user is asking about the capital of France,",
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+          "text": " The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nOkay, so the user is asking about the capital of France,",
+          "text_equal": true,
+          "first_sse_s": 6.986937259032857,
+          "tpot_est_s": 0.02703722044880553,
+          "e2e_s": 10.42066425603116
+        },
+        {
+          "id": "r0-n128-o128-c16-13",
+          "endpoint": 0,
+          "input_tokens": 128,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nOkay, so the user is asking about the capital of France,",
+          "text_equal": true,
+          "first_sse_s": 3.3904667400056496,
+          "tpot_est_s": 0.027382090803250436,
+          "e2e_s": 6.867992272018455
+        },
+        {
+          "id": "r0-n128-o128-c16-14",
+          "endpoint": 0,
+          "input_tokens": 128,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nOkay, so the user is asking about the capital of France,",
+          "text_equal": true,
+          "first_sse_s": 10.494665795005858,
+          "tpot_est_s": 0.02681148585021921,
+          "e2e_s": 13.899724497983698
+        },
+        {
+          "id": "r0-n128-o128-c16-15",
+          "endpoint": 0,
+          "input_tokens": 128,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nOkay, so the user is asking about the capital of France,",
+          "text_equal": true,
+          "first_sse_s": 10.395063627976924,
+          "tpot_est_s": 0.02714665226755649,
+          "e2e_s": 13.842688465956599
+        }
+      ]
+    },
+    {
+      "repeat": 0,
+      "input_tokens": 512,
+      "output_tokens_per_request": 32,
+      "concurrency": 16,
+      "summary": {
+        "requests": 16,
+        "output_tokens": 512,
+        "wall_s": 4.569536221039016,
+        "output_tok_s": 112.04638178436018,
+        "median_first_sse_s": 1.915065808017971,
+        "median_tpot_est_s": 0.03270719445220405,
+        "median_e2e_s": 2.933246725500794,
+        "all_text_equal": true
+      },
+      "rows": [
+        {
+          "id": "r0-n512-o32-c16-0",
+          "endpoint": 0,
+          "input_tokens": 512,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user also provided a",
+          "text_equal": true,
+          "first_sse_s": 2.64383578998968,
+          "tpot_est_s": 0.032095056806405585,
+          "e2e_s": 3.638782550988253
+        },
+        {
+          "id": "r0-n512-o32-c16-1",
+          "endpoint": 0,
+          "input_tokens": 512,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user also provided a",
+          "text_equal": true,
+          "first_sse_s": 1.175247750012204,
+          "tpot_est_s": 0.03361714264610782,
+          "e2e_s": 2.2173791720415466
+        },
+        {
+          "id": "r0-n512-o32-c16-2",
+          "endpoint": 0,
+          "input_tokens": 512,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user also provided a",
+          "text_equal": true,
+          "first_sse_s": 3.613577891024761,
+          "tpot_est_s": 0.028992172161627923,
+          "e2e_s": 4.5123352280352265
+        },
+        {
+          "id": "r0-n512-o32-c16-3",
+          "endpoint": 0,
+          "input_tokens": 512,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user also provided a",
+          "text_equal": true,
+          "first_sse_s": 0.4039126959978603,
+          "tpot_est_s": 0.033360844904241425,
+          "e2e_s": 1.4380988880293444
+        },
+        {
+          "id": "r0-n512-o32-c16-4",
+          "endpoint": 0,
+          "input_tokens": 512,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user also provided a",
+          "text_equal": true,
+          "first_sse_s": 0.0660368159878999,
+          "tpot_est_s": 0.033050331322761674,
+          "e2e_s": 1.0905970869935118
+        },
+        {
+          "id": "r0-n512-o32-c16-5",
+          "endpoint": 0,
+          "input_tokens": 512,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user also provided a",
+          "text_equal": true,
+          "first_sse_s": 0.17509511398384348,
+          "tpot_est_s": 0.033130221033183437,
+          "e2e_s": 1.20213196601253
+        },
+        {
+          "id": "r0-n512-o32-c16-6",
+          "endpoint": 0,
+          "input_tokens": 512,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user also provided a",
+          "text_equal": true,
+          "first_sse_s": 1.5261262120329775,
+          "tpot_est_s": 0.033403862515584595,
+          "e2e_s": 2.5616459500161
+        },
+        {
+          "id": "r0-n512-o32-c16-7",
+          "endpoint": 0,
+          "input_tokens": 512,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user also provided a",
+          "text_equal": true,
+          "first_sse_s": 2.532558261998929,
+          "tpot_est_s": 0.032033362452151076,
+          "e2e_s": 3.5255924980156124
+        },
+        {
+          "id": "r0-n512-o32-c16-8",
+          "endpoint": 0,
+          "input_tokens": 512,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user also provided a",
+          "text_equal": true,
+          "first_sse_s": 1.2878441890352406,
+          "tpot_est_s": 0.03364577567798176,
+          "e2e_s": 2.330863235052675
+        },
+        {
+          "id": "r0-n512-o32-c16-9",
+          "endpoint": 0,
+          "input_tokens": 512,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user also provided a",
+          "text_equal": true,
+          "first_sse_s": 3.721773386991117,
+          "tpot_est_s": 0.027274915355167562,
+          "e2e_s": 4.567295763001312
+        },
+        {
+          "id": "r0-n512-o32-c16-10",
+          "endpoint": 0,
+          "input_tokens": 512,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user also provided a",
+          "text_equal": true,
+          "first_sse_s": 2.3040054040029645,
+          "tpot_est_s": 0.03228522893492012,
+          "e2e_s": 3.3048475009854883
+        },
+        {
+          "id": "r0-n512-o32-c16-11",
+          "endpoint": 0,
+          "input_tokens": 512,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user also provided a",
+          "text_equal": true,
+          "first_sse_s": 2.4210152289597318,
+          "tpot_est_s": 0.03206145335477026,
+          "e2e_s": 3.4149202829576097
+        },
+        {
+          "id": "r0-n512-o32-c16-12",
+          "endpoint": 0,
+          "input_tokens": 512,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user also provided a",
+          "text_equal": true,
+          "first_sse_s": 3.4987971169757657,
+          "tpot_est_s": 0.030741772419142145,
+          "e2e_s": 4.451792061969172
+        },
+        {
+          "id": "r0-n512-o32-c16-13",
+          "endpoint": 0,
+          "input_tokens": 512,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user also provided a",
+          "text_equal": true,
+          "first_sse_s": 1.4081759310211055,
+          "tpot_est_s": 0.03356918780651364,
+          "e2e_s": 2.4488207530230284
+        },
+        {
+          "id": "r0-n512-o32-c16-14",
+          "endpoint": 0,
+          "input_tokens": 512,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user also provided a",
+          "text_equal": true,
+          "first_sse_s": 0.28471251600421965,
+          "tpot_est_s": 0.033303344451386725,
+          "e2e_s": 1.317116193997208
+        },
+        {
+          "id": "r0-n512-o32-c16-15",
+          "endpoint": 0,
+          "input_tokens": 512,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user also provided a",
+          "text_equal": true,
+          "first_sse_s": 3.389067660958972,
+          "tpot_est_s": 0.03236405758164643,
+          "e2e_s": 4.392353445990011
+        }
+      ]
+    },
+    {
+      "repeat": 0,
+      "input_tokens": 512,
+      "output_tokens_per_request": 128,
+      "concurrency": 16,
+      "summary": {
+        "requests": 16,
+        "output_tokens": 2048,
+        "wall_s": 14.672044443024788,
+        "output_tok_s": 139.5851824163221,
+        "median_first_sse_s": 5.693408645485761,
+        "median_tpot_est_s": 0.02773696926374751,
+        "median_e2e_s": 9.253435356513364,
+        "all_text_equal": true
+      },
+      "rows": [
+        {
+          "id": "r0-n512-o128-c16-0",
+          "endpoint": 0,
+          "input_tokens": 512,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user also provided a bunch of repeated background context about boxes containing apples and books. That seems unrelated. Maybe they're testing if I can ignore irrelevant information? Or perhaps they included that as part of a scenario for a different question? Let me check again.\n\nThe user's question was straightforward: \"What is the capital of France?\" and the answer is Paris. The repeated context about boxes might be a red herring, or maybe it's part of a larger problem where the answer requires considering that context",
+          "text_equal": true,
+          "first_sse_s": 7.608199116017204,
+          "tpot_est_s": 0.0274315699682879,
+          "e2e_s": 11.092008501989767
+        },
+        {
+          "id": "r0-n512-o128-c16-1",
+          "endpoint": 0,
+          "input_tokens": 512,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user also provided a bunch of repeated background context about boxes containing apples and books. That seems unrelated. Maybe they're testing if I can ignore irrelevant information? Or perhaps they included that as part of a scenario for a different question? Let me check again.\n\nThe user's question was straightforward: \"What is the capital of France?\" and the answer is Paris. The repeated context about boxes might be a red herring, or maybe it's part of a larger problem where the answer requires considering that context",
+          "text_equal": true,
+          "first_sse_s": 4.0026573140057735,
+          "tpot_est_s": 0.02859027947273516,
+          "e2e_s": 7.633622807043139
+        },
+        {
+          "id": "r0-n512-o128-c16-2",
+          "endpoint": 0,
+          "input_tokens": 512,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user also provided a bunch of repeated background context about boxes containing apples and books. That seems unrelated. Maybe they're testing if I can ignore irrelevant information? Or perhaps they included that as part of a scenario for a different question? Let me check again.\n\nThe user's question was straightforward: \"What is the capital of France?\" and the answer is Paris. The repeated context about boxes might be a red herring, or maybe it's part of a larger problem where the answer requires considering that context",
+          "text_equal": true,
+          "first_sse_s": 7.499913125997409,
+          "tpot_est_s": 0.027423975921389978,
+          "e2e_s": 10.982758068013936
+        },
+        {
+          "id": "r0-n512-o128-c16-3",
+          "endpoint": 0,
+          "input_tokens": 512,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user also provided a bunch of repeated background context about boxes containing apples and books. That seems unrelated. Maybe they're testing if I can ignore irrelevant information? Or perhaps they included that as part of a scenario for a different question? Let me check again.\n\nThe user's question was straightforward: \"What is the capital of France?\" and the answer is Paris. The repeated context about boxes might be a red herring, or maybe it's part of a larger problem where the answer requires considering that context",
+          "text_equal": true,
+          "first_sse_s": 0.17480531299952418,
+          "tpot_est_s": 0.027743845031318055,
+          "e2e_s": 3.6982736319769174
+        },
+        {
+          "id": "r0-n512-o128-c16-4",
+          "endpoint": 0,
+          "input_tokens": 512,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user also provided a bunch of repeated background context about boxes containing apples and books. That seems unrelated. Maybe they're testing if I can ignore irrelevant information? Or perhaps they included that as part of a scenario for a different question? Let me check again.\n\nThe user's question was straightforward: \"What is the capital of France?\" and the answer is Paris. The repeated context about boxes might be a red herring, or maybe it's part of a larger problem where the answer requires considering that context",
+          "text_equal": true,
+          "first_sse_s": 10.957324624992907,
+          "tpot_est_s": 0.0280141114723854,
+          "e2e_s": 14.515116781985853
+        },
+        {
+          "id": "r0-n512-o128-c16-5",
+          "endpoint": 0,
+          "input_tokens": 512,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user also provided a bunch of repeated background context about boxes containing apples and books. That seems unrelated. Maybe they're testing if I can ignore irrelevant information? Or perhaps they included that as part of a scenario for a different question? Let me check again.\n\nThe user's question was straightforward: \"What is the capital of France?\" and the answer is Paris. The repeated context about boxes might be a red herring, or maybe it's part of a larger problem where the answer requires considering that context",
+          "text_equal": true,
+          "first_sse_s": 0.39315161400008947,
+          "tpot_est_s": 0.02773380347236067,
+          "e2e_s": 3.9153446549898945
+        },
+        {
+          "id": "r0-n512-o128-c16-6",
+          "endpoint": 0,
+          "input_tokens": 512,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user also provided a bunch of repeated background context about boxes containing apples and books. That seems unrelated. Maybe they're testing if I can ignore irrelevant information? Or perhaps they included that as part of a scenario for a different question? Let me check again.\n\nThe user's question was straightforward: \"What is the capital of France?\" and the answer is Paris. The repeated context about boxes might be a red herring, or maybe it's part of a larger problem where the answer requires considering that context",
+          "text_equal": true,
+          "first_sse_s": 3.891403470945079,
+          "tpot_est_s": 0.028620381213038955,
+          "e2e_s": 7.526191885001026
+        },
+        {
+          "id": "r0-n512-o128-c16-7",
+          "endpoint": 0,
+          "input_tokens": 512,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user also provided a bunch of repeated background context about boxes containing apples and books. That seems unrelated. Maybe they're testing if I can ignore irrelevant information? Or perhaps they included that as part of a scenario for a different question? Let me check again.\n\nThe user's question was straightforward: \"What is the capital of France?\" and the answer is Paris. The repeated context about boxes might be a red herring, or maybe it's part of a larger problem where the answer requires considering that context",
+          "text_equal": true,
+          "first_sse_s": 11.177454636024777,
+          "tpot_est_s": 0.02710132670082797,
+          "e2e_s": 14.61932312702993
+        },
+        {
+          "id": "r0-n512-o128-c16-8",
+          "endpoint": 0,
+          "input_tokens": 512,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user also provided a bunch of repeated background context about boxes containing apples and books. That seems unrelated. Maybe they're testing if I can ignore irrelevant information? Or perhaps they included that as part of a scenario for a different question? Let me check again.\n\nThe user's question was straightforward: \"What is the capital of France?\" and the answer is Paris. The repeated context about boxes might be a red herring, or maybe it's part of a larger problem where the answer requires considering that context",
+          "text_equal": true,
+          "first_sse_s": 11.285600614966825,
+          "tpot_est_s": 0.026654721425175228,
+          "e2e_s": 14.670750235964078
+        },
+        {
+          "id": "r0-n512-o128-c16-9",
+          "endpoint": 0,
+          "input_tokens": 512,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user also provided a bunch of repeated background context about boxes containing apples and books. That seems unrelated. Maybe they're testing if I can ignore irrelevant information? Or perhaps they included that as part of a scenario for a different question? Let me check again.\n\nThe user's question was straightforward: \"What is the capital of France?\" and the answer is Paris. The repeated context about boxes might be a red herring, or maybe it's part of a larger problem where the answer requires considering that context",
+          "text_equal": true,
+          "first_sse_s": 0.28529108298243955,
+          "tpot_est_s": 0.02774013505513435,
+          "e2e_s": 3.808288234984502
+        },
+        {
+          "id": "r0-n512-o128-c16-10",
+          "endpoint": 0,
+          "input_tokens": 512,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user also provided a bunch of repeated background context about boxes containing apples and books. That seems unrelated. Maybe they're testing if I can ignore irrelevant information? Or perhaps they included that as part of a scenario for a different question? Let me check again.\n\nThe user's question was straightforward: \"What is the capital of France?\" and the answer is Paris. The repeated context about boxes might be a red herring, or maybe it's part of a larger problem where the answer requires considering that context",
+          "text_equal": true,
+          "first_sse_s": 11.067861980001908,
+          "tpot_est_s": 0.02755797492921367,
+          "e2e_s": 14.567724796012044
+        },
+        {
+          "id": "r0-n512-o128-c16-11",
+          "endpoint": 0,
+          "input_tokens": 512,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user also provided a bunch of repeated background context about boxes containing apples and books. That seems unrelated. Maybe they're testing if I can ignore irrelevant information? Or perhaps they included that as part of a scenario for a different question? Let me check again.\n\nThe user's question was straightforward: \"What is the capital of France?\" and the answer is Paris. The repeated context about boxes might be a red herring, or maybe it's part of a larger problem where the answer requires considering that context",
+          "text_equal": true,
+          "first_sse_s": 3.7842316169990227,
+          "tpot_est_s": 0.02855385383444391,
+          "e2e_s": 7.410571053973399
+        },
+        {
+          "id": "r0-n512-o128-c16-12",
+          "endpoint": 0,
+          "input_tokens": 512,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user also provided a bunch of repeated background context about boxes containing apples and books. That seems unrelated. Maybe they're testing if I can ignore irrelevant information? Or perhaps they included that as part of a scenario for a different question? Let me check again.\n\nThe user's question was straightforward: \"What is the capital of France?\" and the answer is Paris. The repeated context about boxes might be a red herring, or maybe it's part of a larger problem where the answer requires considering that context",
+          "text_equal": true,
+          "first_sse_s": 7.715278921008576,
+          "tpot_est_s": 0.027444860330548167,
+          "e2e_s": 11.200776182988193
+        },
+        {
+          "id": "r0-n512-o128-c16-13",
+          "endpoint": 0,
+          "input_tokens": 512,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user also provided a bunch of repeated background context about boxes containing apples and books. That seems unrelated. Maybe they're testing if I can ignore irrelevant information? Or perhaps they included that as part of a scenario for a different question? Let me check again.\n\nThe user's question was straightforward: \"What is the capital of France?\" and the answer is Paris. The repeated context about boxes might be a red herring, or maybe it's part of a larger problem where the answer requires considering that context",
+          "text_equal": true,
+          "first_sse_s": 0.06464740401133895,
+          "tpot_est_s": 0.02774032000791443,
+          "e2e_s": 3.5876680450164713
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+        {
+          "id": "r0-n512-o128-c16-14",
+          "endpoint": 0,
+          "input_tokens": 512,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user also provided a bunch of repeated background context about boxes containing apples and books. That seems unrelated. Maybe they're testing if I can ignore irrelevant information? Or perhaps they included that as part of a scenario for a different question? Let me check again.\n\nThe user's question was straightforward: \"What is the capital of France?\" and the answer is Paris. The repeated context about boxes might be a red herring, or maybe it's part of a larger problem where the answer requires considering that context",
+          "text_equal": true,
+          "first_sse_s": 7.384159976965748,
+          "tpot_est_s": 0.027473133299353084,
+          "e2e_s": 10.87324790598359
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+        {
+          "id": "r0-n512-o128-c16-15",
+          "endpoint": 0,
+          "input_tokens": 512,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user also provided a bunch of repeated background context about boxes containing apples and books. That seems unrelated. Maybe they're testing if I can ignore irrelevant information? Or perhaps they included that as part of a scenario for a different question? Let me check again.\n\nThe user's question was straightforward: \"What is the capital of France?\" and the answer is Paris. The repeated context about boxes might be a red herring, or maybe it's part of a larger problem where the answer requires considering that context",
+          "text_equal": true,
+          "first_sse_s": 3.669728387030773,
+          "tpot_est_s": 0.028590000661247682,
+          "e2e_s": 7.300658471009228
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+    },
+    {
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+      "input_tokens": 2048,
+      "output_tokens_per_request": 32,
+      "concurrency": 16,
+      "summary": {
+        "requests": 16,
+        "output_tokens": 512,
+        "wall_s": 8.723387200967409,
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+        "median_e2e_s": 5.7151207370334305,
+        "all_text_equal": true
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+      "rows": [
+        {
+          "id": "r0-n2048-o32-c16-0",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user provided a bunch",
+          "text_equal": true,
+          "first_sse_s": 4.912231154041365,
+          "tpot_est_s": 0.05126807029028573,
+          "e2e_s": 6.5015413330402225
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+        {
+          "id": "r0-n2048-o32-c16-1",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user provided a bunch",
+          "text_equal": true,
+          "first_sse_s": 5.3755629569641314,
+          "tpot_est_s": 0.05179378719467129,
+          "e2e_s": 6.981170359998941
+        },
+        {
+          "id": "r0-n2048-o32-c16-2",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user provided a bunch",
+          "text_equal": true,
+          "first_sse_s": 3.3427450569579378,
+          "tpot_est_s": 0.05142655187181287,
+          "e2e_s": 4.936968164984137
+        },
+        {
+          "id": "r0-n2048-o32-c16-3",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user provided a bunch",
+          "text_equal": true,
+          "first_sse_s": 0.282409421983175,
+          "tpot_est_s": 0.05257582754805504,
+          "e2e_s": 1.9122600759728812
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+        {
+          "id": "r0-n2048-o32-c16-4",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user provided a bunch",
+          "text_equal": true,
+          "first_sse_s": 6.477156214008573,
+          "tpot_est_s": 0.052461864644541376,
+          "e2e_s": 8.103474017989356
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+        {
+          "id": "r0-n2048-o32-c16-5",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user provided a bunch",
+          "text_equal": true,
+          "first_sse_s": 2.381123212981038,
+          "tpot_est_s": 0.05205657635627675,
+          "e2e_s": 3.994877080025617
+        },
+        {
+          "id": "r0-n2048-o32-c16-6",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user provided a bunch",
+          "text_equal": true,
+          "first_sse_s": 1.7317893670406193,
+          "tpot_est_s": 0.05272792358018999,
+          "e2e_s": 3.366354998026509
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+        {
+          "id": "r0-n2048-o32-c16-7",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user provided a bunch",
+          "text_equal": true,
+          "first_sse_s": 0.761120135022793,
+          "tpot_est_s": 0.05306041477278115,
+          "e2e_s": 2.4059929929790087
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+        {
+          "id": "r0-n2048-o32-c16-8",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user provided a bunch",
+          "text_equal": true,
+          "first_sse_s": 2.874467011017259,
+          "tpot_est_s": 0.05137701222537867,
+          "e2e_s": 4.467154390003998
+        },
+        {
+          "id": "r0-n2048-o32-c16-9",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user provided a bunch",
+          "text_equal": true,
+          "first_sse_s": 6.955291649966966,
+          "tpot_est_s": 0.04373666616119144,
+          "e2e_s": 8.311128300963901
+        },
+        {
+          "id": "r0-n2048-o32-c16-10",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user provided a bunch",
+          "text_equal": true,
+          "first_sse_s": 7.430986250983551,
+          "tpot_est_s": 0.035055904388202414,
+          "e2e_s": 8.517719287017826
+        },
+        {
+          "id": "r0-n2048-o32-c16-11",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user provided a bunch",
+          "text_equal": true,
+          "first_sse_s": 5.853165829030331,
+          "tpot_est_s": 0.05170467183850105,
+          "e2e_s": 7.456010656023864
+        },
+        {
+          "id": "r0-n2048-o32-c16-12",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user provided a bunch",
+          "text_equal": true,
+          "first_sse_s": 7.9185318580130115,
+          "tpot_est_s": 0.025938832903310897,
+          "e2e_s": 8.72263567801565
+        },
+        {
+          "id": "r0-n2048-o32-c16-13",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user provided a bunch",
+          "text_equal": true,
+          "first_sse_s": 3.8181993450270966,
+          "tpot_est_s": 0.05103161977416265,
+          "e2e_s": 5.400179558026139
+        },
+        {
+          "id": "r0-n2048-o32-c16-14",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user provided a bunch",
+          "text_equal": true,
+          "first_sse_s": 4.442438217054587,
+          "tpot_est_s": 0.05121366770923017,
+          "e2e_s": 6.030061916040722
+        },
+        {
+          "id": "r0-n2048-o32-c16-15",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user provided a bunch",
+          "text_equal": true,
+          "first_sse_s": 1.2433890500105917,
+          "tpot_est_s": 0.05339360890361751,
+          "e2e_s": 2.8985909260227345
+        }
+      ]
+    },
+    {
+      "repeat": 0,
+      "input_tokens": 2048,
+      "output_tokens_per_request": 128,
+      "concurrency": 16,
+      "summary": {
+        "requests": 16,
+        "output_tokens": 2048,
+        "wall_s": 18.946034880995285,
+        "output_tok_s": 108.09649685878829,
+        "median_first_sse_s": 7.918753799982369,
+        "median_tpot_est_s": 0.03285254945673276,
+        "median_e2e_s": 12.062205900496338,
+        "all_text_equal": true
+      },
+      "rows": [
+        {
+          "id": "r0-n2048-o128-c16-0",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user provided a bunch of repeated background context about boxes containing apples and books. That seems unrelated. Maybe they're testing if I can ignore irrelevant information? Or maybe there's a trick here. Let me think.\n\nThe background context is just repeated lines about red and blue boxes. The question is about the capital of France. The answer is Paris. The user might be checking if I can focus on the actual question despite the irrelevant context. Or perhaps they want to see if I can handle multiple contexts and",
+          "text_equal": true,
+          "first_sse_s": 15.577113516977988,
+          "tpot_est_s": 0.026515629590309307,
+          "e2e_s": 18.94459847494727
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+        {
+          "id": "r0-n2048-o128-c16-1",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user provided a bunch of repeated background context about boxes containing apples and books. That seems unrelated. Maybe they're testing if I can ignore irrelevant information? Or maybe there's a trick here. Let me think.\n\nThe background context is just repeated lines about red and blue boxes. The question is about the capital of France. The answer is Paris. The user might be checking if I can focus on the actual question despite the irrelevant context. Or perhaps they want to see if I can handle multiple contexts and",
+          "text_equal": true,
+          "first_sse_s": 14.62131189199863,
+          "tpot_est_s": 0.03078115948037736,
+          "e2e_s": 18.530519146006554
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+        {
+          "id": "r0-n2048-o128-c16-2",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user provided a bunch of repeated background context about boxes containing apples and books. That seems unrelated. Maybe they're testing if I can ignore irrelevant information? Or maybe there's a trick here. Let me think.\n\nThe background context is just repeated lines about red and blue boxes. The question is about the capital of France. The answer is Paris. The user might be checking if I can focus on the actual question despite the irrelevant context. Or perhaps they want to see if I can handle multiple contexts and",
+          "text_equal": true,
+          "first_sse_s": 1.2373470000457019,
+          "tpot_est_s": 0.03296552210226728,
+          "e2e_s": 5.423968307033647
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+        {
+          "id": "r0-n2048-o128-c16-3",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user provided a bunch of repeated background context about boxes containing apples and books. That seems unrelated. Maybe they're testing if I can ignore irrelevant information? Or maybe there's a trick here. Let me think.\n\nThe background context is just repeated lines about red and blue boxes. The question is about the capital of France. The answer is Paris. The user might be checking if I can focus on the actual question despite the irrelevant context. Or perhaps they want to see if I can handle multiple contexts and",
+          "text_equal": true,
+          "first_sse_s": 15.096086295961868,
+          "tpot_est_s": 0.028663429897736875,
+          "e2e_s": 18.73634189297445
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+        {
+          "id": "r0-n2048-o128-c16-4",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user provided a bunch of repeated background context about boxes containing apples and books. That seems unrelated. Maybe they're testing if I can ignore irrelevant information? Or maybe there's a trick here. Let me think.\n\nThe background context is just repeated lines about red and blue boxes. The question is about the capital of France. The answer is Paris. The user might be checking if I can focus on the actual question despite the irrelevant context. Or perhaps they want to see if I can handle multiple contexts and",
+          "text_equal": true,
+          "first_sse_s": 5.875946057960391,
+          "tpot_est_s": 0.03237036978743428,
+          "e2e_s": 9.986983020964544
+        },
+        {
+          "id": "r0-n2048-o128-c16-5",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user provided a bunch of repeated background context about boxes containing apples and books. That seems unrelated. Maybe they're testing if I can ignore irrelevant information? Or maybe there's a trick here. Let me think.\n\nThe background context is just repeated lines about red and blue boxes. The question is about the capital of France. The answer is Paris. The user might be checking if I can focus on the actual question despite the irrelevant context. Or perhaps they want to see if I can handle multiple contexts and",
+          "text_equal": true,
+          "first_sse_s": 1.7204541750252247,
+          "tpot_est_s": 0.03293715836205263,
+          "e2e_s": 5.903473287005909
+        },
+        {
+          "id": "r0-n2048-o128-c16-6",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user provided a bunch of repeated background context about boxes containing apples and books. That seems unrelated. Maybe they're testing if I can ignore irrelevant information? Or maybe there's a trick here. Let me think.\n\nThe background context is just repeated lines about red and blue boxes. The question is about the capital of France. The answer is Paris. The user might be checking if I can focus on the actual question despite the irrelevant context. Or perhaps they want to see if I can handle multiple contexts and",
+          "text_equal": true,
+          "first_sse_s": 0.750814618950244,
+          "tpot_est_s": 0.03295412724406833,
+          "e2e_s": 4.935988778946921
+        },
+        {
+          "id": "r0-n2048-o128-c16-7",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user provided a bunch of repeated background context about boxes containing apples and books. That seems unrelated. Maybe they're testing if I can ignore irrelevant information? Or maybe there's a trick here. Let me think.\n\nThe background context is just repeated lines about red and blue boxes. The question is about the capital of France. The answer is Paris. The user might be checking if I can focus on the actual question despite the irrelevant context. Or perhaps they want to see if I can handle multiple contexts and",
+          "text_equal": true,
+          "first_sse_s": 10.922863283951301,
+          "tpot_est_s": 0.033067099968664436,
+          "e2e_s": 15.122384979971685
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+        {
+          "id": "r0-n2048-o128-c16-8",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user provided a bunch of repeated background context about boxes containing apples and books. That seems unrelated. Maybe they're testing if I can ignore irrelevant information? Or maybe there's a trick here. Let me think.\n\nThe background context is just repeated lines about red and blue boxes. The question is about the capital of France. The answer is Paris. The user might be checking if I can focus on the actual question despite the irrelevant context. Or perhaps they want to see if I can handle multiple contexts and",
+          "text_equal": true,
+          "first_sse_s": 9.483606458990835,
+          "tpot_est_s": 0.03285801981889284,
+          "e2e_s": 13.656574975990225
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+        {
+          "id": "r0-n2048-o128-c16-9",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user provided a bunch of repeated background context about boxes containing apples and books. That seems unrelated. Maybe they're testing if I can ignore irrelevant information? Or maybe there's a trick here. Let me think.\n\nThe background context is just repeated lines about red and blue boxes. The question is about the capital of France. The answer is Paris. The user might be checking if I can focus on the actual question despite the irrelevant context. Or perhaps they want to see if I can handle multiple contexts and",
+          "text_equal": true,
+          "first_sse_s": 9.960755502979737,
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+        {
+          "id": "r0-n2048-o128-c16-10",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user provided a bunch of repeated background context about boxes containing apples and books. That seems unrelated. Maybe they're testing if I can ignore irrelevant information? Or maybe there's a trick here. Let me think.\n\nThe background context is just repeated lines about red and blue boxes. The question is about the capital of France. The answer is Paris. The user might be checking if I can focus on the actual question despite the irrelevant context. Or perhaps they want to see if I can handle multiple contexts and",
+          "text_equal": true,
+          "first_sse_s": 6.353901140973903,
+          "tpot_est_s": 0.0323931943624295,
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+        {
+          "id": "r0-n2048-o128-c16-11",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user provided a bunch of repeated background context about boxes containing apples and books. That seems unrelated. Maybe they're testing if I can ignore irrelevant information? Or maybe there's a trick here. Let me think.\n\nThe background context is just repeated lines about red and blue boxes. The question is about the capital of France. The answer is Paris. The user might be checking if I can focus on the actual question despite the irrelevant context. Or perhaps they want to see if I can handle multiple contexts and",
+          "text_equal": true,
+          "first_sse_s": 10.439850185997784,
+          "tpot_est_s": 0.03313262374818648,
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+        {
+          "id": "r0-n2048-o128-c16-12",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user provided a bunch of repeated background context about boxes containing apples and books. That seems unrelated. Maybe they're testing if I can ignore irrelevant information? Or maybe there's a trick here. Let me think.\n\nThe background context is just repeated lines about red and blue boxes. The question is about the capital of France. The answer is Paris. The user might be checking if I can focus on the actual question despite the irrelevant context. Or perhaps they want to see if I can handle multiple contexts and",
+          "text_equal": true,
+          "first_sse_s": 5.396758123009931,
+          "tpot_est_s": 0.03240426611815264,
+          "e2e_s": 9.512099920015316
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+        {
+          "id": "r0-n2048-o128-c16-13",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user provided a bunch of repeated background context about boxes containing apples and books. That seems unrelated. Maybe they're testing if I can ignore irrelevant information? Or maybe there's a trick here. Let me think.\n\nThe background context is just repeated lines about red and blue boxes. The question is about the capital of France. The answer is Paris. The user might be checking if I can focus on the actual question despite the irrelevant context. Or perhaps they want to see if I can handle multiple contexts and",
+          "text_equal": true,
+          "first_sse_s": 14.122728226007894,
+          "tpot_est_s": 0.033039583015625956,
+          "e2e_s": 18.31875526899239
+        },
+        {
+          "id": "r0-n2048-o128-c16-14",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user provided a bunch of repeated background context about boxes containing apples and books. That seems unrelated. Maybe they're testing if I can ignore irrelevant information? Or maybe there's a trick here. Let me think.\n\nThe background context is just repeated lines about red and blue boxes. The question is about the capital of France. The answer is Paris. The user might be checking if I can focus on the actual question despite the irrelevant context. Or perhaps they want to see if I can handle multiple contexts and",
+          "text_equal": true,
+          "first_sse_s": 0.2790607929928228,
+          "tpot_est_s": 0.032847079094572684,
+          "e2e_s": 4.4506398380035535
+        },
+        {
+          "id": "r0-n2048-o128-c16-15",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user provided a bunch of repeated background context about boxes containing apples and books. That seems unrelated. Maybe they're testing if I can ignore irrelevant information? Or maybe there's a trick here. Let me think.\n\nThe background context is just repeated lines about red and blue boxes. The question is about the capital of France. The answer is Paris. The user might be checking if I can focus on the actual question despite the irrelevant context. Or perhaps they want to see if I can handle multiple contexts and",
+          "text_equal": true,
+          "first_sse_s": 4.9087484839838,
+          "tpot_est_s": 0.03247213537812116,
+          "e2e_s": 9.032709677005187
+        }
+      ]
+    },
+    {
+      "repeat": 0,
+      "input_tokens": 8192,
+      "output_tokens_per_request": 32,
+      "concurrency": 16,
+      "summary": {
+        "requests": 16,
+        "output_tokens": 512,
+        "wall_s": 39.62104910501512,
+        "output_tok_s": 12.922424104494308,
+        "median_first_sse_s": 20.26316969646723,
+        "median_tpot_est_s": 0.0748900020647148,
+        "median_e2e_s": 22.59124415399856,
+        "all_text_equal": true
+      },
+      "rows": [
+        {
+          "id": "r0-n8192-o32-c16-0",
+          "endpoint": 0,
+          "input_tokens": 8192,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user asked a question about the capital of France, and I provided the answer Paris. But looking back at",
+          "text_equal": true,
+          "first_sse_s": 23.9809222050244,
+          "tpot_est_s": 0.07587647848319443,
+          "e2e_s": 26.333093038003426
+        },
+        {
+          "id": "r0-n8192-o32-c16-1",
+          "endpoint": 0,
+          "input_tokens": 8192,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user asked a question about the capital of France, and I provided the answer Paris. But looking back at",
+          "text_equal": true,
+          "first_sse_s": 16.529001058021095,
+          "tpot_est_s": 0.07542486787180326,
+          "e2e_s": 18.867171962046996
+        },
+        {
+          "id": "r0-n8192-o32-c16-2",
+          "endpoint": 0,
+          "input_tokens": 8192,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user asked a question about the capital of France, and I provided the answer Paris. But looking back at",
+          "text_equal": true,
+          "first_sse_s": 9.094621126016136,
+          "tpot_est_s": 0.0748134944193636,
+          "e2e_s": 11.413839453016408
+        },
+        {
+          "id": "r0-n8192-o32-c16-3",
+          "endpoint": 0,
+          "input_tokens": 8192,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user asked a question about the capital of France, and I provided the answer Paris. But looking back at",
+          "text_equal": true,
+          "first_sse_s": 4.1549352800357156,
+          "tpot_est_s": 0.07521456548373305,
+          "e2e_s": 6.48658681003144
+        },
+        {
+          "id": "r0-n8192-o32-c16-4",
+          "endpoint": 0,
+          "input_tokens": 8192,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user asked a question about the capital of France, and I provided the answer Paris. But looking back at",
+          "text_equal": true,
+          "first_sse_s": 36.38583693502005,
+          "tpot_est_s": 0.0744297101928462,
+          "e2e_s": 38.693157950998284
+        },
+        {
+          "id": "r0-n8192-o32-c16-5",
+          "endpoint": 0,
+          "input_tokens": 8192,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user asked a question about the capital of France, and I provided the answer Paris. But looking back at",
+          "text_equal": true,
+          "first_sse_s": 19.019455803965684,
+          "tpot_est_s": 0.07532249558191266,
+          "e2e_s": 21.354453167004976
+        },
+        {
+          "id": "r0-n8192-o32-c16-6",
+          "endpoint": 0,
+          "input_tokens": 8192,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user asked a question about the capital of France, and I provided the answer Paris. But looking back at",
+          "text_equal": true,
+          "first_sse_s": 38.84572846698575,
+          "tpot_est_s": 0.024975125257286332,
+          "e2e_s": 39.61995734996162
+        },
+        {
+          "id": "r0-n8192-o32-c16-7",
+          "endpoint": 0,
+          "input_tokens": 8192,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user asked a question about the capital of France, and I provided the answer Paris. But looking back at",
+          "text_equal": true,
+          "first_sse_s": 1.7072844119975343,
+          "tpot_est_s": 0.0740433327418061,
+          "e2e_s": 4.0026277269935235
+        },
+        {
+          "id": "r0-n8192-o32-c16-8",
+          "endpoint": 0,
+          "input_tokens": 8192,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user asked a question about the capital of France, and I provided the answer Paris. But looking back at",
+          "text_equal": true,
+          "first_sse_s": 6.638072646979708,
+          "tpot_est_s": 0.07435913603272169,
+          "e2e_s": 8.94320586399408
+        },
+        {
+          "id": "r0-n8192-o32-c16-9",
+          "endpoint": 0,
+          "input_tokens": 8192,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user asked a question about the capital of France, and I provided the answer Paris. But looking back at",
+          "text_equal": true,
+          "first_sse_s": 11.565592161030509,
+          "tpot_est_s": 0.07501355483714912,
+          "e2e_s": 13.891012360982131
+        },
+        {
+          "id": "r0-n8192-o32-c16-10",
+          "endpoint": 0,
+          "input_tokens": 8192,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user asked a question about the capital of France, and I provided the answer Paris. But looking back at",
+          "text_equal": true,
+          "first_sse_s": 14.042337581980973,
+          "tpot_est_s": 0.07531947445222026,
+          "e2e_s": 16.3772412899998
+        },
+        {
+          "id": "r0-n8192-o32-c16-11",
+          "endpoint": 0,
+          "input_tokens": 8192,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user asked a question about the capital of France, and I provided the answer Paris. But looking back at",
+          "text_equal": true,
+          "first_sse_s": 26.482766409986652,
+          "tpot_est_s": 0.07460742122531237,
+          "e2e_s": 28.795596467971336
+        },
+        {
+          "id": "r0-n8192-o32-c16-12",
+          "endpoint": 0,
+          "input_tokens": 8192,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user asked a question about the capital of France, and I provided the answer Paris. But looking back at",
+          "text_equal": true,
+          "first_sse_s": 31.42267553100828,
+          "tpot_est_s": 0.07543690099884125,
+          "e2e_s": 33.76121946197236
+        },
+        {
+          "id": "r0-n8192-o32-c16-13",
+          "endpoint": 0,
+          "input_tokens": 8192,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user asked a question about the capital of France, and I provided the answer Paris. But looking back at",
+          "text_equal": true,
+          "first_sse_s": 21.506883588968776,
+          "tpot_est_s": 0.07487585651688278,
+          "e2e_s": 23.828035140992142
+        },
+        {
+          "id": "r0-n8192-o32-c16-14",
+          "endpoint": 0,
+          "input_tokens": 8192,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user asked a question about the capital of France, and I provided the answer Paris. But looking back at",
+          "text_equal": true,
+          "first_sse_s": 33.91302050894592,
+          "tpot_est_s": 0.07477850003370774,
+          "e2e_s": 36.23115400999086
+        },
+        {
+          "id": "r0-n8192-o32-c16-15",
+          "endpoint": 0,
+          "input_tokens": 8192,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user asked a question about the capital of France, and I provided the answer Paris. But looking back at",
+          "text_equal": true,
+          "first_sse_s": 28.948478805017658,
+          "tpot_est_s": 0.07490414761254684,
+          "e2e_s": 31.27050738100661
+        }
+      ]
+    },
+    {
+      "repeat": 0,
+      "input_tokens": 8192,
+      "output_tokens_per_request": 128,
+      "concurrency": 16,
+      "summary": {
+        "requests": 16,
+        "output_tokens": 2048,
+        "wall_s": 45.05022216500947,
+        "output_tok_s": 45.460375145290236,
+        "median_first_sse_s": 21.938469171494944,
+        "median_tpot_est_s": 0.06598407014545354,
+        "median_e2e_s": 30.318446079967543,
+        "all_text_equal": true
+      },
+      "rows": [
+        {
+          "id": "r0-n8192-o128-c16-0",
+          "endpoint": 0,
+          "input_tokens": 8192,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nOkay, so the user asked a question about the capital of France, and I provided the answer Paris. But looking back at the context given, it's all about boxes containing apples and books. There's no mention of France, capitals, or any related information. \n\nHmm, the user might have thought that the previous context would provide some clues, but it's actually unrelated. Maybe they tested if I would use the given context to answer the question. But since the context doesn't have any information about France or capitals, I had to rely on my own knowledge.\n\nI should check if there's any",
+          "text_equal": true,
+          "first_sse_s": 41.85394499701215,
+          "tpot_est_s": 0.025147551393382046,
+          "e2e_s": 45.04768402397167
+        },
+        {
+          "id": "r0-n8192-o128-c16-1",
+          "endpoint": 0,
+          "input_tokens": 8192,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nOkay, so the user asked a question about the capital of France, and I provided the answer Paris. But looking back at the context given, it's all about boxes containing apples and books. There's no mention of France, capitals, or any related information. \n\nHmm, the user might have thought that the previous context would provide some clues, but it's actually unrelated. Maybe they tested if I would use the given context to answer the question. But since the context doesn't have any information about France or capitals, I had to rely on my own knowledge.\n\nI should check if there's any",
+          "text_equal": true,
+          "first_sse_s": 39.36142082099104,
+          "tpot_est_s": 0.03870184014974837,
+          "e2e_s": 44.276554520009086
+        },
+        {
+          "id": "r0-n8192-o128-c16-2",
+          "endpoint": 0,
+          "input_tokens": 8192,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nOkay, so the user asked a question about the capital of France, and I provided the answer Paris. But looking back at the context given, it's all about boxes containing apples and books. There's no mention of France, capitals, or any related information. \n\nHmm, the user might have thought that the previous context would provide some clues, but it's actually unrelated. Maybe they tested if I would use the given context to answer the question. But since the context doesn't have any information about France or capitals, I had to rely on my own knowledge.\n\nI should check if there's any",
+          "text_equal": true,
+          "first_sse_s": 9.355377744010184,
+          "tpot_est_s": 0.06640846916523901,
+          "e2e_s": 17.78925332799554
+        },
+        {
+          "id": "r0-n8192-o128-c16-3",
+          "endpoint": 0,
+          "input_tokens": 8192,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nOkay, so the user asked a question about the capital of France, and I provided the answer Paris. But looking back at the context given, it's all about boxes containing apples and books. There's no mention of France, capitals, or any related information. \n\nHmm, the user might have thought that the previous context would provide some clues, but it's actually unrelated. Maybe they tested if I would use the given context to answer the question. But since the context doesn't have any information about France or capitals, I had to rely on my own knowledge.\n\nI should check if there's any",
+          "text_equal": true,
+          "first_sse_s": 1.7372048040269874,
+          "tpot_est_s": 0.06656237629904067,
+          "e2e_s": 10.190626594005153
+        },
+        {
+          "id": "r0-n8192-o128-c16-4",
+          "endpoint": 0,
+          "input_tokens": 8192,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nOkay, so the user asked a question about the capital of France, and I provided the answer Paris. But looking back at the context given, it's all about boxes containing apples and books. There's no mention of France, capitals, or any related information. \n\nHmm, the user might have thought that the previous context would provide some clues, but it's actually unrelated. Maybe they tested if I would use the given context to answer the question. But since the context doesn't have any information about France or capitals, I had to rely on my own knowledge.\n\nI should check if there's any",
+          "text_equal": true,
+          "first_sse_s": 4.2486449659918435,
+          "tpot_est_s": 0.06667365134633182,
+          "e2e_s": 12.716198686975986
+        },
+        {
+          "id": "r0-n8192-o128-c16-5",
+          "endpoint": 0,
+          "input_tokens": 8192,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nOkay, so the user asked a question about the capital of France, and I provided the answer Paris. But looking back at the context given, it's all about boxes containing apples and books. There's no mention of France, capitals, or any related information. \n\nHmm, the user might have thought that the previous context would provide some clues, but it's actually unrelated. Maybe they tested if I would use the given context to answer the question. But since the context doesn't have any information about France or capitals, I had to rely on my own knowledge.\n\nI should check if there's any",
+          "text_equal": true,
+          "first_sse_s": 26.13805103098275,
+          "tpot_est_s": 0.06528803956691234,
+          "e2e_s": 34.42963205598062
+        },
+        {
+          "id": "r0-n8192-o128-c16-6",
+          "endpoint": 0,
+          "input_tokens": 8192,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nOkay, so the user asked a question about the capital of France, and I provided the answer Paris. But looking back at the context given, it's all about boxes containing apples and books. There's no mention of France, capitals, or any related information. \n\nHmm, the user might have thought that the previous context would provide some clues, but it's actually unrelated. Maybe they tested if I would use the given context to answer the question. But since the context doesn't have any information about France or capitals, I had to rely on my own knowledge.\n\nI should check if there's any",
+          "text_equal": true,
+          "first_sse_s": 6.785654508974403,
+          "tpot_est_s": 0.06639447410253617,
+          "e2e_s": 15.217752719996497
+        },
+        {
+          "id": "r0-n8192-o128-c16-7",
+          "endpoint": 0,
+          "input_tokens": 8192,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nOkay, so the user asked a question about the capital of France, and I provided the answer Paris. But looking back at the context given, it's all about boxes containing apples and books. There's no mention of France, capitals, or any related information. \n\nHmm, the user might have thought that the previous context would provide some clues, but it's actually unrelated. Maybe they tested if I would use the given context to answer the question. But since the context doesn't have any information about France or capitals, I had to rely on my own knowledge.\n\nI should check if there's any",
+          "text_equal": true,
+          "first_sse_s": 28.65416253497824,
+          "tpot_est_s": 0.0649113925279294,
+          "e2e_s": 36.897909386025276
+        },
+        {
+          "id": "r0-n8192-o128-c16-8",
+          "endpoint": 0,
+          "input_tokens": 8192,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nOkay, so the user asked a question about the capital of France, and I provided the answer Paris. But looking back at the context given, it's all about boxes containing apples and books. There's no mention of France, capitals, or any related information. \n\nHmm, the user might have thought that the previous context would provide some clues, but it's actually unrelated. Maybe they tested if I would use the given context to answer the question. But since the context doesn't have any information about France or capitals, I had to rely on my own knowledge.\n\nI should check if there's any",
+          "text_equal": true,
+          "first_sse_s": 15.191874898970127,
+          "tpot_est_s": 0.06632967073998348,
+          "e2e_s": 23.61574308294803
+        },
+        {
+          "id": "r0-n8192-o128-c16-9",
+          "endpoint": 0,
+          "input_tokens": 8192,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nOkay, so the user asked a question about the capital of France, and I provided the answer Paris. But looking back at the context given, it's all about boxes containing apples and books. There's no mention of France, capitals, or any related information. \n\nHmm, the user might have thought that the previous context would provide some clues, but it's actually unrelated. Maybe they tested if I would use the given context to answer the question. But since the context doesn't have any information about France or capitals, I had to rely on my own knowledge.\n\nI should check if there's any",
+          "text_equal": true,
+          "first_sse_s": 31.15716403600527,
+          "tpot_est_s": 0.06481024662195888,
+          "e2e_s": 39.38806535699405
+        },
+        {
+          "id": "r0-n8192-o128-c16-10",
+          "endpoint": 0,
+          "input_tokens": 8192,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nOkay, so the user asked a question about the capital of France, and I provided the answer Paris. But looking back at the context given, it's all about boxes containing apples and books. There's no mention of France, capitals, or any related information. \n\nHmm, the user might have thought that the previous context would provide some clues, but it's actually unrelated. Maybe they tested if I would use the given context to answer the question. But since the context doesn't have any information about France or capitals, I had to rely on my own knowledge.\n\nI should check if there's any",
+          "text_equal": true,
+          "first_sse_s": 20.288687633990776,
+          "tpot_est_s": 0.06605808407079253,
+          "e2e_s": 28.678064310981426
+        },
+        {
+          "id": "r0-n8192-o128-c16-11",
+          "endpoint": 0,
+          "input_tokens": 8192,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nOkay, so the user asked a question about the capital of France, and I provided the answer Paris. But looking back at the context given, it's all about boxes containing apples and books. There's no mention of France, capitals, or any related information. \n\nHmm, the user might have thought that the previous context would provide some clues, but it's actually unrelated. Maybe they tested if I would use the given context to answer the question. But since the context doesn't have any information about France or capitals, I had to rely on my own knowledge.\n\nI should check if there's any",
+          "text_equal": true,
+          "first_sse_s": 12.69270219898317,
+          "tpot_est_s": 0.06621127395283827,
+          "e2e_s": 21.10153399099363
+        },
+        {
+          "id": "r0-n8192-o128-c16-12",
+          "endpoint": 0,
+          "input_tokens": 8192,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nOkay, so the user asked a question about the capital of France, and I provided the answer Paris. But looking back at the context given, it's all about boxes containing apples and books. There's no mention of France, capitals, or any related information. \n\nHmm, the user might have thought that the previous context would provide some clues, but it's actually unrelated. Maybe they tested if I would use the given context to answer the question. But since the context doesn't have any information about France or capitals, I had to rely on my own knowledge.\n\nI should check if there's any",
+          "text_equal": true,
+          "first_sse_s": 17.76059662498301,
+          "tpot_est_s": 0.06616642996879646,
+          "e2e_s": 26.16373323102016
+        },
+        {
+          "id": "r0-n8192-o128-c16-13",
+          "endpoint": 0,
+          "input_tokens": 8192,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nOkay, so the user asked a question about the capital of France, and I provided the answer Paris. But looking back at the context given, it's all about boxes containing apples and books. There's no mention of France, capitals, or any related information. \n\nHmm, the user might have thought that the previous context would provide some clues, but it's actually unrelated. Maybe they tested if I would use the given context to answer the question. But since the context doesn't have any information about France or capitals, I had to rely on my own knowledge.\n\nI should check if there's any",
+          "text_equal": true,
+          "first_sse_s": 23.588250708999112,
+          "tpot_est_s": 0.06591005622011456,
+          "e2e_s": 31.95882784895366
+        },
+        {
+          "id": "r0-n8192-o128-c16-14",
+          "endpoint": 0,
+          "input_tokens": 8192,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nOkay, so the user asked a question about the capital of France, and I provided the answer Paris. But looking back at the context given, it's all about boxes containing apples and books. There's no mention of France, capitals, or any related information. \n\nHmm, the user might have thought that the previous context would provide some clues, but it's actually unrelated. Maybe they tested if I would use the given context to answer the question. But since the context doesn't have any information about France or capitals, I had to rely on my own knowledge.\n\nI should check if there's any",
+          "text_equal": true,
+          "first_sse_s": 36.87457464000909,
+          "tpot_est_s": 0.05201527501564605,
+          "e2e_s": 43.480514566996135
+        },
+        {
+          "id": "r0-n8192-o128-c16-15",
+          "endpoint": 0,
+          "input_tokens": 8192,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nOkay, so the user asked a question about the capital of France, and I provided the answer Paris. But looking back at the context given, it's all about boxes containing apples and books. There's no mention of France, capitals, or any related information. \n\nHmm, the user might have thought that the previous context would provide some clues, but it's actually unrelated. Maybe they tested if I would use the given context to answer the question. But since the context doesn't have any information about France or capitals, I had to rely on my own knowledge.\n\nI should check if there's any",
+          "text_equal": true,
+          "first_sse_s": 34.405676216003485,
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+  "harness_sha256": "cfd4821e2a2112f3fcf7f242f3e76efe47134640e4bf22b3b63fb808be6148f0",
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+  "physical_gpus": 1,
+  "physical_rngd": 0,
+  "physical_atom": 0,
+  "slots_per_decoder": 4,
+  "producer_cache": false,
+  "decoder_cache": false,
+  "producer_chunk": 256,
+  "local_chunk": 256,
+  "context": 9216,
+  "server_config": {
+    "servers": [
+      [
+        "/home/gspark/venvs/pt213/bin/python",
+        "-c",
+        "import sys,importlib.util,runpy\ns=importlib.util.spec_from_file_location('opt_prime.accel.serve.llmd','/tmp/llmd240.py')\nm=importlib.util.module_from_spec(s);sys.modules[s.name]=m;s.loader.exec_module(m)\nrunpy.run_path('/home/gspark/workspace/aicomp-rngd-stream-265/opt_prime/examples/pd/serve_batched.py',run_name='__main__')\n",
+        "--model",
+        "/tmp/serving-matrix-models/Qwen3-4B",
+        "--device",
+        "cuda:0",
+        "--dtype",
+        "bfloat16",
+        "--slots",
+        "4",
+        "--max-context",
+        "9216",
+        "--max-output",
+        "128",
+        "--kv",
+        "paged",
+        "--page-size",
+        "64",
+        "--chunk",
+        "256",
+        "--port",
+        "{port}"
+      ]
+    ],
+    "metadata": {
+      "physical_gpus": 1,
+      "physical_rngd": 0,
+      "physical_atom": 0,
+      "slots_per_decoder": 4,
+      "producer_cache": false,
+      "decoder_cache": false,
+      "producer_chunk": 256,
+      "local_chunk": 256,
+      "context": 9216
+    }
+  },
+  "load_s": 4.9817172640468925,
+  "references": {
+    "128:32": {
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+      "text": " The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of"
+    },
+    "128:128": {
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+      "text": " The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nOkay, so the user is asking about the capital of France,"
+    },
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+      "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user also provided a"
+    },
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+      "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user also provided a bunch of repeated background context about boxes containing apples and books. That seems unrelated. Maybe they're testing if I can ignore irrelevant information? Or perhaps they included that as part of a scenario for a different question? Let me check again.\n\nThe user's question was straightforward: \"What is the capital of France?\" and the answer is Paris. The repeated context about boxes might be a red herring, or maybe it's part of a larger problem where the answer requires considering that context"
+    },
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+      "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user provided a bunch"
+    },
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+      "text": " The capital of France is Paris.\nOkay, so the user asked a question about the capital of France, and I provided the answer Paris. But looking back at the context given, it's all about boxes containing apples and books. There's no mention of France, capitals, or any related information. \n\nHmm, the user might have thought that the previous context would provide some clues, but it's actually unrelated. Maybe they tested if I would use the given context to answer the question. But since the context doesn't have any information about France or capitals, I had to rely on my own knowledge.\n\nI should check if there's any"
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airm_stage_request_failures_total counter\nairm_stage_request_failures_total{model_name=\"Qwen3-4B\"} 0\n# HELP vllm:prompt_tokens_total Prompt tokens of finished requests.\n# TYPE vllm:prompt_tokens_total counter\nvllm:prompt_tokens_total{model_name=\"Qwen3-4B\"} 718080\n# HELP vllm:generation_tokens_total Generated tokens of finished requests.\n# TYPE vllm:generation_tokens_total counter\nvllm:generation_tokens_total{model_name=\"Qwen3-4B\"} 21120\n# HELP vllm:time_to_first_token_seconds Time to first token (prefill stage: fenced prefill).\n# TYPE vllm:time_to_first_token_seconds histogram\nvllm:time_to_first_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.001\"} 0\nvllm:time_to_first_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.005\"} 0\nvllm:time_to_first_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.01\"} 0\nvllm:time_to_first_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.02\"} 0\nvllm:time_to_first_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.04\"} 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264\nvllm:time_to_first_token_seconds_sum{model_name=\"Qwen3-4B\"} 2102.4211762598716\nvllm:time_to_first_token_seconds_count{model_name=\"Qwen3-4B\"} 264\n# HELP vllm:time_per_output_token_seconds Time per output token after the first (decode stage).\n# TYPE vllm:time_per_output_token_seconds histogram\nvllm:time_per_output_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.01\"} 0\nvllm:time_per_output_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.025\"} 10\nvllm:time_per_output_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.05\"} 179\nvllm:time_per_output_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.075\"} 257\nvllm:time_per_output_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.1\"} 264\nvllm:time_per_output_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.15\"} 264\nvllm:time_per_output_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.2\"} 264\nvllm:time_per_output_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.3\"} 264\nvllm:time_per_output_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.4\"} 264\nvllm:time_per_output_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.5\"} 264\nvllm:time_per_output_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.75\"} 264\nvllm:time_per_output_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"1.0\"} 264\nvllm:time_per_output_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"2.5\"} 264\nvllm:time_per_output_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"+Inf\"} 264\nvllm:time_per_output_token_seconds_sum{model_name=\"Qwen3-4B\"} 10.57928421453964\nvllm:time_per_output_token_seconds_count{model_name=\"Qwen3-4B\"} 264\n# HELP vllm:e2e_request_latency_seconds Stage request latency, from execution start to reply.\n# TYPE vllm:e2e_request_latency_seconds histogram\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.3\"} 0\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.5\"} 0\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.8\"} 0\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Qwen3-4B\",le=\"1.0\"} 7\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Qwen3-4B\",le=\"1.5\"} 18\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Qwen3-4B\",le=\"2.0\"} 31\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Qwen3-4B\",le=\"2.5\"} 47\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Qwen3-4B\",le=\"5.0\"} 108\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Qwen3-4B\",le=\"10.0\"} 162\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Qwen3-4B\",le=\"15.0\"} 207\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Qwen3-4B\",le=\"20.0\"} 222\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Qwen3-4B\",le=\"30.0\"} 238\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Qwen3-4B\",le=\"40.0\"} 256\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Qwen3-4B\",le=\"50.0\"} 264\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Qwen3-4B\",le=\"60.0\"} 264\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Qwen3-4B\",le=\"+Inf\"} 264\nvllm:e2e_request_latency_seconds_sum{model_name=\"Qwen3-4B\"} 2885.9662324228557\nvllm:e2e_request_latency_seconds_count{model_name=\"Qwen3-4B\"} 264\n# HELP airm_kv_transfer_seconds KV transfer time from the prefill source to the decode destination.\n# TYPE airm_kv_transfer_seconds histogram\nairm_kv_transfer_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.0005\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.001\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.0025\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.005\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.01\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.025\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.05\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.1\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.25\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.5\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"Qwen3-4B\",le=\"1.0\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"Qwen3-4B\",le=\"+Inf\"} 0\nairm_kv_transfer_seconds_sum{model_name=\"Qwen3-4B\"} 0.0\nairm_kv_transfer_seconds_count{model_name=\"Qwen3-4B\"} 0\n# HELP vllm:cache_config_info Information of the LLMEngine CacheConfig.\n# TYPE vllm:cache_config_info gauge\nvllm:cache_config_info{model_name=\"Qwen3-4B\",block_size=\"64\",num_gpu_blocks=\"576\",enable_prefix_caching=\"False\"} 1\n# HELP vllm:prefix_cache_queries_total Prompt tokens looked up in the prefix cache.\n# TYPE vllm:prefix_cache_queries_total counter\nvllm:prefix_cache_queries_total{model_name=\"Qwen3-4B\"} 718080\n# HELP vllm:prefix_cache_hits_total Prompt tokens served from the prefix cache.\n# TYPE vllm:prefix_cache_hits_total counter\nvllm:prefix_cache_hits_total{model_name=\"Qwen3-4B\"} 0\n"
+  ],
+  "parity_passed": true
+}
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen4b-http-c16-stream.json b/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen4b-http-c16-stream.json
new file mode 100644
index 00000000..9726ca18
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen4b-http-c16-stream.json
@@ -0,0 +1,25227 @@
+{
+  "passed": true,
+  "model": "Qwen3-4B",
+  "mode": "pd",
+  "dtype": "bfloat16",
+  "torch": "2.13.0+cu130",
+  "transformers": "5.15.0",
+  "replicas": 1,
+  "cpu_threads_per_server": 4,
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+          "text": " The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nOkay, so the user is asking about the capital of France,",
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+          "text": " The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nOkay, so the user is asking about the capital of France,",
+          "text_equal": true,
+          "first_sse_s": 19.48593812098261,
+          "tpot_est_s": 0.02648317443315072,
+          "e2e_s": 22.84930127399275
+        },
+        {
+          "id": "r0-n128-o128-c16-9",
+          "endpoint": 0,
+          "input_tokens": 128,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nOkay, so the user is asking about the capital of France,",
+          "text_equal": true,
+          "first_sse_s": 4.323711181001272,
+          "tpot_est_s": 0.02618139477146012,
+          "e2e_s": 7.648748316976707
+        },
+        {
+          "id": "r0-n128-o128-c16-10",
+          "endpoint": 0,
+          "input_tokens": 128,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nOkay, so the user is asking about the capital of France,",
+          "text_equal": true,
+          "first_sse_s": 17.32100530102616,
+          "tpot_est_s": 0.02597881869255294,
+          "e2e_s": 20.620315274980385
+        },
+        {
+          "id": "r0-n128-o128-c16-11",
+          "endpoint": 0,
+          "input_tokens": 128,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nOkay, so the user is asking about the capital of France,",
+          "text_equal": true,
+          "first_sse_s": 6.602545631001703,
+          "tpot_est_s": 0.026356028141288863,
+          "e2e_s": 9.94976120494539
+        },
+        {
+          "id": "r0-n128-o128-c16-12",
+          "endpoint": 0,
+          "input_tokens": 128,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nOkay, so the user is asking about the capital of France,",
+          "text_equal": true,
+          "first_sse_s": 23.786729322979227,
+          "tpot_est_s": 0.0262522561178638,
+          "e2e_s": 27.12076584994793
+        },
+        {
+          "id": "r0-n128-o128-c16-13",
+          "endpoint": 0,
+          "input_tokens": 128,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nOkay, so the user is asking about the capital of France,",
+          "text_equal": true,
+          "first_sse_s": 10.907971617009025,
+          "tpot_est_s": 0.026772102055059175,
+          "e2e_s": 14.30802857800154
+        },
+        {
+          "id": "r0-n128-o128-c16-14",
+          "endpoint": 0,
+          "input_tokens": 128,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nOkay, so the user is asking about the capital of France,",
+          "text_equal": true,
+          "first_sse_s": 13.054649495985359,
+          "tpot_est_s": 0.02644870067693616,
+          "e2e_s": 16.41363448195625
+        },
+        {
+          "id": "r0-n128-o128-c16-15",
+          "endpoint": 0,
+          "input_tokens": 128,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nOkay, so the user is asking about the capital of France,",
+          "text_equal": true,
+          "first_sse_s": 30.23184368904913,
+          "tpot_est_s": 0.024914864440609915,
+          "e2e_s": 33.39603147300659
+        }
+      ]
+    },
+    {
+      "repeat": 0,
+      "input_tokens": 512,
+      "output_tokens_per_request": 32,
+      "concurrency": 16,
+      "summary": {
+        "requests": 16,
+        "output_tokens": 512,
+        "wall_s": 35.4657122810022,
+        "output_tok_s": 14.436478702114249,
+        "median_first_sse_s": 18.441696883004624,
+        "median_tpot_est_s": 0.02638734375772577,
+        "median_e2e_s": 19.29483583950787,
+        "all_text_equal": true
+      },
+      "rows": [
+        {
+          "id": "r0-n512-o32-c16-0",
+          "endpoint": 0,
+          "input_tokens": 512,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user also provided a",
+          "text_equal": true,
+          "first_sse_s": 2.1572561800130643,
+          "tpot_est_s": 0.02604052170945872,
+          "e2e_s": 2.9645123530062847
+        },
+        {
+          "id": "r0-n512-o32-c16-1",
+          "endpoint": 0,
+          "input_tokens": 512,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user also provided a",
+          "text_equal": true,
+          "first_sse_s": 30.368148271052632,
+          "tpot_est_s": 0.026272658966556794,
+          "e2e_s": 31.182600699015893
+        },
+        {
+          "id": "r0-n512-o32-c16-2",
+          "endpoint": 0,
+          "input_tokens": 512,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user also provided a",
+          "text_equal": true,
+          "first_sse_s": 26.0328922260087,
+          "tpot_est_s": 0.02650202854889475,
+          "e2e_s": 26.854455111024436
+        },
+        {
+          "id": "r0-n512-o32-c16-3",
+          "endpoint": 0,
+          "input_tokens": 512,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user also provided a",
+          "text_equal": true,
+          "first_sse_s": 21.711939244007226,
+          "tpot_est_s": 0.02526494119346382,
+          "e2e_s": 22.495152421004605
+        },
+        {
+          "id": "r0-n512-o32-c16-4",
+          "endpoint": 0,
+          "input_tokens": 512,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user also provided a",
+          "text_equal": true,
+          "first_sse_s": 32.528072938963305,
+          "tpot_est_s": 0.02529065732303406,
+          "e2e_s": 33.31208331597736
+        },
+        {
+          "id": "r0-n512-o32-c16-5",
+          "endpoint": 0,
+          "input_tokens": 512,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user also provided a",
+          "text_equal": true,
+          "first_sse_s": 15.157452377025038,
+          "tpot_est_s": 0.026877337450822516,
+          "e2e_s": 15.990649838000536
+        },
+        {
+          "id": "r0-n512-o32-c16-6",
+          "endpoint": 0,
+          "input_tokens": 512,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user also provided a",
+          "text_equal": true,
+          "first_sse_s": 19.54664423398208,
+          "tpot_est_s": 0.02796477922651496,
+          "e2e_s": 20.413552390004043
+        },
+        {
+          "id": "r0-n512-o32-c16-7",
+          "endpoint": 0,
+          "input_tokens": 512,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user also provided a",
+          "text_equal": true,
+          "first_sse_s": 4.320164769014809,
+          "tpot_est_s": 0.027573585322487257,
+          "e2e_s": 5.174945914011914
+        },
+        {
+          "id": "r0-n512-o32-c16-8",
+          "endpoint": 0,
+          "input_tokens": 512,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user also provided a",
+          "text_equal": true,
+          "first_sse_s": 34.72181122598704,
+          "tpot_est_s": 0.023991831580191973,
+          "e2e_s": 35.46555800497299
+        },
+        {
+          "id": "r0-n512-o32-c16-9",
+          "endpoint": 0,
+          "input_tokens": 512,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user also provided a",
+          "text_equal": true,
+          "first_sse_s": 23.862607960996684,
+          "tpot_est_s": 0.02525203429057353,
+          "e2e_s": 24.645421024004463
+        },
+        {
+          "id": "r0-n512-o32-c16-10",
+          "endpoint": 0,
+          "input_tokens": 512,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user also provided a",
+          "text_equal": true,
+          "first_sse_s": 10.813824237964582,
+          "tpot_est_s": 0.027390415515495283,
+          "e2e_s": 11.662927118944936
+        },
+        {
+          "id": "r0-n512-o32-c16-11",
+          "endpoint": 0,
+          "input_tokens": 512,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user also provided a",
+          "text_equal": true,
+          "first_sse_s": 17.33674953202717,
+          "tpot_est_s": 0.027076443773694336,
+          "e2e_s": 18.176119289011694
+        },
+        {
+          "id": "r0-n512-o32-c16-12",
+          "endpoint": 0,
+          "input_tokens": 512,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user also provided a",
+          "text_equal": true,
+          "first_sse_s": 28.204327557992656,
+          "tpot_est_s": 0.02594944474197203,
+          "e2e_s": 29.00876034499379
+        },
+        {
+          "id": "r0-n512-o32-c16-13",
+          "endpoint": 0,
+          "input_tokens": 512,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user also provided a",
+          "text_equal": true,
+          "first_sse_s": 8.636992046027444,
+          "tpot_est_s": 0.026894114676847932,
+          "e2e_s": 9.47070960100973
+        },
+        {
+          "id": "r0-n512-o32-c16-14",
+          "endpoint": 0,
+          "input_tokens": 512,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user also provided a",
+          "text_equal": true,
+          "first_sse_s": 12.99417729198467,
+          "tpot_est_s": 0.02685161599994547,
+          "e2e_s": 13.82657738798298
+        },
+        {
+          "id": "r0-n512-o32-c16-15",
+          "endpoint": 0,
+          "input_tokens": 512,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user also provided a",
+          "text_equal": true,
+          "first_sse_s": 6.475530933006667,
+          "tpot_est_s": 0.025936872871111957,
+          "e2e_s": 7.279573992011137
+        }
+      ]
+    },
+    {
+      "repeat": 0,
+      "input_tokens": 512,
+      "output_tokens_per_request": 128,
+      "concurrency": 16,
+      "summary": {
+        "requests": 16,
+        "output_tokens": 2048,
+        "wall_s": 38.47343846899457,
+        "output_tok_s": 53.23153015425087,
+        "median_first_sse_s": 18.62406320500304,
+        "median_tpot_est_s": 0.0258369232559078,
+        "median_e2e_s": 21.897607292979956,
+        "all_text_equal": true
+      },
+      "rows": [
+        {
+          "id": "r0-n512-o128-c16-0",
+          "endpoint": 0,
+          "input_tokens": 512,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user also provided a bunch of repeated background context about boxes containing apples and books. That seems unrelated. Maybe they wanted me to use that context for the question? But the question was straightforward. Did I miss something?\n\nLet me check again. The user's question was \"What is the capital of France?\" and the answer is Paris. The background context about boxes is probably just extra information, maybe a test to see if I can focus on the actual question. Or maybe it's part of a larger",
+          "text_equal": true,
+          "first_sse_s": 8.924791715980973,
+          "tpot_est_s": 0.025531583740174273,
+          "e2e_s": 12.167302850983106
+        },
+        {
+          "id": "r0-n512-o128-c16-1",
+          "endpoint": 0,
+          "input_tokens": 512,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user also provided a bunch of repeated background context about boxes containing apples and books. That seems unrelated. Maybe they wanted me to use that context for the question? But the question was straightforward. Did I miss something?\n\nLet me check again. The user's question was \"What is the capital of France?\" and the answer is Paris. The background context about boxes is probably just extra information, maybe a test to see if I can focus on the actual question. Or maybe it's part of a larger",
+          "text_equal": true,
+          "first_sse_s": 6.751149331976194,
+          "tpot_est_s": 0.024958196196954434,
+          "e2e_s": 9.920840248989407
+        },
+        {
+          "id": "r0-n512-o128-c16-2",
+          "endpoint": 0,
+          "input_tokens": 512,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user also provided a bunch of repeated background context about boxes containing apples and books. That seems unrelated. Maybe they wanted me to use that context for the question? But the question was straightforward. Did I miss something?\n\nLet me check again. The user's question was \"What is the capital of France?\" and the answer is Paris. The background context about boxes is probably just extra information, maybe a test to see if I can focus on the actual question. Or maybe it's part of a larger",
+          "text_equal": true,
+          "first_sse_s": 15.373921076999977,
+          "tpot_est_s": 0.025114913385794094,
+          "e2e_s": 18.563515076995827
+        },
+        {
+          "id": "r0-n512-o128-c16-3",
+          "endpoint": 0,
+          "input_tokens": 512,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user also provided a bunch of repeated background context about boxes containing apples and books. That seems unrelated. Maybe they wanted me to use that context for the question? But the question was straightforward. Did I miss something?\n\nLet me check again. The user's question was \"What is the capital of France?\" and the answer is Paris. The background context about boxes is probably just extra information, maybe a test to see if I can focus on the actual question. Or maybe it's part of a larger",
+          "text_equal": true,
+          "first_sse_s": 28.7826026409748,
+          "tpot_est_s": 0.026385315173254237,
+          "e2e_s": 32.13353766797809
+        },
+        {
+          "id": "r0-n512-o128-c16-4",
+          "endpoint": 0,
+          "input_tokens": 512,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user also provided a bunch of repeated background context about boxes containing apples and books. That seems unrelated. Maybe they wanted me to use that context for the question? But the question was straightforward. Did I miss something?\n\nLet me check again. The user's question was \"What is the capital of France?\" and the answer is Paris. The background context about boxes is probably just extra information, maybe a test to see if I can focus on the actual question. Or maybe it's part of a larger",
+          "text_equal": true,
+          "first_sse_s": 21.933690709993243,
+          "tpot_est_s": 0.026131040755820675,
+          "e2e_s": 25.25233288598247
+        },
+        {
+          "id": "r0-n512-o128-c16-5",
+          "endpoint": 0,
+          "input_tokens": 512,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user also provided a bunch of repeated background context about boxes containing apples and books. That seems unrelated. Maybe they wanted me to use that context for the question? But the question was straightforward. Did I miss something?\n\nLet me check again. The user's question was \"What is the capital of France?\" and the answer is Paris. The background context about boxes is probably just extra information, maybe a test to see if I can focus on the actual question. Or maybe it's part of a larger",
+          "text_equal": true,
+          "first_sse_s": 30.94369880098384,
+          "tpot_est_s": 0.026396120055101988,
+          "e2e_s": 34.29600604798179
+        },
+        {
+          "id": "r0-n512-o128-c16-6",
+          "endpoint": 0,
+          "input_tokens": 512,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user also provided a bunch of repeated background context about boxes containing apples and books. That seems unrelated. Maybe they wanted me to use that context for the question? But the question was straightforward. Did I miss something?\n\nLet me check again. The user's question was \"What is the capital of France?\" and the answer is Paris. The background context about boxes is probably just extra information, maybe a test to see if I can focus on the actual question. Or maybe it's part of a larger",
+          "text_equal": true,
+          "first_sse_s": 17.518132571014576,
+          "tpot_est_s": 0.025442872480140984,
+          "e2e_s": 20.74937737599248
+        },
+        {
+          "id": "r0-n512-o128-c16-7",
+          "endpoint": 0,
+          "input_tokens": 512,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user also provided a bunch of repeated background context about boxes containing apples and books. That seems unrelated. Maybe they wanted me to use that context for the question? But the question was straightforward. Did I miss something?\n\nLet me check again. The user's question was \"What is the capital of France?\" and the answer is Paris. The background context about boxes is probably just extra information, maybe a test to see if I can focus on the actual question. Or maybe it's part of a larger",
+          "text_equal": true,
+          "first_sse_s": 4.354479936009739,
+          "tpot_est_s": 0.025328150409086807,
+          "e2e_s": 7.571155037963763
+        },
+        {
+          "id": "r0-n512-o128-c16-8",
+          "endpoint": 0,
+          "input_tokens": 512,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user also provided a bunch of repeated background context about boxes containing apples and books. That seems unrelated. Maybe they wanted me to use that context for the question? But the question was straightforward. Did I miss something?\n\nLet me check again. The user's question was \"What is the capital of France?\" and the answer is Paris. The background context about boxes is probably just extra information, maybe a test to see if I can focus on the actual question. Or maybe it's part of a larger",
+          "text_equal": true,
+          "first_sse_s": 26.384608043998014,
+          "tpot_est_s": 0.026680280393957506,
+          "e2e_s": 29.773003654030617
+        },
+        {
+          "id": "r0-n512-o128-c16-9",
+          "endpoint": 0,
+          "input_tokens": 512,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user also provided a bunch of repeated background context about boxes containing apples and books. That seems unrelated. Maybe they wanted me to use that context for the question? But the question was straightforward. Did I miss something?\n\nLet me check again. The user's question was \"What is the capital of France?\" and the answer is Paris. The background context about boxes is probably just extra information, maybe a test to see if I can focus on the actual question. Or maybe it's part of a larger",
+          "text_equal": true,
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+        {
+          "id": "r0-n512-o128-c16-10",
+          "endpoint": 0,
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+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user also provided a bunch of repeated background context about boxes containing apples and books. That seems unrelated. Maybe they wanted me to use that context for the question? But the question was straightforward. Did I miss something?\n\nLet me check again. The user's question was \"What is the capital of France?\" and the answer is Paris. The background context about boxes is probably just extra information, maybe a test to see if I can focus on the actual question. Or maybe it's part of a larger",
+          "text_equal": true,
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+        {
+          "id": "r0-n512-o128-c16-11",
+          "endpoint": 0,
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+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user also provided a bunch of repeated background context about boxes containing apples and books. That seems unrelated. Maybe they wanted me to use that context for the question? But the question was straightforward. Did I miss something?\n\nLet me check again. The user's question was \"What is the capital of France?\" and the answer is Paris. The background context about boxes is probably just extra information, maybe a test to see if I can focus on the actual question. Or maybe it's part of a larger",
+          "text_equal": true,
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+          "endpoint": 0,
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+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user also provided a bunch of repeated background context about boxes containing apples and books. That seems unrelated. Maybe they wanted me to use that context for the question? But the question was straightforward. Did I miss something?\n\nLet me check again. The user's question was \"What is the capital of France?\" and the answer is Paris. The background context about boxes is probably just extra information, maybe a test to see if I can focus on the actual question. Or maybe it's part of a larger",
+          "text_equal": true,
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+        {
+          "id": "r0-n512-o128-c16-13",
+          "endpoint": 0,
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+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user also provided a bunch of repeated background context about boxes containing apples and books. That seems unrelated. Maybe they wanted me to use that context for the question? But the question was straightforward. Did I miss something?\n\nLet me check again. The user's question was \"What is the capital of France?\" and the answer is Paris. The background context about boxes is probably just extra information, maybe a test to see if I can focus on the actual question. Or maybe it's part of a larger",
+          "text_equal": true,
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+          "id": "r0-n512-o128-c16-14",
+          "endpoint": 0,
+          "input_tokens": 512,
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+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user also provided a bunch of repeated background context about boxes containing apples and books. That seems unrelated. Maybe they wanted me to use that context for the question? But the question was straightforward. Did I miss something?\n\nLet me check again. The user's question was \"What is the capital of France?\" and the answer is Paris. The background context about boxes is probably just extra information, maybe a test to see if I can focus on the actual question. Or maybe it's part of a larger",
+          "text_equal": true,
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+          "id": "r0-n512-o128-c16-15",
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+          "input_tokens": 512,
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+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user also provided a bunch of repeated background context about boxes containing apples and books. That seems unrelated. Maybe they wanted me to use that context for the question? But the question was straightforward. Did I miss something?\n\nLet me check again. The user's question was \"What is the capital of France?\" and the answer is Paris. The background context about boxes is probably just extra information, maybe a test to see if I can focus on the actual question. Or maybe it's part of a larger",
+          "text_equal": true,
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+          "tpot_est_s": 0.02600234739370571,
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+          "id": "r0-n2048-o32-c16-0",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user provided a bunch",
+          "text_equal": true,
+          "first_sse_s": 2.278031637019012,
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+          "id": "r0-n2048-o32-c16-1",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user provided a bunch",
+          "text_equal": true,
+          "first_sse_s": 29.92659032798838,
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+          "id": "r0-n2048-o32-c16-2",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user provided a bunch",
+          "text_equal": true,
+          "first_sse_s": 37.01559546496719,
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+        {
+          "id": "r0-n2048-o32-c16-3",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user provided a bunch",
+          "text_equal": true,
+          "first_sse_s": 25.299881826038472,
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+        {
+          "id": "r0-n2048-o32-c16-4",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user provided a bunch",
+          "text_equal": true,
+          "first_sse_s": 20.737399808014743,
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+          "id": "r0-n2048-o32-c16-5",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user provided a bunch",
+          "text_equal": true,
+          "first_sse_s": 23.017193384002894,
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+        {
+          "id": "r0-n2048-o32-c16-6",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user provided a bunch",
+          "text_equal": true,
+          "first_sse_s": 16.12346215301659,
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+        {
+          "id": "r0-n2048-o32-c16-7",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user provided a bunch",
+          "text_equal": true,
+          "first_sse_s": 34.512291252962314,
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+        {
+          "id": "r0-n2048-o32-c16-8",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user provided a bunch",
+          "text_equal": true,
+          "first_sse_s": 4.573229296016507,
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+        {
+          "id": "r0-n2048-o32-c16-9",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user provided a bunch",
+          "text_equal": true,
+          "first_sse_s": 13.752238415996544,
+          "tpot_est_s": 0.03240598351581984,
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+        {
+          "id": "r0-n2048-o32-c16-10",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user provided a bunch",
+          "text_equal": true,
+          "first_sse_s": 6.936467053019442,
+          "tpot_est_s": 0.025977929385619297,
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+        {
+          "id": "r0-n2048-o32-c16-11",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user provided a bunch",
+          "text_equal": true,
+          "first_sse_s": 32.195625221997034,
+          "tpot_est_s": 0.02687147700025009,
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+        {
+          "id": "r0-n2048-o32-c16-12",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user provided a bunch",
+          "text_equal": true,
+          "first_sse_s": 11.521884437010158,
+          "tpot_est_s": 0.029696861484778986,
+          "e2e_s": 12.442487143038306
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+        {
+          "id": "r0-n2048-o32-c16-13",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user provided a bunch",
+          "text_equal": true,
+          "first_sse_s": 18.46707371197408,
+          "tpot_est_s": 0.029714370354814754,
+          "e2e_s": 19.388219192973338
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+        {
+          "id": "r0-n2048-o32-c16-14",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user provided a bunch",
+          "text_equal": true,
+          "first_sse_s": 27.57567305502016,
+          "tpot_est_s": 0.029555882225864596,
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+          "id": "r0-n2048-o32-c16-15",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user provided a bunch",
+          "text_equal": true,
+          "first_sse_s": 9.26938061096007,
+          "tpot_est_s": 0.029987334517852193,
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+        {
+          "id": "r0-n2048-o128-c16-0",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user provided a bunch of repeated background context about boxes containing apples and books. That seems unrelated. Maybe they wanted me to use that context to answer the question? But the question is about the capital of France, which is a general knowledge fact. The background context doesn't mention anything about France or capitals. So maybe the user is testing if I can ignore irrelevant information? Or perhaps they included the context by mistake. Either way, the answer is Paris. I should make sure to confirm that even though",
+          "text_equal": true,
+          "first_sse_s": 11.376093705999665,
+          "tpot_est_s": 0.02760304064548567,
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+        {
+          "id": "r0-n2048-o128-c16-1",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user provided a bunch of repeated background context about boxes containing apples and books. That seems unrelated. Maybe they wanted me to use that context to answer the question? But the question is about the capital of France, which is a general knowledge fact. The background context doesn't mention anything about France or capitals. So maybe the user is testing if I can ignore irrelevant information? Or perhaps they included the context by mistake. Either way, the answer is Paris. I should make sure to confirm that even though",
+          "text_equal": true,
+          "first_sse_s": 4.57865900703473,
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+        {
+          "id": "r0-n2048-o128-c16-2",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user provided a bunch of repeated background context about boxes containing apples and books. That seems unrelated. Maybe they wanted me to use that context to answer the question? But the question is about the capital of France, which is a general knowledge fact. The background context doesn't mention anything about France or capitals. So maybe the user is testing if I can ignore irrelevant information? Or perhaps they included the context by mistake. Either way, the answer is Paris. I should make sure to confirm that even though",
+          "text_equal": true,
+          "first_sse_s": 25.08788953302428,
+          "tpot_est_s": 0.027016137306776454,
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+        {
+          "id": "r0-n2048-o128-c16-3",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user provided a bunch of repeated background context about boxes containing apples and books. That seems unrelated. Maybe they wanted me to use that context to answer the question? But the question is about the capital of France, which is a general knowledge fact. The background context doesn't mention anything about France or capitals. So maybe the user is testing if I can ignore irrelevant information? Or perhaps they included the context by mistake. Either way, the answer is Paris. I should make sure to confirm that even though",
+          "text_equal": true,
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+        {
+          "id": "r0-n2048-o128-c16-4",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user provided a bunch of repeated background context about boxes containing apples and books. That seems unrelated. Maybe they wanted me to use that context to answer the question? But the question is about the capital of France, which is a general knowledge fact. The background context doesn't mention anything about France or capitals. So maybe the user is testing if I can ignore irrelevant information? Or perhaps they included the context by mistake. Either way, the answer is Paris. I should make sure to confirm that even though",
+          "text_equal": true,
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+        {
+          "id": "r0-n2048-o128-c16-5",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user provided a bunch of repeated background context about boxes containing apples and books. That seems unrelated. Maybe they wanted me to use that context to answer the question? But the question is about the capital of France, which is a general knowledge fact. The background context doesn't mention anything about France or capitals. So maybe the user is testing if I can ignore irrelevant information? Or perhaps they included the context by mistake. Either way, the answer is Paris. I should make sure to confirm that even though",
+          "text_equal": true,
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+          "id": "r0-n2048-o128-c16-6",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user provided a bunch of repeated background context about boxes containing apples and books. That seems unrelated. Maybe they wanted me to use that context to answer the question? But the question is about the capital of France, which is a general knowledge fact. The background context doesn't mention anything about France or capitals. So maybe the user is testing if I can ignore irrelevant information? Or perhaps they included the context by mistake. Either way, the answer is Paris. I should make sure to confirm that even though",
+          "text_equal": true,
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+        {
+          "id": "r0-n2048-o128-c16-7",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user provided a bunch of repeated background context about boxes containing apples and books. That seems unrelated. Maybe they wanted me to use that context to answer the question? But the question is about the capital of France, which is a general knowledge fact. The background context doesn't mention anything about France or capitals. So maybe the user is testing if I can ignore irrelevant information? Or perhaps they included the context by mistake. Either way, the answer is Paris. I should make sure to confirm that even though",
+          "text_equal": true,
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+          "id": "r0-n2048-o128-c16-8",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user provided a bunch of repeated background context about boxes containing apples and books. That seems unrelated. Maybe they wanted me to use that context to answer the question? But the question is about the capital of France, which is a general knowledge fact. The background context doesn't mention anything about France or capitals. So maybe the user is testing if I can ignore irrelevant information? Or perhaps they included the context by mistake. Either way, the answer is Paris. I should make sure to confirm that even though",
+          "text_equal": true,
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+          "id": "r0-n2048-o128-c16-9",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user provided a bunch of repeated background context about boxes containing apples and books. That seems unrelated. Maybe they wanted me to use that context to answer the question? But the question is about the capital of France, which is a general knowledge fact. The background context doesn't mention anything about France or capitals. So maybe the user is testing if I can ignore irrelevant information? Or perhaps they included the context by mistake. Either way, the answer is Paris. I should make sure to confirm that even though",
+          "text_equal": true,
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+          "input_tokens": 2048,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user provided a bunch of repeated background context about boxes containing apples and books. That seems unrelated. Maybe they wanted me to use that context to answer the question? But the question is about the capital of France, which is a general knowledge fact. The background context doesn't mention anything about France or capitals. So maybe the user is testing if I can ignore irrelevant information? Or perhaps they included the context by mistake. Either way, the answer is Paris. I should make sure to confirm that even though",
+          "text_equal": true,
+          "first_sse_s": 29.569808166997973,
+          "tpot_est_s": 0.02704892557446192,
+          "e2e_s": 33.00502171495464
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+        {
+          "id": "r0-n2048-o128-c16-11",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user provided a bunch of repeated background context about boxes containing apples and books. That seems unrelated. Maybe they wanted me to use that context to answer the question? But the question is about the capital of France, which is a general knowledge fact. The background context doesn't mention anything about France or capitals. So maybe the user is testing if I can ignore irrelevant information? Or perhaps they included the context by mistake. Either way, the answer is Paris. I should make sure to confirm that even though",
+          "text_equal": true,
+          "first_sse_s": 31.839241387031507,
+          "tpot_est_s": 0.02669551753527008,
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+        {
+          "id": "r0-n2048-o128-c16-12",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user provided a bunch of repeated background context about boxes containing apples and books. That seems unrelated. Maybe they wanted me to use that context to answer the question? But the question is about the capital of France, which is a general knowledge fact. The background context doesn't mention anything about France or capitals. So maybe the user is testing if I can ignore irrelevant information? Or perhaps they included the context by mistake. Either way, the answer is Paris. I should make sure to confirm that even though",
+          "text_equal": true,
+          "first_sse_s": 6.8368627250310965,
+          "tpot_est_s": 0.027597644747652875,
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+        {
+          "id": "r0-n2048-o128-c16-13",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user provided a bunch of repeated background context about boxes containing apples and books. That seems unrelated. Maybe they wanted me to use that context to answer the question? But the question is about the capital of France, which is a general knowledge fact. The background context doesn't mention anything about France or capitals. So maybe the user is testing if I can ignore irrelevant information? Or perhaps they included the context by mistake. Either way, the answer is Paris. I should make sure to confirm that even though",
+          "text_equal": true,
+          "first_sse_s": 20.622563645010814,
+          "tpot_est_s": 0.027600499496091245,
+          "e2e_s": 24.127827081014402
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+        {
+          "id": "r0-n2048-o128-c16-14",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user provided a bunch of repeated background context about boxes containing apples and books. That seems unrelated. Maybe they wanted me to use that context to answer the question? But the question is about the capital of France, which is a general knowledge fact. The background context doesn't mention anything about France or capitals. So maybe the user is testing if I can ignore irrelevant information? Or perhaps they included the context by mistake. Either way, the answer is Paris. I should make sure to confirm that even though",
+          "text_equal": true,
+          "first_sse_s": 22.855603497999255,
+          "tpot_est_s": 0.02721285991330345,
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+        {
+          "id": "r0-n2048-o128-c16-15",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user provided a bunch of repeated background context about boxes containing apples and books. That seems unrelated. Maybe they wanted me to use that context to answer the question? But the question is about the capital of France, which is a general knowledge fact. The background context doesn't mention anything about France or capitals. So maybe the user is testing if I can ignore irrelevant information? Or perhaps they included the context by mistake. Either way, the answer is Paris. I should make sure to confirm that even though",
+          "text_equal": true,
+          "first_sse_s": 27.327035285008606,
+          "tpot_est_s": 0.027467993700392837,
+          "e2e_s": 30.815470484958496
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+    {
+      "repeat": 0,
+      "input_tokens": 8192,
+      "output_tokens_per_request": 32,
+      "concurrency": 16,
+      "summary": {
+        "requests": 16,
+        "output_tokens": 512,
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+        "median_e2e_s": 24.133545013988623,
+        "all_text_equal": true
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+      "rows": [
+        {
+          "id": "r0-n8192-o32-c16-0",
+          "endpoint": 0,
+          "input_tokens": 8192,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user is asking for the capital of France. Let me think. I know that Paris is the capital,",
+          "text_equal": true,
+          "first_sse_s": 38.210323281993624,
+          "tpot_est_s": 0.030087617451264974,
+          "e2e_s": 39.14303942298284
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+        {
+          "id": "r0-n8192-o32-c16-1",
+          "endpoint": 0,
+          "input_tokens": 8192,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user is asking for the capital of France. Let me think. I know that Paris is the capital,",
+          "text_equal": true,
+          "first_sse_s": 40.727528556017205,
+          "tpot_est_s": 0.02562201138578295,
+          "e2e_s": 41.52181090897648
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+        {
+          "id": "r0-n8192-o32-c16-2",
+          "endpoint": 0,
+          "input_tokens": 8192,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user is asking for the capital of France. Let me think. I know that Paris is the capital,",
+          "text_equal": true,
+          "first_sse_s": 15.36273676197743,
+          "tpot_est_s": 0.03546278683784147,
+          "e2e_s": 16.462083153950516
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+        {
+          "id": "r0-n8192-o32-c16-3",
+          "endpoint": 0,
+          "input_tokens": 8192,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user is asking for the capital of France. Let me think. I know that Paris is the capital,",
+          "text_equal": true,
+          "first_sse_s": 25.67590041999938,
+          "tpot_est_s": 0.02931480016183829,
+          "e2e_s": 26.584659225016367
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+        {
+          "id": "r0-n8192-o32-c16-4",
+          "endpoint": 0,
+          "input_tokens": 8192,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user is asking for the capital of France. Let me think. I know that Paris is the capital,",
+          "text_equal": true,
+          "first_sse_s": 15.54550508398097,
+          "tpot_est_s": 0.02957854477403265,
+          "e2e_s": 16.462439971975982
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+        {
+          "id": "r0-n8192-o32-c16-5",
+          "endpoint": 0,
+          "input_tokens": 8192,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user is asking for the capital of France. Let me think. I know that Paris is the capital,",
+          "text_equal": true,
+          "first_sse_s": 10.328972161980346,
+          "tpot_est_s": 0.03605125958077429,
+          "e2e_s": 11.446561208984349
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+        {
+          "id": "r0-n8192-o32-c16-6",
+          "endpoint": 0,
+          "input_tokens": 8192,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user is asking for the capital of France. Let me think. I know that Paris is the capital,",
+          "text_equal": true,
+          "first_sse_s": 5.4509941400028765,
+          "tpot_est_s": 0.028234178838770715,
+          "e2e_s": 6.326253684004769
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+        {
+          "id": "r0-n8192-o32-c16-7",
+          "endpoint": 0,
+          "input_tokens": 8192,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user is asking for the capital of France. Let me think. I know that Paris is the capital,",
+          "text_equal": true,
+          "first_sse_s": 30.767036555043887,
+          "tpot_est_s": 0.030328001999746886,
+          "e2e_s": 31.70720461703604
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+        {
+          "id": "r0-n8192-o32-c16-8",
+          "endpoint": 0,
+          "input_tokens": 8192,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user is asking for the capital of France. Let me think. I know that Paris is the capital,",
+          "text_equal": true,
+          "first_sse_s": 5.250765603967011,
+          "tpot_est_s": 0.034670440806827,
+          "e2e_s": 6.325549268978648
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+        {
+          "id": "r0-n8192-o32-c16-9",
+          "endpoint": 0,
+          "input_tokens": 8192,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user is asking for the capital of France. Let me think. I know that Paris is the capital,",
+          "text_equal": true,
+          "first_sse_s": 30.583064144011587,
+          "tpot_est_s": 0.03625996612883624,
+          "e2e_s": 31.70712309400551
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+        {
+          "id": "r0-n8192-o32-c16-10",
+          "endpoint": 0,
+          "input_tokens": 8192,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user is asking for the capital of France. Let me think. I know that Paris is the capital,",
+          "text_equal": true,
+          "first_sse_s": 20.45434950699564,
+          "tpot_est_s": 0.03953919161276351,
+          "e2e_s": 21.68006444699131
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+        {
+          "id": "r0-n8192-o32-c16-11",
+          "endpoint": 0,
+          "input_tokens": 8192,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user is asking for the capital of France. Let me think. I know that Paris is the capital,",
+          "text_equal": true,
+          "first_sse_s": 20.667697619006503,
+          "tpot_est_s": 0.03274243006355039,
+          "e2e_s": 21.682712950976565
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+        {
+          "id": "r0-n8192-o32-c16-12",
+          "endpoint": 0,
+          "input_tokens": 8192,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user is asking for the capital of France. Let me think. I know that Paris is the capital,",
+          "text_equal": true,
+          "first_sse_s": 36.02096238499507,
+          "tpot_est_s": 0.031182167580306168,
+          "e2e_s": 36.987609579984564
+        },
+        {
+          "id": "r0-n8192-o32-c16-13",
+          "endpoint": 0,
+          "input_tokens": 8192,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user is asking for the capital of France. Let me think. I know that Paris is the capital,",
+          "text_equal": true,
+          "first_sse_s": 25.506475807051174,
+          "tpot_est_s": 0.03477100870804861,
+          "e2e_s": 26.58437707700068
+        },
+        {
+          "id": "r0-n8192-o32-c16-14",
+          "endpoint": 0,
+          "input_tokens": 8192,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user is asking for the capital of France. Let me think. I know that Paris is the capital,",
+          "text_equal": true,
+          "first_sse_s": 10.528153591963928,
+          "tpot_est_s": 0.029633511580722107,
+          "e2e_s": 11.446792450966313
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+        {
+          "id": "r0-n8192-o32-c16-15",
+          "endpoint": 0,
+          "input_tokens": 8192,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user is asking for the capital of France. Let me think. I know that Paris is the capital,",
+          "text_equal": true,
+          "first_sse_s": 35.82899295800598,
+          "tpot_est_s": 0.03736541148522989,
+          "e2e_s": 36.98732071404811
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+    },
+    {
+      "repeat": 0,
+      "input_tokens": 8192,
+      "output_tokens_per_request": 128,
+      "concurrency": 16,
+      "summary": {
+        "requests": 16,
+        "output_tokens": 2048,
+        "wall_s": 43.7923671239987,
+        "output_tok_s": 46.76614064275309,
+        "median_first_sse_s": 21.507871351001086,
+        "median_tpot_est_s": 0.0333144449331893,
+        "median_e2e_s": 25.960039144032635,
+        "all_text_equal": true
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+      "rows": [
+        {
+          "id": "r0-n8192-o128-c16-0",
+          "endpoint": 0,
+          "input_tokens": 8192,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nOkay, so the user is asking for the capital of France. Let me think. I know that Paris is the capital, but I should make sure there's no trick here. The user provided a lot of context about boxes with apples and books, but that seems unrelated. Maybe it's a test to see if I can ignore irrelevant information. Yeah, the answer should be Paris. I don't need to consider the boxes because the question is straightforward. Just need to recall that Paris is the capital of France. No need to overcomplicate it.\nThe capital of France is Paris. The information about",
+          "text_equal": true,
+          "first_sse_s": 5.615981902054045,
+          "tpot_est_s": 0.03043375317306133,
+          "e2e_s": 9.481068555032834
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+        {
+          "id": "r0-n8192-o128-c16-1",
+          "endpoint": 0,
+          "input_tokens": 8192,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nOkay, so the user is asking for the capital of France. Let me think. I know that Paris is the capital, but I should make sure there's no trick here. The user provided a lot of context about boxes with apples and books, but that seems unrelated. Maybe it's a test to see if I can ignore irrelevant information. Yeah, the answer should be Paris. I don't need to consider the boxes because the question is straightforward. Just need to recall that Paris is the capital of France. No need to overcomplicate it.\nThe capital of France is Paris. The information about",
+          "text_equal": true,
+          "first_sse_s": 38.093518821988255,
+          "tpot_est_s": 0.027938860086779163,
+          "e2e_s": 41.64175405300921
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+        {
+          "id": "r0-n8192-o128-c16-2",
+          "endpoint": 0,
+          "input_tokens": 8192,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nOkay, so the user is asking for the capital of France. Let me think. I know that Paris is the capital, but I should make sure there's no trick here. The user provided a lot of context about boxes with apples and books, but that seems unrelated. Maybe it's a test to see if I can ignore irrelevant information. Yeah, the answer should be Paris. I don't need to consider the boxes because the question is straightforward. Just need to recall that Paris is the capital of France. No need to overcomplicate it.\nThe capital of France is Paris. The information about",
+          "text_equal": true,
+          "first_sse_s": 30.518567721010186,
+          "tpot_est_s": 0.034117790362082685,
+          "e2e_s": 34.85152709699469
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+        {
+          "id": "r0-n8192-o128-c16-3",
+          "endpoint": 0,
+          "input_tokens": 8192,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nOkay, so the user is asking for the capital of France. Let me think. I know that Paris is the capital, but I should make sure there's no trick here. The user provided a lot of context about boxes with apples and books, but that seems unrelated. Maybe it's a test to see if I can ignore irrelevant information. Yeah, the answer should be Paris. I don't need to consider the boxes because the question is straightforward. Just need to recall that Paris is the capital of France. No need to overcomplicate it.\nThe capital of France is Paris. The information about",
+          "text_equal": true,
+          "first_sse_s": 17.69715617701877,
+          "tpot_est_s": 0.03653332696838935,
+          "e2e_s": 22.336888702004217
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+        {
+          "id": "r0-n8192-o128-c16-4",
+          "endpoint": 0,
+          "input_tokens": 8192,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nOkay, so the user is asking for the capital of France. Let me think. I know that Paris is the capital, but I should make sure there's no trick here. The user provided a lot of context about boxes with apples and books, but that seems unrelated. Maybe it's a test to see if I can ignore irrelevant information. Yeah, the answer should be Paris. I don't need to consider the boxes because the question is straightforward. Just need to recall that Paris is the capital of France. No need to overcomplicate it.\nThe capital of France is Paris. The information about",
+          "text_equal": true,
+          "first_sse_s": 5.413505206990521,
+          "tpot_est_s": 0.03201920177153216,
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+        {
+          "id": "r0-n8192-o128-c16-5",
+          "endpoint": 0,
+          "input_tokens": 8192,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nOkay, so the user is asking for the capital of France. Let me think. I know that Paris is the capital, but I should make sure there's no trick here. The user provided a lot of context about boxes with apples and books, but that seems unrelated. Maybe it's a test to see if I can ignore irrelevant information. Yeah, the answer should be Paris. I don't need to consider the boxes because the question is straightforward. Just need to recall that Paris is the capital of France. No need to overcomplicate it.\nThe capital of France is Paris. The information about",
+          "text_equal": true,
+          "first_sse_s": 33.04714788397541,
+          "tpot_est_s": 0.036918501141866095,
+          "e2e_s": 37.73579752899241
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+        {
+          "id": "r0-n8192-o128-c16-6",
+          "endpoint": 0,
+          "input_tokens": 8192,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nOkay, so the user is asking for the capital of France. Let me think. I know that Paris is the capital, but I should make sure there's no trick here. The user provided a lot of context about boxes with apples and books, but that seems unrelated. Maybe it's a test to see if I can ignore irrelevant information. Yeah, the answer should be Paris. I don't need to consider the boxes because the question is straightforward. Just need to recall that Paris is the capital of France. No need to overcomplicate it.\nThe capital of France is Paris. The information about",
+          "text_equal": true,
+          "first_sse_s": 40.64223339303862,
+          "tpot_est_s": 0.02477501159832688,
+          "e2e_s": 43.78865986602614
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+        {
+          "id": "r0-n8192-o128-c16-7",
+          "endpoint": 0,
+          "input_tokens": 8192,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nOkay, so the user is asking for the capital of France. Let me think. I know that Paris is the capital, but I should make sure there's no trick here. The user provided a lot of context about boxes with apples and books, but that seems unrelated. Maybe it's a test to see if I can ignore irrelevant information. Yeah, the answer should be Paris. I don't need to consider the boxes because the question is straightforward. Just need to recall that Paris is the capital of France. No need to overcomplicate it.\nThe capital of France is Paris. The information about",
+          "text_equal": true,
+          "first_sse_s": 10.264619757013861,
+          "tpot_est_s": 0.03261176706291735,
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+        {
+          "id": "r0-n8192-o128-c16-8",
+          "endpoint": 0,
+          "input_tokens": 8192,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nOkay, so the user is asking for the capital of France. Let me think. I know that Paris is the capital, but I should make sure there's no trick here. The user provided a lot of context about boxes with apples and books, but that seems unrelated. Maybe it's a test to see if I can ignore irrelevant information. Yeah, the answer should be Paris. I don't need to consider the boxes because the question is straightforward. Just need to recall that Paris is the capital of France. No need to overcomplicate it.\nThe capital of France is Paris. The information about",
+          "text_equal": true,
+          "first_sse_s": 20.243671538017225,
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+        {
+          "id": "r0-n8192-o128-c16-9",
+          "endpoint": 0,
+          "input_tokens": 8192,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nOkay, so the user is asking for the capital of France. Let me think. I know that Paris is the capital, but I should make sure there's no trick here. The user provided a lot of context about boxes with apples and books, but that seems unrelated. Maybe it's a test to see if I can ignore irrelevant information. Yeah, the answer should be Paris. I don't need to consider the boxes because the question is straightforward. Just need to recall that Paris is the capital of France. No need to overcomplicate it.\nThe capital of France is Paris. The information about",
+          "text_equal": true,
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+        {
+          "id": "r0-n8192-o128-c16-10",
+          "endpoint": 0,
+          "input_tokens": 8192,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nOkay, so the user is asking for the capital of France. Let me think. I know that Paris is the capital, but I should make sure there's no trick here. The user provided a lot of context about boxes with apples and books, but that seems unrelated. Maybe it's a test to see if I can ignore irrelevant information. Yeah, the answer should be Paris. I don't need to consider the boxes because the question is straightforward. Just need to recall that Paris is the capital of France. No need to overcomplicate it.\nThe capital of France is Paris. The information about",
+          "text_equal": true,
+          "first_sse_s": 15.199426072998904,
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+          "endpoint": 0,
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+          "text": " The capital of France is Paris.\nOkay, so the user is asking for the capital of France. Let me think. I know that Paris is the capital, but I should make sure there's no trick here. The user provided a lot of context about boxes with apples and books, but that seems unrelated. Maybe it's a test to see if I can ignore irrelevant information. Yeah, the answer should be Paris. I don't need to consider the boxes because the question is straightforward. Just need to recall that Paris is the capital of France. No need to overcomplicate it.\nThe capital of France is Paris. The information about",
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+          "text": " The capital of France is Paris.\nOkay, so the user is asking for the capital of France. Let me think. I know that Paris is the capital, but I should make sure there's no trick here. The user provided a lot of context about boxes with apples and books, but that seems unrelated. Maybe it's a test to see if I can ignore irrelevant information. Yeah, the answer should be Paris. I don't need to consider the boxes because the question is straightforward. Just need to recall that Paris is the capital of France. No need to overcomplicate it.\nThe capital of France is Paris. The information about",
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+          "endpoint": 0,
+          "input_tokens": 8192,
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+          "text": " The capital of France is Paris.\nOkay, so the user is asking for the capital of France. Let me think. I know that Paris is the capital, but I should make sure there's no trick here. The user provided a lot of context about boxes with apples and books, but that seems unrelated. Maybe it's a test to see if I can ignore irrelevant information. Yeah, the answer should be Paris. I don't need to consider the boxes because the question is straightforward. Just need to recall that Paris is the capital of France. No need to overcomplicate it.\nThe capital of France is Paris. The information about",
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+        {
+          "id": "r0-n8192-o128-c16-15",
+          "endpoint": 0,
+          "input_tokens": 8192,
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+          "text": " The capital of France is Paris.\nOkay, so the user is asking for the capital of France. Let me think. I know that Paris is the capital, but I should make sure there's no trick here. The user provided a lot of context about boxes with apples and books, but that seems unrelated. Maybe it's a test to see if I can ignore irrelevant information. Yeah, the answer should be Paris. I don't need to consider the boxes because the question is straightforward. Just need to recall that Paris is the capital of France. No need to overcomplicate it.\nThe capital of France is Paris. The information about",
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+  "producer_cache": false,
+  "decoder_cache": false,
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+  "local_chunk": 256,
+  "context": 9216,
+  "server_config": {
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+      [
+        "/home/gspark/venvs/pt213/bin/python",
+        "-c",
+        "import sys,importlib.util,runpy\ns=importlib.util.spec_from_file_location('opt_prime.accel.serve.llmd','/tmp/llmd240.py')\nm=importlib.util.module_from_spec(s);sys.modules[s.name]=m;s.loader.exec_module(m)\nrunpy.run_path('/home/gspark/workspace/aicomp-rngd-stream-265/opt_prime/examples/pd/serve_batched.py',run_name='__main__')\n",
+        "--model",
+        "/tmp/serving-matrix-models/Qwen3-4B",
+        "--device",
+        "cuda:0",
+        "--dtype",
+        "bfloat16",
+        "--slots",
+        "4",
+        "--max-context",
+        "9216",
+        "--max-output",
+        "128",
+        "--kv",
+        "paged",
+        "--page-size",
+        "64",
+        "--chunk",
+        "256",
+        "--port",
+        "{port}",
+        "--pd-prefill-python",
+        "/tmp/stream265-python.sh",
+        "--pd-prefill-args",
+        "--engine factory --engine-factory streaming_factories:qwen --width 8192 --chip 0",
+        "--pd-prefill-stream-layers"
+      ]
+    ],
+    "metadata": {
+      "physical_gpus": 1,
+      "physical_rngd": 1,
+      "physical_atom": 0,
+      "slots_per_decoder": 4,
+      "producer_cache": false,
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+      "producer_chunk": 8192,
+      "local_chunk": 256,
+      "context": 9216
+    }
+  },
+  "load_s": 32.0464784129872,
+  "references": {
+    "128:32": {
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+      "text": " The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of"
+    },
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+      "text": " The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nOkay, so the user is asking about the capital of France,"
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+      "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user also provided a"
+    },
+    "512:128": {
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+    },
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+      "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user provided a bunch"
+    },
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+      "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user provided a bunch of repeated background context about boxes containing apples and books. That seems unrelated. Maybe they wanted me to use that context to answer the question? But the question is about the capital of France, which is a general knowledge fact. The background context doesn't mention anything about France or capitals. So maybe the user is testing if I can ignore irrelevant information? Or perhaps they included the context by mistake. Either way, the answer is Paris. I should make sure to confirm that even though"
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+      "text": " The capital of France is Paris.\nOkay, so the user is asking for the capital of France. Let me think. I know that Paris is the capital,"
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+      "text": " The capital of France is Paris.\nOkay, so the user is asking for the capital of France. Let me think. I know that Paris is the capital, but I should make sure there's no trick here. The user provided a lot of context about boxes with apples and books, but that seems unrelated. Maybe it's a test to see if I can ignore irrelevant information. Yeah, the answer should be Paris. I don't need to consider the boxes because the question is straightforward. Just need to recall that Paris is the capital of France. No need to overcomplicate it.\nThe capital of France is Paris. The information about"
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airm_stage_request_failures_total counter\nairm_stage_request_failures_total{model_name=\"Qwen3-4B\"} 0\n# HELP vllm:prompt_tokens_total Prompt tokens of finished requests.\n# TYPE vllm:prompt_tokens_total counter\nvllm:prompt_tokens_total{model_name=\"Qwen3-4B\"} 718080\n# HELP vllm:generation_tokens_total Generated tokens of finished requests.\n# TYPE vllm:generation_tokens_total counter\nvllm:generation_tokens_total{model_name=\"Qwen3-4B\"} 21120\n# HELP vllm:time_to_first_token_seconds Time to first token (prefill stage: fenced prefill).\n# TYPE vllm:time_to_first_token_seconds histogram\nvllm:time_to_first_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.001\"} 0\nvllm:time_to_first_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.005\"} 0\nvllm:time_to_first_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.01\"} 0\nvllm:time_to_first_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.02\"} 0\nvllm:time_to_first_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.04\"} 0\nvllm:time_to_first_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.06\"} 0\nvllm:time_to_first_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.08\"} 0\nvllm:time_to_first_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.1\"} 0\nvllm:time_to_first_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.25\"} 0\nvllm:time_to_first_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.5\"} 0\nvllm:time_to_first_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.75\"} 0\nvllm:time_to_first_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"1.0\"} 0\nvllm:time_to_first_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"2.5\"} 17\nvllm:time_to_first_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"5.0\"} 32\nvllm:time_to_first_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"7.5\"} 52\nvllm:time_to_first_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"10.0\"} 66\nvllm:time_to_first_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"+Inf\"} 264\nvllm:time_to_first_token_seconds_sum{model_name=\"Qwen3-4B\"} 5065.703591250756\nvllm:time_to_first_token_seconds_count{model_name=\"Qwen3-4B\"} 264\n# HELP vllm:time_per_output_token_seconds Time per output token after the first (decode stage).\n# TYPE vllm:time_per_output_token_seconds histogram\nvllm:time_per_output_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.01\"} 0\nvllm:time_per_output_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.025\"} 21\nvllm:time_per_output_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.05\"} 263\nvllm:time_per_output_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.075\"} 263\nvllm:time_per_output_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.1\"} 264\nvllm:time_per_output_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.15\"} 264\nvllm:time_per_output_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.2\"} 264\nvllm:time_per_output_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.3\"} 264\nvllm:time_per_output_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.4\"} 264\nvllm:time_per_output_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.5\"} 264\nvllm:time_per_output_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.75\"} 264\nvllm:time_per_output_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"1.0\"} 264\nvllm:time_per_output_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"2.5\"} 264\nvllm:time_per_output_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"+Inf\"} 264\nvllm:time_per_output_token_seconds_sum{model_name=\"Qwen3-4B\"} 7.504212190329107\nvllm:time_per_output_token_seconds_count{model_name=\"Qwen3-4B\"} 264\n# HELP vllm:e2e_request_latency_seconds Stage request latency, from execution start to reply.\n# TYPE vllm:e2e_request_latency_seconds histogram\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.3\"} 0\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.5\"} 0\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.8\"} 0\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Qwen3-4B\",le=\"1.0\"} 0\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Qwen3-4B\",le=\"1.5\"} 0\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Qwen3-4B\",le=\"2.0\"} 0\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Qwen3-4B\",le=\"2.5\"} 0\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Qwen3-4B\",le=\"5.0\"} 9\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Qwen3-4B\",le=\"10.0\"} 54\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Qwen3-4B\",le=\"15.0\"} 89\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Qwen3-4B\",le=\"20.0\"} 122\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Qwen3-4B\",le=\"30.0\"} 194\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Qwen3-4B\",le=\"40.0\"} 256\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Qwen3-4B\",le=\"50.0\"} 264\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Qwen3-4B\",le=\"60.0\"} 264\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Qwen3-4B\",le=\"+Inf\"} 264\nvllm:e2e_request_latency_seconds_sum{model_name=\"Qwen3-4B\"} 5652.730436959595\nvllm:e2e_request_latency_seconds_count{model_name=\"Qwen3-4B\"} 264\n# HELP airm_kv_transfer_seconds KV transfer time from the prefill source to the decode destination.\n# TYPE airm_kv_transfer_seconds histogram\nairm_kv_transfer_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.0005\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.001\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.0025\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.005\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.01\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.025\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.05\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.1\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.25\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.5\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"Qwen3-4B\",le=\"1.0\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"Qwen3-4B\",le=\"+Inf\"} 0\nairm_kv_transfer_seconds_sum{model_name=\"Qwen3-4B\"} 0.0\nairm_kv_transfer_seconds_count{model_name=\"Qwen3-4B\"} 0\n# HELP vllm:cache_config_info Information of the LLMEngine CacheConfig.\n# TYPE vllm:cache_config_info gauge\nvllm:cache_config_info{model_name=\"Qwen3-4B\",block_size=\"64\",num_gpu_blocks=\"576\",enable_prefix_caching=\"False\"} 1\n# HELP vllm:prefix_cache_queries_total Prompt tokens looked up in the prefix cache.\n# TYPE vllm:prefix_cache_queries_total counter\nvllm:prefix_cache_queries_total{model_name=\"Qwen3-4B\"} 718080\n# HELP vllm:prefix_cache_hits_total Prompt tokens served from the prefix cache.\n# TYPE vllm:prefix_cache_hits_total counter\nvllm:prefix_cache_hits_total{model_name=\"Qwen3-4B\"} 0\n# HELP airm_pd_prefills_total Prompts prefilled by the PD prefill worker (remote) or here (local).\n# TYPE airm_pd_prefills_total counter\nairm_pd_prefills_total{model_name=\"Qwen3-4B\",side=\"remote\"} 264\nairm_pd_prefills_total{model_name=\"Qwen3-4B\",side=\"local\"} 0\n# HELP airm_pd_transferred_tokens_total Prompt KV tokens received from the prefill side.\n# TYPE airm_pd_transferred_tokens_total counter\nairm_pd_transferred_tokens_total{model_name=\"Qwen3-4B\"} 718080\n# HELP airm_pd_producer_seconds_total Accumulated worker-reported seconds for admitted PD handoffs; excludes failed/cancelled handoffs. Prefill includes KV gathering; transfer excludes destination KV installation.\n# TYPE airm_pd_producer_seconds_total counter\nairm_pd_producer_seconds_total{model_name=\"Qwen3-4B\",stage=\"prefill\"} 611.6590946391225\nairm_pd_producer_seconds_total{model_name=\"Qwen3-4B\",stage=\"transfer\"} 0.0\n"
+  ],
+  "parity_passed": true
+}
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen4b-http-c16-whole.json b/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen4b-http-c16-whole.json
new file mode 100644
index 00000000..7d184766
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen4b-http-c16-whole.json
@@ -0,0 +1,25226 @@
+{
+  "passed": true,
+  "model": "Qwen3-4B",
+  "mode": "pd",
+  "dtype": "bfloat16",
+  "torch": "2.13.0+cu130",
+  "transformers": "5.15.0",
+  "replicas": 1,
+  "cpu_threads_per_server": 4,
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+          "text": " The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nOkay, so the user is asking about the capital of France,",
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+          "text": " The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nOkay, so the user is asking about the capital of France,",
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+          "text": " The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nOkay, so the user is asking about the capital of France,",
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+          "text": " The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nOkay, so the user is asking about the capital of France,",
+          "text_equal": true,
+          "first_sse_s": 32.828518120979425,
+          "tpot_est_s": 0.028563464645710662,
+          "e2e_s": 36.45607813098468
+        },
+        {
+          "id": "r0-n128-o128-c16-4",
+          "endpoint": 0,
+          "input_tokens": 128,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nOkay, so the user is asking about the capital of France,",
+          "text_equal": true,
+          "first_sse_s": 21.922258228994906,
+          "tpot_est_s": 0.028477514015536345,
+          "e2e_s": 25.53890250896802
+        },
+        {
+          "id": "r0-n128-o128-c16-5",
+          "endpoint": 0,
+          "input_tokens": 128,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nOkay, so the user is asking about the capital of France,",
+          "text_equal": true,
+          "first_sse_s": 16.454788423958234,
+          "tpot_est_s": 0.028764657086810082,
+          "e2e_s": 20.107899873983115
+        },
+        {
+          "id": "r0-n128-o128-c16-6",
+          "endpoint": 0,
+          "input_tokens": 128,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nOkay, so the user is asking about the capital of France,",
+          "text_equal": true,
+          "first_sse_s": 19.19438059197273,
+          "tpot_est_s": 0.028400036535229475,
+          "e2e_s": 22.801185231946874
+        },
+        {
+          "id": "r0-n128-o128-c16-7",
+          "endpoint": 0,
+          "input_tokens": 128,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nOkay, so the user is asking about the capital of France,",
+          "text_equal": true,
+          "first_sse_s": 40.99456069601001,
+          "tpot_est_s": 0.028053910740079197,
+          "e2e_s": 44.55740736000007
+        },
+        {
+          "id": "r0-n128-o128-c16-8",
+          "endpoint": 0,
+          "input_tokens": 128,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nOkay, so the user is asking about the capital of France,",
+          "text_equal": true,
+          "first_sse_s": 13.711849417013582,
+          "tpot_est_s": 0.027717020094035354,
+          "e2e_s": 17.231910968956072
+        },
+        {
+          "id": "r0-n128-o128-c16-9",
+          "endpoint": 0,
+          "input_tokens": 128,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nOkay, so the user is asking about the capital of France,",
+          "text_equal": true,
+          "first_sse_s": 43.73003938101465,
+          "tpot_est_s": 0.026676585173186676,
+          "e2e_s": 47.11796569800936
+        },
+        {
+          "id": "r0-n128-o128-c16-10",
+          "endpoint": 0,
+          "input_tokens": 128,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nOkay, so the user is asking about the capital of France,",
+          "text_equal": true,
+          "first_sse_s": 24.667175645998213,
+          "tpot_est_s": 0.02910345494490498,
+          "e2e_s": 28.363314424001146
+        },
+        {
+          "id": "r0-n128-o128-c16-11",
+          "endpoint": 0,
+          "input_tokens": 128,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nOkay, so the user is asking about the capital of France,",
+          "text_equal": true,
+          "first_sse_s": 2.709327488031704,
+          "tpot_est_s": 0.027948255425201744,
+          "e2e_s": 6.258755927032325
+        },
+        {
+          "id": "r0-n128-o128-c16-12",
+          "endpoint": 0,
+          "input_tokens": 128,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nOkay, so the user is asking about the capital of France,",
+          "text_equal": true,
+          "first_sse_s": 38.292009177966975,
+          "tpot_est_s": 0.029425978307212577,
+          "e2e_s": 42.02910842298297
+        },
+        {
+          "id": "r0-n128-o128-c16-13",
+          "endpoint": 0,
+          "input_tokens": 128,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nOkay, so the user is asking about the capital of France,",
+          "text_equal": true,
+          "first_sse_s": 35.564715622982476,
+          "tpot_est_s": 0.028841126606594332,
+          "e2e_s": 39.227538702019956
+        },
+        {
+          "id": "r0-n128-o128-c16-14",
+          "endpoint": 0,
+          "input_tokens": 128,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nOkay, so the user is asking about the capital of France,",
+          "text_equal": true,
+          "first_sse_s": 5.462945087987464,
+          "tpot_est_s": 0.02755590848804371,
+          "e2e_s": 8.962545465969015
+        },
+        {
+          "id": "r0-n128-o128-c16-15",
+          "endpoint": 0,
+          "input_tokens": 128,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nOkay, so the user is asking about the capital of France,",
+          "text_equal": true,
+          "first_sse_s": 10.950510567985475,
+          "tpot_est_s": 0.02864093555130182,
+          "e2e_s": 14.587909383000806
+        }
+      ]
+    },
+    {
+      "repeat": 0,
+      "input_tokens": 512,
+      "output_tokens_per_request": 32,
+      "concurrency": 16,
+      "summary": {
+        "requests": 16,
+        "output_tokens": 512,
+        "wall_s": 44.99134785705246,
+        "output_tok_s": 11.379965802019049,
+        "median_first_sse_s": 23.409665855026105,
+        "median_tpot_est_s": 0.02771560640370984,
+        "median_e2e_s": 24.29826425103238,
+        "all_text_equal": true
+      },
+      "rows": [
+        {
+          "id": "r0-n512-o32-c16-0",
+          "endpoint": 0,
+          "input_tokens": 512,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user also provided a",
+          "text_equal": true,
+          "first_sse_s": 2.800885499047581,
+          "tpot_est_s": 0.028199237611730613,
+          "e2e_s": 3.67506186501123
+        },
+        {
+          "id": "r0-n512-o32-c16-1",
+          "endpoint": 0,
+          "input_tokens": 512,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user also provided a",
+          "text_equal": true,
+          "first_sse_s": 38.52729344100226,
+          "tpot_est_s": 0.026937629514929628,
+          "e2e_s": 39.362359955965076
+        },
+        {
+          "id": "r0-n512-o32-c16-2",
+          "endpoint": 0,
+          "input_tokens": 512,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user also provided a",
+          "text_equal": true,
+          "first_sse_s": 11.029800368996803,
+          "tpot_est_s": 0.02952279458047762,
+          "e2e_s": 11.945007000991609
+        },
+        {
+          "id": "r0-n512-o32-c16-3",
+          "endpoint": 0,
+          "input_tokens": 512,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user also provided a",
+          "text_equal": true,
+          "first_sse_s": 13.78740359202493,
+          "tpot_est_s": 0.02822078629058876,
+          "e2e_s": 14.662247967033181
+        },
+        {
+          "id": "r0-n512-o32-c16-4",
+          "endpoint": 0,
+          "input_tokens": 512,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user also provided a",
+          "text_equal": true,
+          "first_sse_s": 27.495054418977816,
+          "tpot_est_s": 0.02768218929075726,
+          "e2e_s": 28.35320228699129
+        },
+        {
+          "id": "r0-n512-o32-c16-5",
+          "endpoint": 0,
+          "input_tokens": 512,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user also provided a",
+          "text_equal": true,
+          "first_sse_s": 24.780679188028444,
+          "tpot_est_s": 0.027410429806613755,
+          "e2e_s": 25.63040251203347
+        },
+        {
+          "id": "r0-n512-o32-c16-6",
+          "endpoint": 0,
+          "input_tokens": 512,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user also provided a",
+          "text_equal": true,
+          "first_sse_s": 35.77443497197237,
+          "tpot_est_s": 0.02708322996829426,
+          "e2e_s": 36.61401510098949
+        },
+        {
+          "id": "r0-n512-o32-c16-7",
+          "endpoint": 0,
+          "input_tokens": 512,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user also provided a",
+          "text_equal": true,
+          "first_sse_s": 44.120522505021654,
+          "tpot_est_s": 0.028028810838042127,
+          "e2e_s": 44.98941564100096
+        },
+        {
+          "id": "r0-n512-o32-c16-8",
+          "endpoint": 0,
+          "input_tokens": 512,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user also provided a",
+          "text_equal": true,
+          "first_sse_s": 30.253088835976087,
+          "tpot_est_s": 0.027741529257787813,
+          "e2e_s": 31.11307624296751
+        },
+        {
+          "id": "r0-n512-o32-c16-9",
+          "endpoint": 0,
+          "input_tokens": 512,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user also provided a",
+          "text_equal": true,
+          "first_sse_s": 8.291902641998604,
+          "tpot_est_s": 0.02914264509754796,
+          "e2e_s": 9.19532464002259
+        },
+        {
+          "id": "r0-n512-o32-c16-10",
+          "endpoint": 0,
+          "input_tokens": 512,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user also provided a",
+          "text_equal": true,
+          "first_sse_s": 41.350641358993016,
+          "tpot_est_s": 0.027311546743036277,
+          "e2e_s": 42.19729930802714
+        },
+        {
+          "id": "r0-n512-o32-c16-11",
+          "endpoint": 0,
+          "input_tokens": 512,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user also provided a",
+          "text_equal": true,
+          "first_sse_s": 22.038652522023767,
+          "tpot_est_s": 0.02991849896798451,
+          "e2e_s": 22.966125990031287
+        },
+        {
+          "id": "r0-n512-o32-c16-12",
+          "endpoint": 0,
+          "input_tokens": 512,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user also provided a",
+          "text_equal": true,
+          "first_sse_s": 33.03085938398726,
+          "tpot_est_s": 0.027689683549631867,
+          "e2e_s": 33.88923957402585
+        },
+        {
+          "id": "r0-n512-o32-c16-13",
+          "endpoint": 0,
+          "input_tokens": 512,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user also provided a",
+          "text_equal": true,
+          "first_sse_s": 19.27866966201691,
+          "tpot_est_s": 0.028765117805901793,
+          "e2e_s": 20.170388313999865
+        },
+        {
+          "id": "r0-n512-o32-c16-14",
+          "endpoint": 0,
+          "input_tokens": 512,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user also provided a",
+          "text_equal": true,
+          "first_sse_s": 16.523101443017367,
+          "tpot_est_s": 0.02715453761254227,
+          "e2e_s": 17.364892109006178
+        },
+        {
+          "id": "r0-n512-o32-c16-15",
+          "endpoint": 0,
+          "input_tokens": 512,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user also provided a",
+          "text_equal": true,
+          "first_sse_s": 5.549827979004476,
+          "tpot_est_s": 0.02714113532302661,
+          "e2e_s": 6.391203174018301
+        }
+      ]
+    },
+    {
+      "repeat": 0,
+      "input_tokens": 512,
+      "output_tokens_per_request": 128,
+      "concurrency": 16,
+      "summary": {
+        "requests": 16,
+        "output_tokens": 2048,
+        "wall_s": 48.099250981002115,
+        "output_tok_s": 42.57862561745304,
+        "median_first_sse_s": 23.656272448017262,
+        "median_tpot_est_s": 0.02792518417347281,
+        "median_e2e_s": 27.210936400020728,
+        "all_text_equal": true
+      },
+      "rows": [
+        {
+          "id": "r0-n512-o128-c16-0",
+          "endpoint": 0,
+          "input_tokens": 512,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user also provided a bunch of repeated background context about boxes containing apples and books. That seems unrelated. Maybe they wanted me to use that context for the question? But the question was straightforward. Did I miss something?\n\nLet me check again. The user's question was \"What is the capital of France?\" and the answer is Paris. The background context about boxes is probably just extra information, maybe a test to see if I can focus on the actual question. Or maybe it's part of a larger",
+          "text_equal": true,
+          "first_sse_s": 13.851761453959625,
+          "tpot_est_s": 0.027961385228733465,
+          "e2e_s": 17.402857378008775
+        },
+        {
+          "id": "r0-n512-o128-c16-1",
+          "endpoint": 0,
+          "input_tokens": 512,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user also provided a bunch of repeated background context about boxes containing apples and books. That seems unrelated. Maybe they wanted me to use that context for the question? But the question was straightforward. Did I miss something?\n\nLet me check again. The user's question was \"What is the capital of France?\" and the answer is Paris. The background context about boxes is probably just extra information, maybe a test to see if I can focus on the actual question. Or maybe it's part of a larger",
+          "text_equal": true,
+          "first_sse_s": 41.82248846901348,
+          "tpot_est_s": 0.02667291000014259,
+          "e2e_s": 45.20994803903159
+        },
+        {
+          "id": "r0-n512-o128-c16-2",
+          "endpoint": 0,
+          "input_tokens": 512,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user also provided a bunch of repeated background context about boxes containing apples and books. That seems unrelated. Maybe they wanted me to use that context for the question? But the question was straightforward. Did I miss something?\n\nLet me check again. The user's question was \"What is the capital of France?\" and the answer is Paris. The background context about boxes is probably just extra information, maybe a test to see if I can focus on the actual question. Or maybe it's part of a larger",
+          "text_equal": true,
+          "first_sse_s": 22.26910581899574,
+          "tpot_est_s": 0.027888983118212158,
+          "e2e_s": 25.811006675008684
+        },
+        {
+          "id": "r0-n512-o128-c16-3",
+          "endpoint": 0,
+          "input_tokens": 512,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user also provided a bunch of repeated background context about boxes containing apples and books. That seems unrelated. Maybe they wanted me to use that context for the question? But the question was straightforward. Did I miss something?\n\nLet me check again. The user's question was \"What is the capital of France?\" and the answer is Paris. The background context about boxes is probably just extra information, maybe a test to see if I can focus on the actual question. Or maybe it's part of a larger",
+          "text_equal": true,
+          "first_sse_s": 19.43794200802222,
+          "tpot_est_s": 0.028344625519592256,
+          "e2e_s": 23.037709449010435
+        },
+        {
+          "id": "r0-n512-o128-c16-4",
+          "endpoint": 0,
+          "input_tokens": 512,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user also provided a bunch of repeated background context about boxes containing apples and books. That seems unrelated. Maybe they wanted me to use that context for the question? But the question was straightforward. Did I miss something?\n\nLet me check again. The user's question was \"What is the capital of France?\" and the answer is Paris. The background context about boxes is probably just extra information, maybe a test to see if I can focus on the actual question. Or maybe it's part of a larger",
+          "text_equal": true,
+          "first_sse_s": 25.043439077038784,
+          "tpot_est_s": 0.02808997675585817,
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+        {
+          "id": "r0-n512-o128-c16-5",
+          "endpoint": 0,
+          "input_tokens": 512,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user also provided a bunch of repeated background context about boxes containing apples and books. That seems unrelated. Maybe they wanted me to use that context for the question? But the question was straightforward. Did I miss something?\n\nLet me check again. The user's question was \"What is the capital of France?\" and the answer is Paris. The background context about boxes is probably just extra information, maybe a test to see if I can focus on the actual question. Or maybe it's part of a larger",
+          "text_equal": true,
+          "first_sse_s": 16.585214579012245,
+          "tpot_est_s": 0.028878295031471515,
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+        {
+          "id": "r0-n512-o128-c16-6",
+          "endpoint": 0,
+          "input_tokens": 512,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user also provided a bunch of repeated background context about boxes containing apples and books. That seems unrelated. Maybe they wanted me to use that context for the question? But the question was straightforward. Did I miss something?\n\nLet me check again. The user's question was \"What is the capital of France?\" and the answer is Paris. The background context about boxes is probably just extra information, maybe a test to see if I can focus on the actual question. Or maybe it's part of a larger",
+          "text_equal": true,
+          "first_sse_s": 30.610653382958844,
+          "tpot_est_s": 0.027687914472432646,
+          "e2e_s": 34.12701852095779
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+        {
+          "id": "r0-n512-o128-c16-7",
+          "endpoint": 0,
+          "input_tokens": 512,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user also provided a bunch of repeated background context about boxes containing apples and books. That seems unrelated. Maybe they wanted me to use that context for the question? But the question was straightforward. Did I miss something?\n\nLet me check again. The user's question was \"What is the capital of France?\" and the answer is Paris. The background context about boxes is probably just extra information, maybe a test to see if I can focus on the actual question. Or maybe it's part of a larger",
+          "text_equal": true,
+          "first_sse_s": 27.837884064996615,
+          "tpot_est_s": 0.027829369110253264,
+          "e2e_s": 31.37221394199878
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+        {
+          "id": "r0-n512-o128-c16-8",
+          "endpoint": 0,
+          "input_tokens": 512,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user also provided a bunch of repeated background context about boxes containing apples and books. That seems unrelated. Maybe they wanted me to use that context for the question? But the question was straightforward. Did I miss something?\n\nLet me check again. The user's question was \"What is the capital of France?\" and the answer is Paris. The background context about boxes is probably just extra information, maybe a test to see if I can focus on the actual question. Or maybe it's part of a larger",
+          "text_equal": true,
+          "first_sse_s": 44.64434438297758,
+          "tpot_est_s": 0.027197608197159245,
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+        {
+          "id": "r0-n512-o128-c16-9",
+          "endpoint": 0,
+          "input_tokens": 512,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user also provided a bunch of repeated background context about boxes containing apples and books. That seems unrelated. Maybe they wanted me to use that context for the question? But the question was straightforward. Did I miss something?\n\nLet me check again. The user's question was \"What is the capital of France?\" and the answer is Paris. The background context about boxes is probably just extra information, maybe a test to see if I can focus on the actual question. Or maybe it's part of a larger",
+          "text_equal": true,
+          "first_sse_s": 8.39022489398485,
+          "tpot_est_s": 0.028957075866096926,
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+        {
+          "id": "r0-n512-o128-c16-10",
+          "endpoint": 0,
+          "input_tokens": 512,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user also provided a bunch of repeated background context about boxes containing apples and books. That seems unrelated. Maybe they wanted me to use that context for the question? But the question was straightforward. Did I miss something?\n\nLet me check again. The user's question was \"What is the capital of France?\" and the answer is Paris. The background context about boxes is probably just extra information, maybe a test to see if I can focus on the actual question. Or maybe it's part of a larger",
+          "text_equal": true,
+          "first_sse_s": 5.6207816419773735,
+          "tpot_est_s": 0.028847603307024936,
+          "e2e_s": 9.28442726196954
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+        {
+          "id": "r0-n512-o128-c16-11",
+          "endpoint": 0,
+          "input_tokens": 512,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user also provided a bunch of repeated background context about boxes containing apples and books. That seems unrelated. Maybe they wanted me to use that context for the question? But the question was straightforward. Did I miss something?\n\nLet me check again. The user's question was \"What is the capital of France?\" and the answer is Paris. The background context about boxes is probably just extra information, maybe a test to see if I can focus on the actual question. Or maybe it's part of a larger",
+          "text_equal": true,
+          "first_sse_s": 36.09325630398234,
+          "tpot_est_s": 0.027566722953072418,
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+        {
+          "id": "r0-n512-o128-c16-12",
+          "endpoint": 0,
+          "input_tokens": 512,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user also provided a bunch of repeated background context about boxes containing apples and books. That seems unrelated. Maybe they wanted me to use that context for the question? But the question was straightforward. Did I miss something?\n\nLet me check again. The user's question was \"What is the capital of France?\" and the answer is Paris. The background context about boxes is probably just extra information, maybe a test to see if I can focus on the actual question. Or maybe it's part of a larger",
+          "text_equal": true,
+          "first_sse_s": 2.773874252976384,
+          "tpot_est_s": 0.02830993957491225,
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+        {
+          "id": "r0-n512-o128-c16-13",
+          "endpoint": 0,
+          "input_tokens": 512,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user also provided a bunch of repeated background context about boxes containing apples and books. That seems unrelated. Maybe they wanted me to use that context for the question? But the question was straightforward. Did I miss something?\n\nLet me check again. The user's question was \"What is the capital of France?\" and the answer is Paris. The background context about boxes is probably just extra information, maybe a test to see if I can focus on the actual question. Or maybe it's part of a larger",
+          "text_equal": true,
+          "first_sse_s": 11.149694107007235,
+          "tpot_est_s": 0.028582481945146197,
+          "e2e_s": 14.779669314040802
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+        {
+          "id": "r0-n512-o128-c16-14",
+          "endpoint": 0,
+          "input_tokens": 512,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user also provided a bunch of repeated background context about boxes containing apples and books. That seems unrelated. Maybe they wanted me to use that context for the question? But the question was straightforward. Did I miss something?\n\nLet me check again. The user's question was \"What is the capital of France?\" and the answer is Paris. The background context about boxes is probably just extra information, maybe a test to see if I can focus on the actual question. Or maybe it's part of a larger",
+          "text_equal": true,
+          "first_sse_s": 38.84945814299863,
+          "tpot_est_s": 0.02663679289774544,
+          "e2e_s": 42.2323308410123
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+        {
+          "id": "r0-n512-o128-c16-15",
+          "endpoint": 0,
+          "input_tokens": 512,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user also provided a bunch of repeated background context about boxes containing apples and books. That seems unrelated. Maybe they wanted me to use that context for the question? But the question was straightforward. Did I miss something?\n\nLet me check again. The user's question was \"What is the capital of France?\" and the answer is Paris. The background context about boxes is probably just extra information, maybe a test to see if I can focus on the actual question. Or maybe it's part of a larger",
+          "text_equal": true,
+          "first_sse_s": 33.35140311100986,
+          "tpot_est_s": 0.027683702503756333,
+          "e2e_s": 36.86723332898691
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+    {
+      "repeat": 0,
+      "input_tokens": 2048,
+      "output_tokens_per_request": 32,
+      "concurrency": 16,
+      "summary": {
+        "requests": 16,
+        "output_tokens": 512,
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+        "median_e2e_s": 25.32433363798191,
+        "all_text_equal": true
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+      "rows": [
+        {
+          "id": "r0-n2048-o32-c16-0",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user provided a bunch",
+          "text_equal": true,
+          "first_sse_s": 20.180089855974074,
+          "tpot_est_s": 0.029218294485021504,
+          "e2e_s": 21.08585698500974
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+        {
+          "id": "r0-n2048-o32-c16-1",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user provided a bunch",
+          "text_equal": true,
+          "first_sse_s": 2.9502906809793785,
+          "tpot_est_s": 0.027370939547601607,
+          "e2e_s": 3.7987898069550283
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+        {
+          "id": "r0-n2048-o32-c16-2",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user provided a bunch",
+          "text_equal": true,
+          "first_sse_s": 8.617263789987192,
+          "tpot_est_s": 0.0274113038378287,
+          "e2e_s": 9.467014208959881
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+        {
+          "id": "r0-n2048-o32-c16-3",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user provided a bunch",
+          "text_equal": true,
+          "first_sse_s": 40.29644720297074,
+          "tpot_est_s": 0.0258705792591847,
+          "e2e_s": 41.098435160005465
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+        {
+          "id": "r0-n2048-o32-c16-4",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user provided a bunch",
+          "text_equal": true,
+          "first_sse_s": 11.465903761039954,
+          "tpot_est_s": 0.026925366935169986,
+          "e2e_s": 12.300590136030223
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+        {
+          "id": "r0-n2048-o32-c16-5",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user provided a bunch",
+          "text_equal": true,
+          "first_sse_s": 25.885214740002993,
+          "tpot_est_s": 0.026165401193148065,
+          "e2e_s": 26.696342176990584
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+        {
+          "id": "r0-n2048-o32-c16-6",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user provided a bunch",
+          "text_equal": true,
+          "first_sse_s": 31.569308120000642,
+          "tpot_est_s": 0.02782233857977835,
+          "e2e_s": 32.43180061597377
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+        {
+          "id": "r0-n2048-o32-c16-7",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user provided a bunch",
+          "text_equal": true,
+          "first_sse_s": 23.11914295499446,
+          "tpot_est_s": 0.026876843354154016,
+          "e2e_s": 23.952325098973233
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+        {
+          "id": "r0-n2048-o32-c16-8",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user provided a bunch",
+          "text_equal": true,
+          "first_sse_s": 5.7962648090324365,
+          "tpot_est_s": 0.027273063386429944,
+          "e2e_s": 6.641729774011765
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+        {
+          "id": "r0-n2048-o32-c16-9",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user provided a bunch",
+          "text_equal": true,
+          "first_sse_s": 46.33496466599172,
+          "tpot_est_s": 0.02665346532293986,
+          "e2e_s": 47.161222091002855
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+        {
+          "id": "r0-n2048-o32-c16-10",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user provided a bunch",
+          "text_equal": true,
+          "first_sse_s": 28.701105991960503,
+          "tpot_est_s": 0.026572805581494205,
+          "e2e_s": 29.524862964986823
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+        {
+          "id": "r0-n2048-o32-c16-11",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user provided a bunch",
+          "text_equal": true,
+          "first_sse_s": 43.27513118303614,
+          "tpot_est_s": 0.027071638483433955,
+          "e2e_s": 44.11435197602259
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+        {
+          "id": "r0-n2048-o32-c16-12",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user provided a bunch",
+          "text_equal": true,
+          "first_sse_s": 17.177353565988597,
+          "tpot_est_s": 0.02807178270942982,
+          "e2e_s": 18.04757882998092
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+        {
+          "id": "r0-n2048-o32-c16-13",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user provided a bunch",
+          "text_equal": true,
+          "first_sse_s": 37.36928819003515,
+          "tpot_est_s": 0.028054952677789954,
+          "e2e_s": 38.23899172304664
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+        {
+          "id": "r0-n2048-o32-c16-14",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user provided a bunch",
+          "text_equal": true,
+          "first_sse_s": 14.320239024003968,
+          "tpot_est_s": 0.02667721893428074,
+          "e2e_s": 15.14723281096667
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+        {
+          "id": "r0-n2048-o32-c16-15",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user provided a bunch",
+          "text_equal": true,
+          "first_sse_s": 34.387158835015725,
+          "tpot_est_s": 0.025990048967932743,
+          "e2e_s": 35.19285035302164
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+    {
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+      "input_tokens": 2048,
+      "output_tokens_per_request": 128,
+      "concurrency": 16,
+      "summary": {
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+        "output_tokens": 2048,
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+        {
+          "id": "r0-n2048-o128-c16-0",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user provided a bunch of repeated background context about boxes containing apples and books. That seems unrelated. Maybe they wanted me to use that context to answer the question? But the question is about the capital of France, which is a general knowledge fact. The background context doesn't mention anything about France or capitals. So maybe the user is testing if I can ignore irrelevant information? Or perhaps they included the context by mistake. Either way, the answer is Paris. I should make sure to confirm that even though",
+          "text_equal": true,
+          "first_sse_s": 2.931829135050066,
+          "tpot_est_s": 0.029693349771472942,
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+        {
+          "id": "r0-n2048-o128-c16-1",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user provided a bunch of repeated background context about boxes containing apples and books. That seems unrelated. Maybe they wanted me to use that context to answer the question? But the question is about the capital of France, which is a general knowledge fact. The background context doesn't mention anything about France or capitals. So maybe the user is testing if I can ignore irrelevant information? Or perhaps they included the context by mistake. Either way, the answer is Paris. I should make sure to confirm that even though",
+          "text_equal": true,
+          "first_sse_s": 26.039966294018086,
+          "tpot_est_s": 0.028727332472445546,
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+        {
+          "id": "r0-n2048-o128-c16-2",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user provided a bunch of repeated background context about boxes containing apples and books. That seems unrelated. Maybe they wanted me to use that context to answer the question? But the question is about the capital of France, which is a general knowledge fact. The background context doesn't mention anything about France or capitals. So maybe the user is testing if I can ignore irrelevant information? Or perhaps they included the context by mistake. Either way, the answer is Paris. I should make sure to confirm that even though",
+          "text_equal": true,
+          "first_sse_s": 23.198269039974548,
+          "tpot_est_s": 0.029122176574631117,
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+        {
+          "id": "r0-n2048-o128-c16-3",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user provided a bunch of repeated background context about boxes containing apples and books. That seems unrelated. Maybe they wanted me to use that context to answer the question? But the question is about the capital of France, which is a general knowledge fact. The background context doesn't mention anything about France or capitals. So maybe the user is testing if I can ignore irrelevant information? Or perhaps they included the context by mistake. Either way, the answer is Paris. I should make sure to confirm that even though",
+          "text_equal": true,
+          "first_sse_s": 11.685569002991542,
+          "tpot_est_s": 0.028493707369844332,
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+        {
+          "id": "r0-n2048-o128-c16-4",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user provided a bunch of repeated background context about boxes containing apples and books. That seems unrelated. Maybe they wanted me to use that context to answer the question? But the question is about the capital of France, which is a general knowledge fact. The background context doesn't mention anything about France or capitals. So maybe the user is testing if I can ignore irrelevant information? Or perhaps they included the context by mistake. Either way, the answer is Paris. I should make sure to confirm that even though",
+          "text_equal": true,
+          "first_sse_s": 17.434758713003248,
+          "tpot_est_s": 0.028348901275615023,
+          "e2e_s": 21.035069175006356
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+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user provided a bunch of repeated background context about boxes containing apples and books. That seems unrelated. Maybe they wanted me to use that context to answer the question? But the question is about the capital of France, which is a general knowledge fact. The background context doesn't mention anything about France or capitals. So maybe the user is testing if I can ignore irrelevant information? Or perhaps they included the context by mistake. Either way, the answer is Paris. I should make sure to confirm that even though",
+          "text_equal": true,
+          "first_sse_s": 14.548540504998527,
+          "tpot_est_s": 0.02812591750370925,
+          "e2e_s": 18.120532027969602
+        },
+        {
+          "id": "r0-n2048-o128-c16-6",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user provided a bunch of repeated background context about boxes containing apples and books. That seems unrelated. Maybe they wanted me to use that context to answer the question? But the question is about the capital of France, which is a general knowledge fact. The background context doesn't mention anything about France or capitals. So maybe the user is testing if I can ignore irrelevant information? Or perhaps they included the context by mistake. Either way, the answer is Paris. I should make sure to confirm that even though",
+          "text_equal": true,
+          "first_sse_s": 31.804322298034094,
+          "tpot_est_s": 0.029137357858130635,
+          "e2e_s": 35.504766746016685
+        },
+        {
+          "id": "r0-n2048-o128-c16-7",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user provided a bunch of repeated background context about boxes containing apples and books. That seems unrelated. Maybe they wanted me to use that context to answer the question? But the question is about the capital of France, which is a general knowledge fact. The background context doesn't mention anything about France or capitals. So maybe the user is testing if I can ignore irrelevant information? Or perhaps they included the context by mistake. Either way, the answer is Paris. I should make sure to confirm that even though",
+          "text_equal": true,
+          "first_sse_s": 8.730217655014712,
+          "tpot_est_s": 0.027993429511763507,
+          "e2e_s": 12.285383203008678
+        },
+        {
+          "id": "r0-n2048-o128-c16-8",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user provided a bunch of repeated background context about boxes containing apples and books. That seems unrelated. Maybe they wanted me to use that context to answer the question? But the question is about the capital of France, which is a general knowledge fact. The background context doesn't mention anything about France or capitals. So maybe the user is testing if I can ignore irrelevant information? Or perhaps they included the context by mistake. Either way, the answer is Paris. I should make sure to confirm that even though",
+          "text_equal": true,
+          "first_sse_s": 20.331899851036724,
+          "tpot_est_s": 0.029043474826800423,
+          "e2e_s": 24.020421154040378
+        },
+        {
+          "id": "r0-n2048-o128-c16-9",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user provided a bunch of repeated background context about boxes containing apples and books. That seems unrelated. Maybe they wanted me to use that context to answer the question? But the question is about the capital of France, which is a general knowledge fact. The background context doesn't mention anything about France or capitals. So maybe the user is testing if I can ignore irrelevant information? Or perhaps they included the context by mistake. Either way, the answer is Paris. I should make sure to confirm that even though",
+          "text_equal": true,
+          "first_sse_s": 43.19215008098399,
+          "tpot_est_s": 0.029199045779381504,
+          "e2e_s": 46.900428894965444
+        },
+        {
+          "id": "r0-n2048-o128-c16-10",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user provided a bunch of repeated background context about boxes containing apples and books. That seems unrelated. Maybe they wanted me to use that context to answer the question? But the question is about the capital of France, which is a general knowledge fact. The background context doesn't mention anything about France or capitals. So maybe the user is testing if I can ignore irrelevant information? Or perhaps they included the context by mistake. Either way, the answer is Paris. I should make sure to confirm that even though",
+          "text_equal": true,
+          "first_sse_s": 34.65938978001941,
+          "tpot_est_s": 0.029125249535340347,
+          "e2e_s": 38.358296471007634
+        },
+        {
+          "id": "r0-n2048-o128-c16-11",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user provided a bunch of repeated background context about boxes containing apples and books. That seems unrelated. Maybe they wanted me to use that context to answer the question? But the question is about the capital of France, which is a general knowledge fact. The background context doesn't mention anything about France or capitals. So maybe the user is testing if I can ignore irrelevant information? Or perhaps they included the context by mistake. Either way, the answer is Paris. I should make sure to confirm that even though",
+          "text_equal": true,
+          "first_sse_s": 28.853945464012213,
+          "tpot_est_s": 0.0288177315433561,
+          "e2e_s": 32.51379737001844
+        },
+        {
+          "id": "r0-n2048-o128-c16-12",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user provided a bunch of repeated background context about boxes containing apples and books. That seems unrelated. Maybe they wanted me to use that context to answer the question? But the question is about the capital of France, which is a general knowledge fact. The background context doesn't mention anything about France or capitals. So maybe the user is testing if I can ignore irrelevant information? Or perhaps they included the context by mistake. Either way, the answer is Paris. I should make sure to confirm that even though",
+          "text_equal": true,
+          "first_sse_s": 40.20854010095354,
+          "tpot_est_s": 0.028789886236319128,
+          "e2e_s": 43.86485565296607
+        },
+        {
+          "id": "r0-n2048-o128-c16-13",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user provided a bunch of repeated background context about boxes containing apples and books. That seems unrelated. Maybe they wanted me to use that context to answer the question? But the question is about the capital of France, which is a general knowledge fact. The background context doesn't mention anything about France or capitals. So maybe the user is testing if I can ignore irrelevant information? Or perhaps they included the context by mistake. Either way, the answer is Paris. I should make sure to confirm that even though",
+          "text_equal": true,
+          "first_sse_s": 46.18213754001772,
+          "tpot_est_s": 0.02706509696064869,
+          "e2e_s": 49.619404854020104
+        },
+        {
+          "id": "r0-n2048-o128-c16-14",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user provided a bunch of repeated background context about boxes containing apples and books. That seems unrelated. Maybe they wanted me to use that context to answer the question? But the question is about the capital of France, which is a general knowledge fact. The background context doesn't mention anything about France or capitals. So maybe the user is testing if I can ignore irrelevant information? Or perhaps they included the context by mistake. Either way, the answer is Paris. I should make sure to confirm that even though",
+          "text_equal": true,
+          "first_sse_s": 5.864467989013065,
+          "tpot_est_s": 0.02874023570884459,
+          "e2e_s": 9.514477924036328
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+        {
+          "id": "r0-n2048-o128-c16-15",
+          "endpoint": 0,
+          "input_tokens": 2048,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user provided a bunch of repeated background context about boxes containing apples and books. That seems unrelated. Maybe they wanted me to use that context to answer the question? But the question is about the capital of France, which is a general knowledge fact. The background context doesn't mention anything about France or capitals. So maybe the user is testing if I can ignore irrelevant information? Or perhaps they included the context by mistake. Either way, the answer is Paris. I should make sure to confirm that even though",
+          "text_equal": true,
+          "first_sse_s": 37.40403993596556,
+          "tpot_est_s": 0.02885805855149506,
+          "e2e_s": 41.06901337200543
+        }
+      ]
+    },
+    {
+      "repeat": 0,
+      "input_tokens": 8192,
+      "output_tokens_per_request": 32,
+      "concurrency": 16,
+      "summary": {
+        "requests": 16,
+        "output_tokens": 512,
+        "wall_s": 50.719234908989165,
+        "output_tok_s": 10.094789499856123,
+        "median_first_sse_s": 28.51709923002636,
+        "median_tpot_est_s": 0.028653154274516347,
+        "median_e2e_s": 29.571128801529994,
+        "all_text_equal": true
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+      "rows": [
+        {
+          "id": "r0-n8192-o32-c16-0",
+          "endpoint": 0,
+          "input_tokens": 8192,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user is asking for the capital of France. Let me think. I know that Paris is the capital,",
+          "text_equal": true,
+          "first_sse_s": 19.63908747199457,
+          "tpot_est_s": 0.02800139996801473,
+          "e2e_s": 20.507130871003028
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+        {
+          "id": "r0-n8192-o32-c16-1",
+          "endpoint": 0,
+          "input_tokens": 8192,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user is asking for the capital of France. Let me think. I know that Paris is the capital,",
+          "text_equal": true,
+          "first_sse_s": 25.238138849963434,
+          "tpot_est_s": 0.04058675777407423,
+          "e2e_s": 26.496328340959735
+        },
+        {
+          "id": "r0-n8192-o32-c16-2",
+          "endpoint": 0,
+          "input_tokens": 8192,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user is asking for the capital of France. Let me think. I know that Paris is the capital,",
+          "text_equal": true,
+          "first_sse_s": 13.390963044017553,
+          "tpot_est_s": 0.027593266290252008,
+          "e2e_s": 14.246354299015366
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+        {
+          "id": "r0-n8192-o32-c16-3",
+          "endpoint": 0,
+          "input_tokens": 8192,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user is asking for the capital of France. Let me think. I know that Paris is the capital,",
+          "text_equal": true,
+          "first_sse_s": 31.78309939103201,
+          "tpot_est_s": 0.027842497321656876,
+          "e2e_s": 32.64621680800337
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+        {
+          "id": "r0-n8192-o32-c16-4",
+          "endpoint": 0,
+          "input_tokens": 8192,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user is asking for the capital of France. Let me think. I know that Paris is the capital,",
+          "text_equal": true,
+          "first_sse_s": 6.830606720992364,
+          "tpot_est_s": 0.03903104287136586,
+          "e2e_s": 8.040569050004706
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+        {
+          "id": "r0-n8192-o32-c16-5",
+          "endpoint": 0,
+          "input_tokens": 8192,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user is asking for the capital of France. Let me think. I know that Paris is the capital,",
+          "text_equal": true,
+          "first_sse_s": 7.182683225022629,
+          "tpot_est_s": 0.02765917409616973,
+          "e2e_s": 8.04011762200389
+        },
+        {
+          "id": "r0-n8192-o32-c16-6",
+          "endpoint": 0,
+          "input_tokens": 8192,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user is asking for the capital of France. Let me think. I know that Paris is the capital,",
+          "text_equal": true,
+          "first_sse_s": 37.64320523600327,
+          "tpot_est_s": 0.0394285270970525,
+          "e2e_s": 38.865489576011896
+        },
+        {
+          "id": "r0-n8192-o32-c16-7",
+          "endpoint": 0,
+          "input_tokens": 8192,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user is asking for the capital of France. Let me think. I know that Paris is the capital,",
+          "text_equal": true,
+          "first_sse_s": 44.05205387499882,
+          "tpot_est_s": 0.028664060290001573,
+          "e2e_s": 44.94063974398887
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+        {
+          "id": "r0-n8192-o32-c16-8",
+          "endpoint": 0,
+          "input_tokens": 8192,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user is asking for the capital of France. Let me think. I know that Paris is the capital,",
+          "text_equal": true,
+          "first_sse_s": 37.98811876802938,
+          "tpot_est_s": 0.02826760022623104,
+          "e2e_s": 38.86441437504254
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+        {
+          "id": "r0-n8192-o32-c16-9",
+          "endpoint": 0,
+          "input_tokens": 8192,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user is asking for the capital of France. Let me think. I know that Paris is the capital,",
+          "text_equal": true,
+          "first_sse_s": 19.28088098898297,
+          "tpot_est_s": 0.039489131614250404,
+          "e2e_s": 20.505044069024734
+        },
+        {
+          "id": "r0-n8192-o32-c16-10",
+          "endpoint": 0,
+          "input_tokens": 8192,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user is asking for the capital of France. Let me think. I know that Paris is the capital,",
+          "text_equal": true,
+          "first_sse_s": 49.86238982400391,
+          "tpot_est_s": 0.027550692256960656,
+          "e2e_s": 50.71646128396969
+        },
+        {
+          "id": "r0-n8192-o32-c16-11",
+          "endpoint": 0,
+          "input_tokens": 8192,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user is asking for the capital of France. Let me think. I know that Paris is the capital,",
+          "text_equal": true,
+          "first_sse_s": 25.60859196301317,
+          "tpot_est_s": 0.028642248259031125,
+          "e2e_s": 26.496501659043133
+        },
+        {
+          "id": "r0-n8192-o32-c16-12",
+          "endpoint": 0,
+          "input_tokens": 8192,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user is asking for the capital of France. Let me think. I know that Paris is the capital,",
+          "text_equal": true,
+          "first_sse_s": 13.02434538397938,
+          "tpot_est_s": 0.0393405384196329,
+          "e2e_s": 14.243902074988
+        },
+        {
+          "id": "r0-n8192-o32-c16-13",
+          "endpoint": 0,
+          "input_tokens": 8192,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user is asking for the capital of France. Let me think. I know that Paris is the capital,",
+          "text_equal": true,
+          "first_sse_s": 46.86278152599698,
+          "tpot_est_s": 0.02791303219348793,
+          "e2e_s": 47.728085523995105
+        },
+        {
+          "id": "r0-n8192-o32-c16-14",
+          "endpoint": 0,
+          "input_tokens": 8192,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user is asking for the capital of France. Let me think. I know that Paris is the capital,",
+          "text_equal": true,
+          "first_sse_s": 43.69913799298229,
+          "tpot_est_s": 0.03999159022632445,
+          "e2e_s": 44.938877289998345
+        },
+        {
+          "id": "r0-n8192-o32-c16-15",
+          "endpoint": 0,
+          "input_tokens": 8192,
+          "output_tokens": 32,
+          "text": " The capital of France is Paris.\nOkay, so the user is asking for the capital of France. Let me think. I know that Paris is the capital,",
+          "text_equal": true,
+          "first_sse_s": 31.425606497039553,
+          "tpot_est_s": 0.039359659579912984,
+          "e2e_s": 32.645755944016855
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+      ]
+    },
+    {
+      "repeat": 0,
+      "input_tokens": 8192,
+      "output_tokens_per_request": 128,
+      "concurrency": 16,
+      "summary": {
+        "requests": 16,
+        "output_tokens": 2048,
+        "wall_s": 53.50810752104735,
+        "output_tok_s": 38.274573609138045,
+        "median_first_sse_s": 26.9483449094987,
+        "median_tpot_est_s": 0.034134499630139134,
+        "median_e2e_s": 31.776315025490476,
+        "all_text_equal": true
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+      "rows": [
+        {
+          "id": "r0-n8192-o128-c16-0",
+          "endpoint": 0,
+          "input_tokens": 8192,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nOkay, so the user is asking for the capital of France. Let me think. I know that Paris is the capital, but I should make sure there's no trick here. The user provided a lot of context about boxes with apples and books, but that seems unrelated. Maybe it's a test to see if I can ignore irrelevant information. Yeah, the answer should be Paris. I don't need to consider the boxes because the question is straightforward. Just need to recall that Paris is the capital of France. No need to overcomplicate it.\nThe capital of France is Paris. The information about",
+          "text_equal": true,
+          "first_sse_s": 40.84014320001006,
+          "tpot_est_s": 0.04298429033860168,
+          "e2e_s": 46.299148073012475
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+        {
+          "id": "r0-n8192-o128-c16-1",
+          "endpoint": 0,
+          "input_tokens": 8192,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nOkay, so the user is asking for the capital of France. Let me think. I know that Paris is the capital, but I should make sure there's no trick here. The user provided a lot of context about boxes with apples and books, but that seems unrelated. Maybe it's a test to see if I can ignore irrelevant information. Yeah, the answer should be Paris. I don't need to consider the boxes because the question is straightforward. Just need to recall that Paris is the capital of France. No need to overcomplicate it.\nThe capital of France is Paris. The information about",
+          "text_equal": true,
+          "first_sse_s": 6.871440722956322,
+          "tpot_est_s": 0.034406635952897106,
+          "e2e_s": 11.241083488974255
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+        {
+          "id": "r0-n8192-o128-c16-2",
+          "endpoint": 0,
+          "input_tokens": 8192,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nOkay, so the user is asking for the capital of France. Let me think. I know that Paris is the capital, but I should make sure there's no trick here. The user provided a lot of context about boxes with apples and books, but that seems unrelated. Maybe it's a test to see if I can ignore irrelevant information. Yeah, the answer should be Paris. I don't need to consider the boxes because the question is straightforward. Just need to recall that Paris is the capital of France. No need to overcomplicate it.\nThe capital of France is Paris. The information about",
+          "text_equal": true,
+          "first_sse_s": 16.149407670018263,
+          "tpot_est_s": 0.04308935131476165,
+          "e2e_s": 21.621755286992993
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+        {
+          "id": "r0-n8192-o128-c16-3",
+          "endpoint": 0,
+          "input_tokens": 8192,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nOkay, so the user is asking for the capital of France. Let me think. I know that Paris is the capital, but I should make sure there's no trick here. The user provided a lot of context about boxes with apples and books, but that seems unrelated. Maybe it's a test to see if I can ignore irrelevant information. Yeah, the answer should be Paris. I don't need to consider the boxes because the question is straightforward. Just need to recall that Paris is the capital of France. No need to overcomplicate it.\nThe capital of France is Paris. The information about",
+          "text_equal": true,
+          "first_sse_s": 43.9684940680163,
+          "tpot_est_s": 0.030253058850332568,
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+        {
+          "id": "r0-n8192-o128-c16-4",
+          "endpoint": 0,
+          "input_tokens": 8192,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nOkay, so the user is asking for the capital of France. Let me think. I know that Paris is the capital, but I should make sure there's no trick here. The user provided a lot of context about boxes with apples and books, but that seems unrelated. Maybe it's a test to see if I can ignore irrelevant information. Yeah, the answer should be Paris. I don't need to consider the boxes because the question is straightforward. Just need to recall that Paris is the capital of France. No need to overcomplicate it.\nThe capital of France is Paris. The information about",
+          "text_equal": true,
+          "first_sse_s": 50.18411590496544,
+          "tpot_est_s": 0.026167993039611405,
+          "e2e_s": 53.507451020996086
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+        {
+          "id": "r0-n8192-o128-c16-5",
+          "endpoint": 0,
+          "input_tokens": 8192,
+          "output_tokens": 128,
+          "text": " The capital of France is Paris.\nOkay, so the user is asking for the capital of France. Let me think. I know that Paris is the capital, but I should make sure there's no trick here. The user provided a lot of context about boxes with apples and books, but that seems unrelated. Maybe it's a test to see if I can ignore irrelevant information. Yeah, the answer should be Paris. I don't need to consider the boxes because the question is straightforward. Just need to recall that Paris is the capital of France. No need to overcomplicate it.\nThe capital of France is Paris. The information about",
+          "text_equal": true,
+          "first_sse_s": 47.127296396996826,
+          "tpot_est_s": 0.029976635559014683,
+          "e2e_s": 50.93432911299169
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+        {
+          "id": "r0-n8192-o128-c16-6",
+          "endpoint": 0,
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+          "text": " The capital of France is Paris.\nOkay, so the user is asking for the capital of France. Let me think. I know that Paris is the capital, but I should make sure there's no trick here. The user provided a lot of context about boxes with apples and books, but that seems unrelated. Maybe it's a test to see if I can ignore irrelevant information. Yeah, the answer should be Paris. I don't need to consider the boxes because the question is straightforward. Just need to recall that Paris is the capital of France. No need to overcomplicate it.\nThe capital of France is Paris. The information about",
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+          "text": " The capital of France is Paris.\nOkay, so the user is asking for the capital of France. Let me think. I know that Paris is the capital, but I should make sure there's no trick here. The user provided a lot of context about boxes with apples and books, but that seems unrelated. Maybe it's a test to see if I can ignore irrelevant information. Yeah, the answer should be Paris. I don't need to consider the boxes because the question is straightforward. Just need to recall that Paris is the capital of France. No need to overcomplicate it.\nThe capital of France is Paris. The information about",
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+          "text": " The capital of France is Paris.\nOkay, so the user is asking for the capital of France. Let me think. I know that Paris is the capital, but I should make sure there's no trick here. The user provided a lot of context about boxes with apples and books, but that seems unrelated. Maybe it's a test to see if I can ignore irrelevant information. Yeah, the answer should be Paris. I don't need to consider the boxes because the question is straightforward. Just need to recall that Paris is the capital of France. No need to overcomplicate it.\nThe capital of France is Paris. The information about",
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+          "text": " The capital of France is Paris.\nOkay, so the user is asking for the capital of France. Let me think. I know that Paris is the capital, but I should make sure there's no trick here. The user provided a lot of context about boxes with apples and books, but that seems unrelated. Maybe it's a test to see if I can ignore irrelevant information. Yeah, the answer should be Paris. I don't need to consider the boxes because the question is straightforward. Just need to recall that Paris is the capital of France. No need to overcomplicate it.\nThe capital of France is Paris. The information about",
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+          "text": " The capital of France is Paris.\nOkay, so the user is asking for the capital of France. Let me think. I know that Paris is the capital, but I should make sure there's no trick here. The user provided a lot of context about boxes with apples and books, but that seems unrelated. Maybe it's a test to see if I can ignore irrelevant information. Yeah, the answer should be Paris. I don't need to consider the boxes because the question is straightforward. Just need to recall that Paris is the capital of France. No need to overcomplicate it.\nThe capital of France is Paris. The information about",
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+          "text": " The capital of France is Paris.\nOkay, so the user is asking for the capital of France. Let me think. I know that Paris is the capital, but I should make sure there's no trick here. The user provided a lot of context about boxes with apples and books, but that seems unrelated. Maybe it's a test to see if I can ignore irrelevant information. Yeah, the answer should be Paris. I don't need to consider the boxes because the question is straightforward. Just need to recall that Paris is the capital of France. No need to overcomplicate it.\nThe capital of France is Paris. The information about",
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+          "text": " The capital of France is Paris.\nOkay, so the user is asking for the capital of France. Let me think. I know that Paris is the capital, but I should make sure there's no trick here. The user provided a lot of context about boxes with apples and books, but that seems unrelated. Maybe it's a test to see if I can ignore irrelevant information. Yeah, the answer should be Paris. I don't need to consider the boxes because the question is straightforward. Just need to recall that Paris is the capital of France. No need to overcomplicate it.\nThe capital of France is Paris. The information about",
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+          "text": " The capital of France is Paris.\nOkay, so the user is asking for the capital of France. Let me think. I know that Paris is the capital, but I should make sure there's no trick here. The user provided a lot of context about boxes with apples and books, but that seems unrelated. Maybe it's a test to see if I can ignore irrelevant information. Yeah, the answer should be Paris. I don't need to consider the boxes because the question is straightforward. Just need to recall that Paris is the capital of France. No need to overcomplicate it.\nThe capital of France is Paris. The information about",
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+          "id": "r0-n8192-o128-c16-15",
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+          "text": " The capital of France is Paris.\nOkay, so the user is asking for the capital of France. Let me think. I know that Paris is the capital, but I should make sure there's no trick here. The user provided a lot of context about boxes with apples and books, but that seems unrelated. Maybe it's a test to see if I can ignore irrelevant information. Yeah, the answer should be Paris. I don't need to consider the boxes because the question is straightforward. Just need to recall that Paris is the capital of France. No need to overcomplicate it.\nThe capital of France is Paris. The information about",
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+        "/home/gspark/venvs/pt213/bin/python",
+        "-c",
+        "import sys,importlib.util,runpy\ns=importlib.util.spec_from_file_location('opt_prime.accel.serve.llmd','/tmp/llmd240.py')\nm=importlib.util.module_from_spec(s);sys.modules[s.name]=m;s.loader.exec_module(m)\nrunpy.run_path('/home/gspark/workspace/aicomp-rngd-stream-265/opt_prime/examples/pd/serve_batched.py',run_name='__main__')\n",
+        "--model",
+        "/tmp/serving-matrix-models/Qwen3-4B",
+        "--device",
+        "cuda:0",
+        "--dtype",
+        "bfloat16",
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+        "256",
+        "--port",
+        "{port}",
+        "--pd-prefill-python",
+        "/tmp/stream265-python.sh",
+        "--pd-prefill-args",
+        "--engine factory --engine-factory streaming_factories:qwen --width 8192 --chip 0"
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+    "metadata": {
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+      "physical_rngd": 1,
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+  "references": {
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+      "text": " The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of"
+    },
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+      "text": " The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nQuestion: What is the capital of France?\nAnswer: The capital of France is Paris.\nOkay, so the user is asking about the capital of France,"
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+      "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user also provided a"
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+    },
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+      "text": " The capital of France is Paris.\nOkay, so the user asked for the capital of France, and I answered Paris. But wait, the user provided a bunch"
+    },
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+      "text": " The capital of France is Paris.\nOkay, so the user is asking for the capital of France. Let me think. I know that Paris is the capital,"
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+      "text": " The capital of France is Paris.\nOkay, so the user is asking for the capital of France. Let me think. I know that Paris is the capital, but I should make sure there's no trick here. The user provided a lot of context about boxes with apples and books, but that seems unrelated. Maybe it's a test to see if I can ignore irrelevant information. Yeah, the answer should be Paris. I don't need to consider the boxes because the question is straightforward. Just need to recall that Paris is the capital of France. No need to overcomplicate it.\nThe capital of France is Paris. The information about"
+    }
+  },
+  "warmup_and_checks_s": 427.7694704660098,
+  "metrics": [
+    "# HELP vllm:num_requests_running Requests executing on the stage.\n# TYPE vllm:num_requests_running gauge\nvllm:num_requests_running{model_name=\"Qwen3-4B\"} 0\n# HELP vllm:num_requests_waiting Requests queued for the stage owner.\n# TYPE vllm:num_requests_waiting gauge\nvllm:num_requests_waiting{model_name=\"Qwen3-4B\"} 0\n# HELP vllm:kv_cache_usage_perc Occupied fraction of the stage's KV lanes (1 = full).\n# TYPE vllm:kv_cache_usage_perc gauge\nvllm:kv_cache_usage_perc{model_name=\"Qwen3-4B\"} 0.0\n# HELP vllm:gpu_cache_usage_perc Occupied fraction of the stage's KV lanes (1 = full).\n# TYPE vllm:gpu_cache_usage_perc gauge\nvllm:gpu_cache_usage_perc{model_name=\"Qwen3-4B\"} 0.0\n# HELP vllm:request_success_total Finished requests by finish reason.\n# TYPE vllm:request_success_total counter\nvllm:request_success_total{model_name=\"Qwen3-4B\",finished_reason=\"length\"} 264\n# HELP airm_stage_request_failures_total Requests that ended with an engine error.\n# TYPE airm_stage_request_failures_total counter\nairm_stage_request_failures_total{model_name=\"Qwen3-4B\"} 0\n# HELP vllm:prompt_tokens_total Prompt tokens of finished requests.\n# TYPE vllm:prompt_tokens_total counter\nvllm:prompt_tokens_total{model_name=\"Qwen3-4B\"} 718080\n# HELP vllm:generation_tokens_total Generated tokens of finished requests.\n# TYPE vllm:generation_tokens_total counter\nvllm:generation_tokens_total{model_name=\"Qwen3-4B\"} 21120\n# HELP vllm:time_to_first_token_seconds Time to first token (prefill stage: fenced prefill).\n# TYPE vllm:time_to_first_token_seconds histogram\nvllm:time_to_first_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.001\"} 0\nvllm:time_to_first_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.005\"} 0\nvllm:time_to_first_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.01\"} 0\nvllm:time_to_first_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.02\"} 0\nvllm:time_to_first_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.04\"} 0\nvllm:time_to_first_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.06\"} 0\nvllm:time_to_first_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.08\"} 0\nvllm:time_to_first_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.1\"} 0\nvllm:time_to_first_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.25\"} 0\nvllm:time_to_first_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.5\"} 0\nvllm:time_to_first_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.75\"} 0\nvllm:time_to_first_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"1.0\"} 0\nvllm:time_to_first_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"2.5\"} 0\nvllm:time_to_first_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"5.0\"} 20\nvllm:time_to_first_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"7.5\"} 40\nvllm:time_to_first_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"10.0\"} 53\nvllm:time_to_first_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"+Inf\"} 264\nvllm:time_to_first_token_seconds_sum{model_name=\"Qwen3-4B\"} 6283.05648460181\nvllm:time_to_first_token_seconds_count{model_name=\"Qwen3-4B\"} 264\n# HELP vllm:time_per_output_token_seconds Time per output token after the first (decode stage).\n# TYPE vllm:time_per_output_token_seconds histogram\nvllm:time_per_output_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.01\"} 0\nvllm:time_per_output_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.025\"} 0\nvllm:time_per_output_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.05\"} 263\nvllm:time_per_output_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.075\"} 263\nvllm:time_per_output_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.1\"} 264\nvllm:time_per_output_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.15\"} 264\nvllm:time_per_output_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.2\"} 264\nvllm:time_per_output_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.3\"} 264\nvllm:time_per_output_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.4\"} 264\nvllm:time_per_output_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.5\"} 264\nvllm:time_per_output_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.75\"} 264\nvllm:time_per_output_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"1.0\"} 264\nvllm:time_per_output_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"2.5\"} 264\nvllm:time_per_output_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"+Inf\"} 264\nvllm:time_per_output_token_seconds_sum{model_name=\"Qwen3-4B\"} 7.7953805047443865\nvllm:time_per_output_token_seconds_count{model_name=\"Qwen3-4B\"} 264\n# HELP vllm:e2e_request_latency_seconds Stage request latency, from execution start to reply.\n# TYPE vllm:e2e_request_latency_seconds histogram\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.3\"} 0\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.5\"} 0\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.8\"} 0\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Qwen3-4B\",le=\"1.0\"} 0\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Qwen3-4B\",le=\"1.5\"} 0\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Qwen3-4B\",le=\"2.0\"} 0\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Qwen3-4B\",le=\"2.5\"} 0\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Qwen3-4B\",le=\"5.0\"} 9\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Qwen3-4B\",le=\"10.0\"} 42\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Qwen3-4B\",le=\"15.0\"} 69\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Qwen3-4B\",le=\"20.0\"} 92\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Qwen3-4B\",le=\"30.0\"} 156\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Qwen3-4B\",le=\"40.0\"} 210\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Qwen3-4B\",le=\"50.0\"} 260\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Qwen3-4B\",le=\"60.0\"} 264\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Qwen3-4B\",le=\"+Inf\"} 264\nvllm:e2e_request_latency_seconds_sum{model_name=\"Qwen3-4B\"} 6903.700648609898\nvllm:e2e_request_latency_seconds_count{model_name=\"Qwen3-4B\"} 264\n# HELP airm_kv_transfer_seconds KV transfer time from the prefill source to the decode destination.\n# TYPE airm_kv_transfer_seconds histogram\nairm_kv_transfer_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.0005\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.001\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.0025\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.005\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.01\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.025\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.05\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.1\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.25\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.5\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"Qwen3-4B\",le=\"1.0\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"Qwen3-4B\",le=\"+Inf\"} 0\nairm_kv_transfer_seconds_sum{model_name=\"Qwen3-4B\"} 0.0\nairm_kv_transfer_seconds_count{model_name=\"Qwen3-4B\"} 0\n# HELP vllm:cache_config_info Information of the LLMEngine CacheConfig.\n# TYPE vllm:cache_config_info gauge\nvllm:cache_config_info{model_name=\"Qwen3-4B\",block_size=\"64\",num_gpu_blocks=\"576\",enable_prefix_caching=\"False\"} 1\n# HELP vllm:prefix_cache_queries_total Prompt tokens looked up in the prefix cache.\n# TYPE vllm:prefix_cache_queries_total counter\nvllm:prefix_cache_queries_total{model_name=\"Qwen3-4B\"} 718080\n# HELP vllm:prefix_cache_hits_total Prompt tokens served from the prefix cache.\n# TYPE vllm:prefix_cache_hits_total counter\nvllm:prefix_cache_hits_total{model_name=\"Qwen3-4B\"} 0\n# HELP airm_pd_prefills_total Prompts prefilled by the PD prefill worker (remote) or here (local).\n# TYPE airm_pd_prefills_total counter\nairm_pd_prefills_total{model_name=\"Qwen3-4B\",side=\"remote\"} 264\nairm_pd_prefills_total{model_name=\"Qwen3-4B\",side=\"local\"} 0\n# HELP airm_pd_transferred_tokens_total Prompt KV tokens received from the prefill side.\n# TYPE airm_pd_transferred_tokens_total counter\nairm_pd_transferred_tokens_total{model_name=\"Qwen3-4B\"} 718080\n# HELP airm_pd_producer_seconds_total Accumulated worker-reported seconds for admitted PD handoffs; excludes failed/cancelled handoffs. Prefill includes KV gathering; transfer excludes destination KV installation.\n# TYPE airm_pd_producer_seconds_total counter\nairm_pd_producer_seconds_total{model_name=\"Qwen3-4B\",stage=\"prefill\"} 758.5554205485387\nairm_pd_producer_seconds_total{model_name=\"Qwen3-4B\",stage=\"transfer\"} 0.0\n"
+  ],
+  "parity_passed": true
+}
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen4b-init-diagnostic.json b/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen4b-init-diagnostic.json
new file mode 100644
index 00000000..ed29c55b
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen4b-init-diagnostic.json
@@ -0,0 +1,15 @@
+{
+  "observed_at": "2026-10-23T22:53:35.562541+00:00",
+  "role": "RNGD+ATOM host",
+  "pod": "stream265long",
+  "source_rss_kib": 38245120,
+  "source_swap_kib": 0,
+  "source_threads": 29,
+  "pod_memory_bytes": 50786693120,
+  "oom": 0,
+  "oom_kill": 0,
+  "memory_pressure_avg10": 0.0,
+  "cgroup_usage_usec": 6627115061,
+  "cgroup_throttled_usec": 16360853,
+  "scope": "read-only owned-Pod snapshot during source startup; CPU activity does not prove successful initialization"
+}
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen4b-long-atom.json b/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen4b-long-atom.json
new file mode 100644
index 00000000..cbafe11d
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen4b-long-atom.json
@@ -0,0 +1,5492 @@
+{
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+      "imports": [
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diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen4b-long-atom.log b/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen4b-long-atom.log
new file mode 100644
index 00000000..f08923d0
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen4b-long-atom.log
@@ -0,0 +1,17 @@
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diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen4b-long-gpu.json b/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen4b-long-gpu.json
new file mode 100644
index 00000000..08c05c15
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen4b-long-gpu.json
@@ -0,0 +1,3568 @@
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diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen4b-long-gpu.log b/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen4b-long-gpu.log
new file mode 100644
index 00000000..cde0b2f8
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen4b-long-gpu.log
@@ -0,0 +1,12 @@
+GPU loading qwen4b
+
+Loading weights:   0%|          | 0/398 [00:00<?, ?it/s]
+Loading weights: 100%|██████████| 398/398 [00:00<00:00, 9465.06it/s]
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diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen4b-long-source.json b/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen4b-long-source.json
new file mode 100644
index 00000000..19f74cd4
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen4b-long-source.json
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diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen4b-long-source.log b/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen4b-long-source.log
new file mode 100644
index 00000000..ae7cf5bb
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen4b-long-source.log
@@ -0,0 +1,8 @@
+initializing qwen4b
+initialized qwen4b
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diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen4b-source.json b/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen4b-source.json
new file mode 100644
index 00000000..2a43ac45
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen4b-source.json
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+          "time_s": 0.03452529100468382
+        }
+      ],
+      "elapsed_s": 0.06284647702705115
+    },
+    {
+      "input_len": 128,
+      "send_from": 128,
+      "keys_exact": true,
+      "values_exact": true,
+      "logits_exact": true,
+      "max_logit_error": 0.0,
+      "token_whole": 374,
+      "token_stream": 374,
+      "notifications": [],
+      "elapsed_s": 0.05486393999308348
+    }
+  ],
+  "scope": "same RNGD source whole vs streamed KV/logits; not decoder parity or speedup",
+  "source_sha256": {
+    "prefill_server.py": "47b1aa1db946eaa40b12cc71815c6c31d43ccf9f3d2ce4df79897e9b4387b930",
+    "streaming_factories.py": "77745350973cc54bf508e0c8b473efc442afef20b1c64f7c4edc179ea1471389"
+  },
+  "stage": "complete"
+}
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen4b-source.log b/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen4b-source.log
new file mode 100644
index 00000000..d2cc68bc
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-qwen4b-source.log
@@ -0,0 +1,8 @@
+initializing qwen4b
+initialized qwen4b
+{"input_len": 14, "send_from": 0, "keys_exact": true, "values_exact": true, "logits_exact": true, "max_logit_error": 0.0, "token_whole": 3555, "token_stream": 3555, "elapsed_s": 0.05662998801562935}
+{"input_len": 14, "send_from": 7, "keys_exact": true, "values_exact": true, "logits_exact": true, "max_logit_error": 0.0, "token_whole": 3555, "token_stream": 3555, "elapsed_s": 0.054423809982836246}
+{"input_len": 14, "send_from": 14, "keys_exact": true, "values_exact": true, "logits_exact": true, "max_logit_error": 0.0, "token_whole": 3555, "token_stream": 3555, "elapsed_s": 0.05222300800960511}
+{"input_len": 128, "send_from": 0, "keys_exact": true, "values_exact": true, "logits_exact": true, "max_logit_error": 0.0, "token_whole": 374, "token_stream": 374, "elapsed_s": 0.05851396301295608}
+{"input_len": 128, "send_from": 64, "keys_exact": true, "values_exact": true, "logits_exact": true, "max_logit_error": 0.0, "token_whole": 374, "token_stream": 374, "elapsed_s": 0.06284647702705115}
+{"input_len": 128, "send_from": 128, "keys_exact": true, "values_exact": true, "logits_exact": true, "max_logit_error": 0.0, "token_whole": 374, "token_stream": 374, "elapsed_s": 0.05486393999308348}
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-red.log b/opt_prime/examples/pd/http_matrix/evidence265/stream265-red.log
new file mode 100644
index 00000000..e7d6c8b1
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-red.log
@@ -0,0 +1,245 @@
+FFFFFFFFFFF                                                              [100%]
+=================================== FAILURES ===================================
+____ test_partial_prefix_and_out_of_order_layers_require_complete_coverage _____
+
+    def test_partial_prefix_and_out_of_order_layers_require_complete_coverage():
+>       p=tracker(3,5,9)
+
+opt_prime/tests/test_pd_layer_progress.py:16:
+_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
+
+args = (3, 5, 9)
+
+    def tracker(*args):
+>       assert path.exists(), 'layer-progress validator is not implemented'
+E       AssertionError: layer-progress validator is not implemented
+E       assert False
+E        +  where False = exists()
+E        +    where exists = PosixPath('/home/gspark/workspace/aicomp-rngd-stream-265/opt_prime/opt_prime/accel/pd/layer_progress.py').exists
+
+opt_prime/tests/test_pd_layer_progress.py:9: AssertionError
+________________ test_overlap_rejection_is_atomic_across_layers ________________
+
+    def test_overlap_rejection_is_atomic_across_layers():
+>       p=tracker(3,0,4)
+
+opt_prime/tests/test_pd_layer_progress.py:29:
+_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
+
+args = (3, 0, 4)
+
+    def tracker(*args):
+>       assert path.exists(), 'layer-progress validator is not implemented'
+E       AssertionError: layer-progress validator is not implemented
+E       assert False
+E        +  where False = exists()
+E        +    where exists = PosixPath('/home/gspark/workspace/aicomp-rngd-stream-265/opt_prime/opt_prime/accel/pd/layer_progress.py').exists
+
+opt_prime/tests/test_pd_layer_progress.py:9: AssertionError
+_______________ test_invalid_progress_never_enters_queue[span0] ________________
+
+span = (-1, 1, 0, 4)
+
+    @pytest.mark.parametrize('span',[(-1,1,0,4),(0,4,0,4),(0,1,-1,4),
+        (0,1,0,5),(0,0,0,4),(0,1,2,2),(True,2,0,4),(0,1,0.0,4)])
+    def test_invalid_progress_never_enters_queue(span):
+>       p=tracker(3,0,4)
+
+opt_prime/tests/test_pd_layer_progress.py:43:
+_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
+
+args = (3, 0, 4)
+
+    def tracker(*args):
+>       assert path.exists(), 'layer-progress validator is not implemented'
+E       AssertionError: layer-progress validator is not implemented
+E       assert False
+E        +  where False = exists()
+E        +    where exists = PosixPath('/home/gspark/workspace/aicomp-rngd-stream-265/opt_prime/opt_prime/accel/pd/layer_progress.py').exists
+
+opt_prime/tests/test_pd_layer_progress.py:9: AssertionError
+_______________ test_invalid_progress_never_enters_queue[span1] ________________
+
+span = (0, 4, 0, 4)
+
+    @pytest.mark.parametrize('span',[(-1,1,0,4),(0,4,0,4),(0,1,-1,4),
+        (0,1,0,5),(0,0,0,4),(0,1,2,2),(True,2,0,4),(0,1,0.0,4)])
+    def test_invalid_progress_never_enters_queue(span):
+>       p=tracker(3,0,4)
+
+opt_prime/tests/test_pd_layer_progress.py:43:
+_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
+
+args = (3, 0, 4)
+
+    def tracker(*args):
+>       assert path.exists(), 'layer-progress validator is not implemented'
+E       AssertionError: layer-progress validator is not implemented
+E       assert False
+E        +  where False = exists()
+E        +    where exists = PosixPath('/home/gspark/workspace/aicomp-rngd-stream-265/opt_prime/opt_prime/accel/pd/layer_progress.py').exists
+
+opt_prime/tests/test_pd_layer_progress.py:9: AssertionError
+_______________ test_invalid_progress_never_enters_queue[span2] ________________
+
+span = (0, 1, -1, 4)
+
+    @pytest.mark.parametrize('span',[(-1,1,0,4),(0,4,0,4),(0,1,-1,4),
+        (0,1,0,5),(0,0,0,4),(0,1,2,2),(True,2,0,4),(0,1,0.0,4)])
+    def test_invalid_progress_never_enters_queue(span):
+>       p=tracker(3,0,4)
+
+opt_prime/tests/test_pd_layer_progress.py:43:
+_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
+
+args = (3, 0, 4)
+
+    def tracker(*args):
+>       assert path.exists(), 'layer-progress validator is not implemented'
+E       AssertionError: layer-progress validator is not implemented
+E       assert False
+E        +  where False = exists()
+E        +    where exists = PosixPath('/home/gspark/workspace/aicomp-rngd-stream-265/opt_prime/opt_prime/accel/pd/layer_progress.py').exists
+
+opt_prime/tests/test_pd_layer_progress.py:9: AssertionError
+_______________ test_invalid_progress_never_enters_queue[span3] ________________
+
+span = (0, 1, 0, 5)
+
+    @pytest.mark.parametrize('span',[(-1,1,0,4),(0,4,0,4),(0,1,-1,4),
+        (0,1,0,5),(0,0,0,4),(0,1,2,2),(True,2,0,4),(0,1,0.0,4)])
+    def test_invalid_progress_never_enters_queue(span):
+>       p=tracker(3,0,4)
+
+opt_prime/tests/test_pd_layer_progress.py:43:
+_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
+
+args = (3, 0, 4)
+
+    def tracker(*args):
+>       assert path.exists(), 'layer-progress validator is not implemented'
+E       AssertionError: layer-progress validator is not implemented
+E       assert False
+E        +  where False = exists()
+E        +    where exists = PosixPath('/home/gspark/workspace/aicomp-rngd-stream-265/opt_prime/opt_prime/accel/pd/layer_progress.py').exists
+
+opt_prime/tests/test_pd_layer_progress.py:9: AssertionError
+_______________ test_invalid_progress_never_enters_queue[span4] ________________
+
+span = (0, 0, 0, 4)
+
+    @pytest.mark.parametrize('span',[(-1,1,0,4),(0,4,0,4),(0,1,-1,4),
+        (0,1,0,5),(0,0,0,4),(0,1,2,2),(True,2,0,4),(0,1,0.0,4)])
+    def test_invalid_progress_never_enters_queue(span):
+>       p=tracker(3,0,4)
+
+opt_prime/tests/test_pd_layer_progress.py:43:
+_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
+
+args = (3, 0, 4)
+
+    def tracker(*args):
+>       assert path.exists(), 'layer-progress validator is not implemented'
+E       AssertionError: layer-progress validator is not implemented
+E       assert False
+E        +  where False = exists()
+E        +    where exists = PosixPath('/home/gspark/workspace/aicomp-rngd-stream-265/opt_prime/opt_prime/accel/pd/layer_progress.py').exists
+
+opt_prime/tests/test_pd_layer_progress.py:9: AssertionError
+_______________ test_invalid_progress_never_enters_queue[span5] ________________
+
+span = (0, 1, 2, 2)
+
+    @pytest.mark.parametrize('span',[(-1,1,0,4),(0,4,0,4),(0,1,-1,4),
+        (0,1,0,5),(0,0,0,4),(0,1,2,2),(True,2,0,4),(0,1,0.0,4)])
+    def test_invalid_progress_never_enters_queue(span):
+>       p=tracker(3,0,4)
+
+opt_prime/tests/test_pd_layer_progress.py:43:
+_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
+
+args = (3, 0, 4)
+
+    def tracker(*args):
+>       assert path.exists(), 'layer-progress validator is not implemented'
+E       AssertionError: layer-progress validator is not implemented
+E       assert False
+E        +  where False = exists()
+E        +    where exists = PosixPath('/home/gspark/workspace/aicomp-rngd-stream-265/opt_prime/opt_prime/accel/pd/layer_progress.py').exists
+
+opt_prime/tests/test_pd_layer_progress.py:9: AssertionError
+_______________ test_invalid_progress_never_enters_queue[span6] ________________
+
+span = (True, 2, 0, 4)
+
+    @pytest.mark.parametrize('span',[(-1,1,0,4),(0,4,0,4),(0,1,-1,4),
+        (0,1,0,5),(0,0,0,4),(0,1,2,2),(True,2,0,4),(0,1,0.0,4)])
+    def test_invalid_progress_never_enters_queue(span):
+>       p=tracker(3,0,4)
+
+opt_prime/tests/test_pd_layer_progress.py:43:
+_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
+
+args = (3, 0, 4)
+
+    def tracker(*args):
+>       assert path.exists(), 'layer-progress validator is not implemented'
+E       AssertionError: layer-progress validator is not implemented
+E       assert False
+E        +  where False = exists()
+E        +    where exists = PosixPath('/home/gspark/workspace/aicomp-rngd-stream-265/opt_prime/opt_prime/accel/pd/layer_progress.py').exists
+
+opt_prime/tests/test_pd_layer_progress.py:9: AssertionError
+_______________ test_invalid_progress_never_enters_queue[span7] ________________
+
+span = (0, 1, 0.0, 4)
+
+    @pytest.mark.parametrize('span',[(-1,1,0,4),(0,4,0,4),(0,1,-1,4),
+        (0,1,0,5),(0,0,0,4),(0,1,2,2),(True,2,0,4),(0,1,0.0,4)])
+    def test_invalid_progress_never_enters_queue(span):
+>       p=tracker(3,0,4)
+
+opt_prime/tests/test_pd_layer_progress.py:43:
+_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
+
+args = (3, 0, 4)
+
+    def tracker(*args):
+>       assert path.exists(), 'layer-progress validator is not implemented'
+E       AssertionError: layer-progress validator is not implemented
+E       assert False
+E        +  where False = exists()
+E        +    where exists = PosixPath('/home/gspark/workspace/aicomp-rngd-stream-265/opt_prime/opt_prime/accel/pd/layer_progress.py').exists
+
+opt_prime/tests/test_pd_layer_progress.py:9: AssertionError
+_________________ test_empty_uncached_suffix_needs_no_transfer _________________
+
+    def test_empty_uncached_suffix_needs_no_transfer():
+>       p=tracker(3,8,8)
+
+opt_prime/tests/test_pd_layer_progress.py:49:
+_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
+
+args = (3, 8, 8)
+
+    def tracker(*args):
+>       assert path.exists(), 'layer-progress validator is not implemented'
+E       AssertionError: layer-progress validator is not implemented
+E       assert False
+E        +  where False = exists()
+E        +    where exists = PosixPath('/home/gspark/workspace/aicomp-rngd-stream-265/opt_prime/opt_prime/accel/pd/layer_progress.py').exists
+
+opt_prime/tests/test_pd_layer_progress.py:9: AssertionError
+=========================== short test summary info ============================
+FAILED opt_prime/tests/test_pd_layer_progress.py::test_partial_prefix_and_out_of_order_layers_require_complete_coverage
+FAILED opt_prime/tests/test_pd_layer_progress.py::test_overlap_rejection_is_atomic_across_layers
+FAILED opt_prime/tests/test_pd_layer_progress.py::test_invalid_progress_never_enters_queue[span0]
+FAILED opt_prime/tests/test_pd_layer_progress.py::test_invalid_progress_never_enters_queue[span1]
+FAILED opt_prime/tests/test_pd_layer_progress.py::test_invalid_progress_never_enters_queue[span2]
+FAILED opt_prime/tests/test_pd_layer_progress.py::test_invalid_progress_never_enters_queue[span3]
+FAILED opt_prime/tests/test_pd_layer_progress.py::test_invalid_progress_never_enters_queue[span4]
+FAILED opt_prime/tests/test_pd_layer_progress.py::test_invalid_progress_never_enters_queue[span5]
+FAILED opt_prime/tests/test_pd_layer_progress.py::test_invalid_progress_never_enters_queue[span6]
+FAILED opt_prime/tests/test_pd_layer_progress.py::test_invalid_progress_never_enters_queue[span7]
+FAILED opt_prime/tests/test_pd_layer_progress.py::test_empty_uncached_suffix_needs_no_transfer
+11 failed in 0.05s
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-source-dependencies.json b/opt_prime/examples/pd/http_matrix/evidence265/stream265-source-dependencies.json
new file mode 100644
index 00000000..00b71ccb
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-source-dependencies.json
@@ -0,0 +1,116 @@
+{
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+  "/home/gspark/workspace/aicomp-live-pd-234/opt_prime/examples/pd/serving_matrix/test_semantic_export.py": "970dfcd4d7d7998dd1a1b5937bad1e4746c5c5e1bb153e277b0927c90b4853c6",
+  "/home/gspark/workspace/aicomp-live-pd-234/opt_prime/examples/pd/serving_matrix/long_width_variant.py": "74849665643d87b6d44f86c64849c4f532c7dedff9880404950a2692ec443c84",
+  "/home/gspark/workspace/aicomp-live-pd-234/opt_prime/examples/pd/serving_matrix/check_batch_numerics.py": "7a2cc3ef5b733216d79f85b099a87a5260f6871ccecf6a8664796e9da189a0ed",
+  "/home/gspark/workspace/aicomp-live-pd-234/opt_prime/examples/pd/serving_matrix/inputs.py": "0c2879f78b5b71085a4a0b7ae2770690dde87e1dddaaa50b7d57606ff9250ec6",
+  "/home/gspark/workspace/aicomp-live-pd-234/opt_prime/examples/pd/serving_matrix/summarize_gpu.py": "3a5bb41e7149665f5e4b34563c0ce3d1f050849b8727cc10f38bcb2341a5067a",
+  "/home/gspark/workspace/aicomp-live-pd-234/opt_prime/examples/pd/serving_matrix/rowwise_rms.py": "0b67b1ccf03cd14f3cbc3357d9fc5a078fb6eb1d6a4185a09870fac697189fde",
+  "/home/gspark/workspace/aicomp-live-pd-234/opt_prime/examples/pd/serving_matrix/koni_partitioned.py": "4853d5db3f2b1292feca41aa83bf3b415a258129a6e127840617d5ce70dfe6d3",
+  "/home/gspark/workspace/aicomp-live-pd-234/opt_prime/examples/pd/serving_matrix/test_inputs.py": "2003af8255c95b16956f6a1b4850de62340ae8ec0c7e256e99ce0d7e4de28ebe",
+  "/home/gspark/workspace/aicomp-live-pd-234/opt_prime/examples/pd/serving_matrix/rngd_gemma_sweep.py": "df2943e7df567c56b56f7c1e38a75037f61c564733888f4c9b2dc7e89546b545",
+  "/home/gspark/workspace/aicomp-live-pd-234/opt_prime/examples/pd/serving_matrix/probe_rms219.py": "90d680abe7bba06d8286e00b07cb79114e792a961899e48ed220754eddf9da4e",
+  "/home/gspark/workspace/aicomp-live-pd-234/opt_prime/examples/pd/qwen3_rngd/spec.py": "9813e086b8408da7f09142388b0be5855c95047ebdca67aaa2acf5745964bcf1",
+  "/home/gspark/workspace/aicomp-live-pd-234/opt_prime/examples/pd/qwen3_rngd/bench_export.py": "09b2a0f8e238e499e24ee8172d7ff9e723446aab750ca0c0b65140cf829cf3cb",
+  "/home/gspark/workspace/aicomp-live-pd-234/opt_prime/examples/pd/qwen3_rngd/test_worker.py": "be8f6f66e94e500091f833126c5584498fbf0aad484079240777e1986b8529b4",
+  "/home/gspark/workspace/aicomp-live-pd-234/opt_prime/examples/pd/qwen3_rngd/prefill.py": "53dfd7123d0e7593cfb9073bcd125c92af267204fc480b06ef615cf58f4b5cbd",
+  "/home/gspark/workspace/aicomp-live-pd-234/opt_prime/examples/pd/qwen3_rngd/worker.py": "d2339d5de902a34bdbd2f021546a5dc0425d9ca440833d5e44c74af80a869935",
+  "/home/gspark/workspace/aicomp-live-pd-234/opt_prime/examples/pd/qwen3_rngd/bench_http.py": "dea525b7c3671b89bff6115ddb5ba748c9238a180f8d19344f0a5f25793f1b72",
+  "/home/gspark/workspace/aicomp-live-pd-234/opt_prime/examples/pd/qwen3_rngd/run_prefill.py": "74b4c40975712bb1641cf16671eb0cede13c1bc62a961f216e08f2b153c3e03d",
+  "/home/gspark/workspace/aicomp-live-pd-234/opt_prime/examples/pd/qwen3_rngd/test_spec.py": "6c01067ae3c799cdf3d4cbd539a72e66e1d44bcdcc26c786c8905d98c223aec4",
+  "/home/gspark/workspace/aicomp-live-pd-234/opt_prime/examples/pd/qwen3_rngd/bench_stream.py": "06d04e9deb3a506791d58ac25102378c43f075c1848c76f8113fb76988c94da9",
+  "/home/gspark/workspace/aicomp-live-pd-234/opt_prime/examples/pd/qwen3_rngd/check_atom.py": "ab014b95df6c96c3e5d2c2d35949b345c25a63683b1d2b37b8578099b2d4e326",
+  "/home/gspark/workspace/aicomp-live-pd-234/opt_prime/examples/pd/qwen3_rngd/serve.py": "4af184e0a77fa33e4aa967cf7eef3ed9e923ede0f6fd0ad24ef233b0f3f92197"
+}
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-source-final.log b/opt_prime/examples/pd/http_matrix/evidence265/stream265-source-final.log
new file mode 100644
index 00000000..33d359b6
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-source-final.log
@@ -0,0 +1,2 @@
+.............                                                            [100%]
+13 passed in 2.93s
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-source-focused.log b/opt_prime/examples/pd/http_matrix/evidence265/stream265-source-focused.log
new file mode 100644
index 00000000..be0bb3d6
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-source-focused.log
@@ -0,0 +1,2 @@
+...............                                                          [100%]
+15 passed in 2.24s
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-source-full.log b/opt_prime/examples/pd/http_matrix/evidence265/stream265-source-full.log
new file mode 100644
index 00000000..9cc1c9a4
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-source-full.log
@@ -0,0 +1,51 @@
+............................................................................................s..... [ 16%]
+.........................................................s.s............ [ 28%]
+.....ss...........................s..................................... [ 40%]
+........................................................................ [ 52%]
+.....s.s...........................................s.................... [ 64%]
+........................................................................ [ 75%]
+........................s.s.............................. [ 85%]
+........................ [ 89%]
+...........................................................s....         [100%]
+=============================== warnings summary ===============================
+examples/pd/native_kv/test_native_pack.py::NativePackTest::test_exact_bytes_and_independent_lifetime
+examples/pd/native_kv/test_native_pack.py::NativePackTest::test_exact_bytes_and_independent_lifetime
+examples/pd/native_kv/test_native_pack.py::NativePackTest::test_exact_bytes_and_independent_lifetime
+  /home/gspark/workspace/aicomp-rngd-stream-265/opt_prime/examples/pd/native_kv/test_native_pack.py:24: DeprecationWarning: The argument 'device' of Tensor.is_pinned() is deprecated. Please do not pass this argument. (Triggered internally at /__w/pytorch/pytorch/aten/src/ATen/native/Memory.cpp:31.)
+    self.assertTrue(actual.is_pinned('cuda'))
+
+examples/pd/native_kv/test_packed_lane.py::PackedLaneTest::test_owned_packet_and_other_lane_isolation
+examples/pd/native_kv/test_packed_lane.py::PackedLaneTest::test_owned_packet_and_other_lane_isolation
+examples/pd/native_kv/test_packed_lane.py::PackedLaneTest::test_owned_packet_and_other_lane_isolation
+  /home/gspark/workspace/aicomp-rngd-stream-265/opt_prime/examples/pd/native_kv/gpu_batch_decoder.py:93: DeprecationWarning: The argument 'device' of Tensor.is_pinned() is deprecated. Please do not pass this argument. (Triggered internally at /__w/pytorch/pytorch/aten/src/ATen/native/Memory.cpp:31.)
+    or not host.is_pinned('cuda') or not host.is_contiguous()):
+
+examples/pd/native_kv/test_pinned_receiver.py::PinnedReceiveTest::test_eight_slot_tcp_frames_exact_at_capacity
+examples/pd/native_kv/test_pinned_receiver.py::PinnedReceiveTest::test_eight_slot_tcp_frames_exact_at_capacity
+examples/pd/native_kv/test_pinned_receiver.py::PinnedReceiveTest::test_eight_slot_tcp_frames_exact_at_capacity
+  /home/gspark/workspace/aicomp-rngd-stream-265/opt_prime/examples/pd/native_kv/test_pinned_receiver.py:75: DeprecationWarning: The argument 'device' of Tensor.is_pinned() is deprecated. Please do not pass this argument. (Triggered internally at /__w/pytorch/pytorch/aten/src/ATen/native/Memory.cpp:31.)
+    self.assertTrue(direct.data.host.is_pinned('cuda'))
+
+examples/pd/native_kv/test_pinned_receiver.py::PinnedReceiveTest::test_real_multipart_exact_and_owned
+examples/pd/native_kv/test_pinned_receiver.py::PinnedReceiveTest::test_real_multipart_exact_and_owned
+examples/pd/native_kv/test_pinned_receiver.py::PinnedReceiveTest::test_real_multipart_exact_and_owned
+  /home/gspark/workspace/aicomp-rngd-stream-265/opt_prime/examples/pd/native_kv/test_pinned_receiver.py:25: DeprecationWarning: The argument 'device' of Tensor.is_pinned() is deprecated. Please do not pass this argument. (Triggered internally at /__w/pytorch/pytorch/aten/src/ATen/native/Memory.cpp:31.)
+    self.assertTrue(direct.data.host.is_pinned('cuda'))
+
+tests/test_kvstore.py::test_second_engine_reuses_chunks_from_the_store[lmcache]
+  /home/gspark/venvs/pt213/lib/python3.10/site-packages/torch/nn/attention/flex_attention.py:2481: UserWarning: flex_attention called without torch.compile() - this will use an unfused implementation that materializes the full scores matrix instead of generating a fused kernel.
+
+  SOLUTION: Use torch.compile(flex_attention)(...)
+
+  If you want to debug your score_mod/mask_mod, you can set:
+  torch.nn.attention.flex_attention._FLEX_ATTENTION_DISABLE_COMPILE_DEBUG = True
+
+  This will allow you to use print statements or breakpoints. Note: This doesn't work with the backwards pass and may produce incorrect results.
+    _warn_once(
+
+tests/test_pd_stream_admission.py: 14 warnings
+  /home/gspark/venvs/pt213/lib/python3.10/site-packages/torch/jit/_script.py:365: DeprecationWarning: `torch.jit.script_method` is deprecated. Please switch to `torch.compile` or `torch.export`.
+    warnings.warn(
+
+-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html
+591 passed, 15 skipped, 27 warnings, 253 subtests passed in 165.79s (0:02:45)
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-source-hardware.log b/opt_prime/examples/pd/http_matrix/evidence265/stream265-source-hardware.log
new file mode 100644
index 00000000..0379f2a1
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-source-hardware.log
@@ -0,0 +1,26 @@
+
+Loading weights:   0%|          | 0/291 [00:00<?, ?it/s]
+Loading weights:   0%|          | 1/291 [00:00<01:35,  3.05it/s]
+Loading weights:  20%|██        | 59/291 [00:00<00:01, 177.50it/s]
+Loading weights:  32%|███▏      | 93/291 [00:00<00:00, 209.61it/s]
+Loading weights:  43%|████▎     | 124/291 [00:00<00:00, 196.95it/s]
+Loading weights:  52%|█████▏    | 150/291 [00:00<00:00, 199.52it/s]
+Loading weights:  60%|██████    | 175/291 [00:00<00:00, 206.01it/s]
+Loading weights:  69%|██████▉   | 202/291 [00:01<00:00, 206.67it/s]
+Loading weights:  79%|███████▊  | 229/291 [00:01<00:00, 208.53it/s]
+Loading weights:  88%|████████▊ | 256/291 [00:01<00:00, 208.64it/s]
+Loading weights:  97%|█████████▋| 283/291 [00:01<00:00, 210.54it/s]
+Loading weights: 100%|██████████| 291/291 [00:01<00:00, 196.10it/s]
+[full-engine] C=128 compiled in 74s
+Traceback (most recent call last):
+  File "/tmp/stream265-source-hardware.py", line 13, in <module>
+    engine=_RngdFull(SimpleNamespace(model=model,width='128',max_context=128,chip=0))
+  File "/home/gspark/workspace/aicomp-rngd-stream-265/opt_prime/opt_prime/accel/pd/prefill_server.py", line 89, in __init__
+    self.engine = FullPrefillEngine(cfg, sd, C=widths[0], group=2, chip=a.chip, cache_dir=None)
+  File "/tmp/rngd-gpu201-src/rngd_pd/engines/prefill_full.py", line 141, in __init__
+    self._build_lmhead(cache_dir)
+  File "/tmp/rngd-gpu201-src/rngd_pd/engines/prefill_full.py", line 219, in _build_lmhead
+    from rngd_pd.kernels import dsl_b1 as LE
+  File "/tmp/rngd-gpu201-src/rngd_pd/kernels/dsl_b1.py", line 23, in <module>
+    import furiosa.native_llm_common as nlc
+ModuleNotFoundError: No module named 'furiosa.native_llm_common'
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-source-hardware.py b/opt_prime/examples/pd/http_matrix/evidence265/stream265-source-hardware.py
new file mode 100644
index 00000000..84cacdf7
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-source-hardware.py
@@ -0,0 +1,27 @@
+import json,time,hashlib
+from pathlib import Path
+from types import SimpleNamespace
+import torch
+from furiosa.torch._register import _register
+_register()
+import rngd_pd.engines.fsdk as sdk
+sdk.open_device=lambda *a,**k:(sdk.set_fusion(4),sdk.set_mesh_kind(sdk.MeshKind.Mesh(2)))
+from opt_prime.accel.pd.prefill_server import _RngdFull
+from transformers import AutoTokenizer
+model='/tmp/serving-matrix-models/KONI-8B'
+out=Path('/home/gspark/workspace/npu_offload/analysis/serving-matrix-201/stream265-source-hardware.json')
+engine=_RngdFull(SimpleNamespace(model=model,width='128',max_context=128,chip=0))
+tokens=AutoTokenizer.from_pretrained(model).encode('Seoul is the capital of',add_special_tokens=True)
+with torch.inference_mode():
+ k,v,logits=engine.prefill(tokens)
+ spans=[]; ks=[];vs=[];start=time.perf_counter()
+ def emit(layer,first,keys,values):
+  spans.append({'layer':layer,'first':first,'layers':keys.shape[0],'t_s':time.perf_counter()-start})
+  ks.append(keys.clone());vs.append(values.clone())
+ other=engine.prefill_stream(tokens,emit)
+ result={'model':'KONI-8B','width':128,'input_tokens':tokens,'spans':spans,'completed_s':time.perf_counter()-start,
+         'keys_exact':torch.equal(k,torch.cat(ks)),'values_exact':torch.equal(v,torch.cat(vs)),
+         'logits_exact':torch.equal(logits,other),'token_whole':int(logits.argmax()),'token_stream':int(other.argmax()),
+         'scope':'RNGD source parity only; no end-to-end decoder or speedup claim'}
+ out.write_text(json.dumps(result,indent=2)+'\n');print(json.dumps(result),flush=True)
+ assert result['keys_exact'] and result['values_exact'] and result['logits_exact']
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-source-integration.log b/opt_prime/examples/pd/http_matrix/evidence265/stream265-source-integration.log
new file mode 100644
index 00000000..c2eb0fca
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-source-integration.log
@@ -0,0 +1,19 @@
+....................................                                     [100%]
+=============================== warnings summary ===============================
+tests/test_pd_stream_admission.py::test_hybrid_partial_import_happens_before_completion_without_admission[cpu]
+  /home/gspark/venvs/pt213/lib/python3.10/site-packages/torch/nn/attention/flex_attention.py:2481: UserWarning: flex_attention called without torch.compile() - this will use an unfused implementation that materializes the full scores matrix instead of generating a fused kernel.
+
+  SOLUTION: Use torch.compile(flex_attention)(...)
+
+  If you want to debug your score_mod/mask_mod, you can set:
+  torch.nn.attention.flex_attention._FLEX_ATTENTION_DISABLE_COMPILE_DEBUG = True
+
+  This will allow you to use print statements or breakpoints. Note: This doesn't work with the backwards pass and may produce incorrect results.
+    _warn_once(
+
+tests/test_pd_stream_admission.py: 14 warnings
+  /home/gspark/venvs/pt213/lib/python3.10/site-packages/torch/jit/_script.py:365: DeprecationWarning: `torch.jit.script_method` is deprecated. Please switch to `torch.compile` or `torch.export`.
+    warnings.warn(
+
+-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html
+36 passed, 15 warnings in 10.57s
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-source-queue.json b/opt_prime/examples/pd/http_matrix/evidence265/stream265-source-queue.json
new file mode 100644
index 00000000..ef57f7cc
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-source-queue.json
@@ -0,0 +1,18 @@
+[
+  {
+    "label": "qwen06b",
+    "exit_code": 0
+  },
+  {
+    "label": "qwen4b",
+    "exit_code": 0
+  },
+  {
+    "label": "gemma",
+    "exit_code": 0
+  },
+  {
+    "label": "koni",
+    "exit_code": 0
+  }
+]
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-source-queue.py b/opt_prime/examples/pd/http_matrix/evidence265/stream265-source-queue.py
new file mode 100644
index 00000000..23eb0e14
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-source-queue.py
@@ -0,0 +1,13 @@
+import os,subprocess,json
+from pathlib import Path
+root=Path('/home/gspark/workspace/npu_offload/analysis/serving-matrix-201')
+env=dict(os.environ,OMP_NUM_THREADS='4',MKL_NUM_THREADS='4',TORCH_DEVICE_BACKEND_AUTOLOAD='0',PYTHONPATH='/home/gspark/workspace/aicomp-rngd-stream-265/opt_prime:/home/gspark/workspace/aicomp-rngd-stream-265/opt_prime/examples/pd:/tmp/rngd-gpu201-src:/home/gspark/workspace/aicomp-live-pd-234/opt_prime/examples/pd/serving_matrix')
+rows=[]
+for label,family,model,width in [('qwen06b','qwen','/home/gspark/.cache/huggingface/hub/models--Qwen--Qwen3-0.6B/snapshots/c1899de289a04d12100db370d81485cdf75e47ca',128),('qwen4b','qwen','/tmp/serving-matrix-models/Qwen3-4B',128),('gemma','gemma','/tmp/serving-matrix-models/Gemma3-1B',128),('koni','koni','/tmp/serving-matrix-models/KONI-8B',512)]:
+ log=root/f'stream265-{label}-source.log'
+ print('start',label,flush=True)
+ with log.open('x') as f:
+  p=subprocess.run(['/tmp/codex-rngd-gpu-201/bin/python','/tmp/stream265-factory-hardware.py','--label',label,'--family',family,'--model',model,'--width',str(width)],env=env,stdout=f,stderr=subprocess.STDOUT)
+ rows.append({'label':label,'exit_code':p.returncode});(root/'stream265-source-queue.json').write_text(json.dumps(rows,indent=2)+'\n')
+ print('finished',label,p.returncode,flush=True)
+ if p.returncode:break
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-source-red.log b/opt_prime/examples/pd/http_matrix/evidence265/stream265-source-red.log
new file mode 100644
index 00000000..d7ec5676
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-source-red.log
@@ -0,0 +1,113 @@
+FFFFF                                                                    [100%]
+=================================== FAILURES ===================================
+__ test_source_suffix_written_before_notification_and_complete_before_return ___
+
+    def test_source_suffix_written_before_notification_and_complete_before_return():
+        slab=np.zeros((1,2,2,1,8,2),dtype=np.uint16)
+        messages=[]
+        class Engine:
+            def prefill_stream(self,tokens,emit):
+                for layer in range(2):
+                    k=torch.full((1,1,len(tokens),2),layer+1,dtype=torch.bfloat16)
+                    emit(layer,0,k,k+2)
+                    assert len(messages)==layer+1
+                return torch.tensor([1.,2.])
+        def publish(msg):
+            layer=msg['kv_span'][0]
+            got=torch.from_numpy(slab[0,0,layer,:,:2].view(np.int16)).view(torch.bfloat16)
+            assert torch.all(got==layer+1)
+            messages.append(msg)
+>       logits=server.stream_result(Engine(),dict(id=7,tokens=[1,2,3,4],send_from=2,slot=0),slab,publish)
+E       AttributeError: module 'opt_prime.accel.pd.prefill_server' has no attribute 'stream_result'
+
+tests/test_prefill_source_progress.py:23: AttributeError
+_________________ test_invalid_source_never_completes[overlap] _________________
+
+mode = 'overlap'
+
+    @pytest.mark.parametrize('mode',['overlap','hole','geometry'])
+    def test_invalid_source_never_completes(mode):
+        slab=np.zeros((1,2,2,1,8,2),dtype=np.uint16);messages=[]
+        class Engine:
+            def prefill_stream(self,tokens,emit):
+                k=torch.ones((1,1,2,2),dtype=torch.bfloat16)
+                emit(0,0,k,k)
+                if mode=='overlap':emit(0,0,k*3,k*3)
+                if mode=='geometry':emit(1,0,k,torch.ones((1,2,2,2)))
+                return torch.zeros(2)
+        with pytest.raises(ValueError):
+>           server.stream_result(Engine(),dict(id=0,tokens=[1,2],send_from=0,slot=0),slab,messages.append)
+E           AttributeError: module 'opt_prime.accel.pd.prefill_server' has no attribute 'stream_result'
+
+tests/test_prefill_source_progress.py:40: AttributeError
+__________________ test_invalid_source_never_completes[hole] ___________________
+
+mode = 'hole'
+
+    @pytest.mark.parametrize('mode',['overlap','hole','geometry'])
+    def test_invalid_source_never_completes(mode):
+        slab=np.zeros((1,2,2,1,8,2),dtype=np.uint16);messages=[]
+        class Engine:
+            def prefill_stream(self,tokens,emit):
+                k=torch.ones((1,1,2,2),dtype=torch.bfloat16)
+                emit(0,0,k,k)
+                if mode=='overlap':emit(0,0,k*3,k*3)
+                if mode=='geometry':emit(1,0,k,torch.ones((1,2,2,2)))
+                return torch.zeros(2)
+        with pytest.raises(ValueError):
+>           server.stream_result(Engine(),dict(id=0,tokens=[1,2],send_from=0,slot=0),slab,messages.append)
+E           AttributeError: module 'opt_prime.accel.pd.prefill_server' has no attribute 'stream_result'
+
+tests/test_prefill_source_progress.py:40: AttributeError
+________________ test_invalid_source_never_completes[geometry] _________________
+
+mode = 'geometry'
+
+    @pytest.mark.parametrize('mode',['overlap','hole','geometry'])
+    def test_invalid_source_never_completes(mode):
+        slab=np.zeros((1,2,2,1,8,2),dtype=np.uint16);messages=[]
+        class Engine:
+            def prefill_stream(self,tokens,emit):
+                k=torch.ones((1,1,2,2),dtype=torch.bfloat16)
+                emit(0,0,k,k)
+                if mode=='overlap':emit(0,0,k*3,k*3)
+                if mode=='geometry':emit(1,0,k,torch.ones((1,2,2,2)))
+                return torch.zeros(2)
+        with pytest.raises(ValueError):
+>           server.stream_result(Engine(),dict(id=0,tokens=[1,2],send_from=0,slot=0),slab,messages.append)
+E           AttributeError: module 'opt_prime.accel.pd.prefill_server' has no attribute 'stream_result'
+
+tests/test_prefill_source_progress.py:40: AttributeError
+_____________ test_rngd_full_emits_each_group_before_next_advance ______________
+
+    def test_rngd_full_emits_each_group_before_next_advance():
+        emitted=[]
+        class Stage:
+            Ks=[];Vs=[];x=torch.tensor([1.])
+            def advance(self,count):
+                assert count==1
+                assert len(emitted)==len(self.Ks)
+                self.Ks.append(torch.ones((1,1,2,4))*len(self.Ks))
+                self.Vs.append(torch.ones((1,1,4,2))*len(self.Vs))
+                return len(self.Ks)==2
+        class Engine:
+            def begin_device_prefill(self,tokens):return Stage()
+            def _logits(self,x,n):return np.array([1.,2.])
+        wrapper=server._RngdFull.__new__(server._RngdFull)
+        wrapper.engine=Engine();wrapper.widths=[4]
+        def emit(layer,start,k,v):
+            assert (layer,start)==(len(emitted),0)
+            assert k.shape==v.shape==(1,1,3,2)
+            assert k.device.type==v.device.type=='cpu'
+            emitted.append(layer)
+>       assert wrapper.prefill_stream([1,2,3],emit).tolist()==[1.,2.]
+E       AttributeError: '_RngdFull' object has no attribute 'prefill_stream'
+
+tests/test_prefill_source_progress.py:65: AttributeError
+=========================== short test summary info ============================
+FAILED tests/test_prefill_source_progress.py::test_source_suffix_written_before_notification_and_complete_before_return
+FAILED tests/test_prefill_source_progress.py::test_invalid_source_never_completes[overlap]
+FAILED tests/test_prefill_source_progress.py::test_invalid_source_never_completes[hole]
+FAILED tests/test_prefill_source_progress.py::test_invalid_source_never_completes[geometry]
+FAILED tests/test_prefill_source_progress.py::test_rngd_full_emits_each_group_before_next_advance
+5 failed in 0.95s
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-source-server-final.log b/opt_prime/examples/pd/http_matrix/evidence265/stream265-source-server-final.log
new file mode 100644
index 00000000..414ee96a
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-source-server-final.log
@@ -0,0 +1,2 @@
+......                                                                   [100%]
+6 passed in 2.73s
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-source-server.log b/opt_prime/examples/pd/http_matrix/evidence265/stream265-source-server.log
new file mode 100644
index 00000000..52b49a22
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-source-server.log
@@ -0,0 +1,58 @@
+.....F                                                                   [100%]
+=================================== FAILURES ===================================
+____________ test_real_server_and_receiver_stream_before_completion ____________
+
+children = (<function children.<locals>.make at 0x73c3fcb04430>, [<Popen: returncode: 1 args: ['/tmp/pytest-of-gspark/pytest-192/test_real_ser...>], ['psm_f0688770'], PosixPath('/tmp/pytest-of-gspark/pytest-192/test_real_server_and_receiver_0/gate'))
+
+    def test_real_server_and_receiver_stream_before_completion(children):
+        import sys,time
+        from opt_prime.accel.pd import PDRequest
+        make,_,_,gate=children
+        child=gate.parent/'child'
+        child.write_text(f'#!{sys.executable}\n'+'''
+    import sys,os,time
+    from pathlib import Path
+    import torch
+    from opt_prime.accel.pd import prefill_server as server
+    class Engine:
+        def __init__(self,args):pass
+        def prefill_stream(self,tokens,emit):
+            k=torch.ones((1,1,1,2),dtype=torch.bfloat16)
+            emit(0,0,k,k)
+            gate=Path(os.environ['PREFILL_TEST_GATE'])
+            while not gate.exists():time.sleep(.005)
+            emit(0,1,k*2,k*2)
+            return torch.tensor([0.,1.])
+    server._RngdFull=Engine
+    sys.argv=[sys.argv[0]]+sys.argv[3:]+['--engine','rngd-full','--model',str(Path.cwd())]
+    server.main()
+    ''')
+>       w=make(stream_layers=True)
+
+tests/test_prefill_source_progress.py:92:
+_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
+tests/test_subprocess_prefill_failure.py:70: in make
+    w = SubprocessPrefillWorker(str(script), [], slots=2, layers=1, kv_heads=1,
+opt_prime/accel/pd/pd_batching.py:383: in __init__
+    ready.result(timeout=startup_timeout)
+/usr/lib/python3.10/concurrent/futures/_base.py:458: in result
+    return self.__get_result()
+_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
+
+self = None
+
+    def __get_result(self):
+        if self._exception:
+            try:
+>               raise self._exception
+E               RuntimeError: prefill subprocess response failed: prefill subprocess exited
+
+/usr/lib/python3.10/concurrent/futures/_base.py:403: RuntimeError
+----------------------------- Captured stderr call -----------------------------
+Traceback (most recent call last):
+  File "/tmp/pytest-of-gspark/pytest-192/test_real_server_and_receiver_0/child", line 6, in <module>
+    from opt_prime.accel.pd import prefill_server as server
+ModuleNotFoundError: No module named 'opt_prime'
+=========================== short test summary info ============================
+FAILED tests/test_prefill_source_progress.py::test_real_server_and_receiver_stream_before_completion
+1 failed, 5 passed in 2.61s
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-wire-focused.log b/opt_prime/examples/pd/http_matrix/evidence265/stream265-wire-focused.log
new file mode 100644
index 00000000..1630596d
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-wire-focused.log
@@ -0,0 +1,2 @@
+...................................                                      [100%]
+35 passed in 3.61s
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265-wire-red.log b/opt_prime/examples/pd/http_matrix/evidence265/stream265-wire-red.log
new file mode 100644
index 00000000..f450b866
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265-wire-red.log
@@ -0,0 +1,195 @@
+FFFFFFFF                                                                 [100%]
+=================================== FAILURES ===================================
+____________ test_progress_reads_actual_shm_before_final_completion ____________
+
+children = (<function children.<locals>.make at 0x797a991a1f30>, [], [], PosixPath('/tmp/pytest-of-gspark/pytest-180/test_progress_reads_actual_shm0/gate'))
+
+    def test_progress_reads_actual_shm_before_final_completion(children):
+>       w,gate=make_stream(children)
+
+tests/test_subprocess_layer_progress.py:58:
+_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
+tests/test_subprocess_layer_progress.py:45: in make_stream
+    return make(mode,stream_layers=True),gate
+_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
+
+mode = 'ok', kwargs = {'stream_layers': True}
+
+    def make(mode="ok", **kwargs):
+        env = dict(os.environ, PREFILL_TEST_MODE=mode, PREFILL_TEST_GATE=str(tmp_path / "gate"))
+>       w = SubprocessPrefillWorker(str(script), [], slots=2, layers=1, kv_heads=1,
+                                   head_dim=2, max_context=8, env=env, **kwargs)
+E       TypeError: SubprocessPrefillWorker.__init__() got an unexpected keyword argument 'stream_layers'
+
+tests/test_subprocess_prefill_failure.py:70: TypeError
+____________ test_bad_stream_never_completes_successfully[overlap] _____________
+
+children = (<function children.<locals>.make at 0x797a9901eb90>, [], [], PosixPath('/tmp/pytest-of-gspark/pytest-180/test_bad_stream_never_complete0/gate'))
+mode = 'overlap'
+
+    @pytest.mark.parametrize('mode',['overlap','hole','short','exit'])
+    def test_bad_stream_never_completes_successfully(children,mode):
+>       w,gate=make_stream(children,mode)
+
+tests/test_subprocess_layer_progress.py:74:
+_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
+tests/test_subprocess_layer_progress.py:45: in make_stream
+    return make(mode,stream_layers=True),gate
+_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
+
+mode = 'overlap', kwargs = {'stream_layers': True}
+
+    def make(mode="ok", **kwargs):
+        env = dict(os.environ, PREFILL_TEST_MODE=mode, PREFILL_TEST_GATE=str(tmp_path / "gate"))
+>       w = SubprocessPrefillWorker(str(script), [], slots=2, layers=1, kv_heads=1,
+                                   head_dim=2, max_context=8, env=env, **kwargs)
+E       TypeError: SubprocessPrefillWorker.__init__() got an unexpected keyword argument 'stream_layers'
+
+tests/test_subprocess_prefill_failure.py:70: TypeError
+______________ test_bad_stream_never_completes_successfully[hole] ______________
+
+children = (<function children.<locals>.make at 0x797a9901ecb0>, [], [], PosixPath('/tmp/pytest-of-gspark/pytest-180/test_bad_stream_never_complete1/gate'))
+mode = 'hole'
+
+    @pytest.mark.parametrize('mode',['overlap','hole','short','exit'])
+    def test_bad_stream_never_completes_successfully(children,mode):
+>       w,gate=make_stream(children,mode)
+
+tests/test_subprocess_layer_progress.py:74:
+_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
+tests/test_subprocess_layer_progress.py:45: in make_stream
+    return make(mode,stream_layers=True),gate
+_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
+
+mode = 'hole', kwargs = {'stream_layers': True}
+
+    def make(mode="ok", **kwargs):
+        env = dict(os.environ, PREFILL_TEST_MODE=mode, PREFILL_TEST_GATE=str(tmp_path / "gate"))
+>       w = SubprocessPrefillWorker(str(script), [], slots=2, layers=1, kv_heads=1,
+                                   head_dim=2, max_context=8, env=env, **kwargs)
+E       TypeError: SubprocessPrefillWorker.__init__() got an unexpected keyword argument 'stream_layers'
+
+tests/test_subprocess_prefill_failure.py:70: TypeError
+_____________ test_bad_stream_never_completes_successfully[short] ______________
+
+children = (<function children.<locals>.make at 0x797a9901ed40>, [], [], PosixPath('/tmp/pytest-of-gspark/pytest-180/test_bad_stream_never_complete2/gate'))
+mode = 'short'
+
+    @pytest.mark.parametrize('mode',['overlap','hole','short','exit'])
+    def test_bad_stream_never_completes_successfully(children,mode):
+>       w,gate=make_stream(children,mode)
+
+tests/test_subprocess_layer_progress.py:74:
+_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
+tests/test_subprocess_layer_progress.py:45: in make_stream
+    return make(mode,stream_layers=True),gate
+_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
+
+mode = 'short', kwargs = {'stream_layers': True}
+
+    def make(mode="ok", **kwargs):
+        env = dict(os.environ, PREFILL_TEST_MODE=mode, PREFILL_TEST_GATE=str(tmp_path / "gate"))
+>       w = SubprocessPrefillWorker(str(script), [], slots=2, layers=1, kv_heads=1,
+                                   head_dim=2, max_context=8, env=env, **kwargs)
+E       TypeError: SubprocessPrefillWorker.__init__() got an unexpected keyword argument 'stream_layers'
+
+tests/test_subprocess_prefill_failure.py:70: TypeError
+______________ test_bad_stream_never_completes_successfully[exit] ______________
+
+children = (<function children.<locals>.make at 0x797a9901e8c0>, [], [], PosixPath('/tmp/pytest-of-gspark/pytest-180/test_bad_stream_never_complete3/gate'))
+mode = 'exit'
+
+    @pytest.mark.parametrize('mode',['overlap','hole','short','exit'])
+    def test_bad_stream_never_completes_successfully(children,mode):
+>       w,gate=make_stream(children,mode)
+
+tests/test_subprocess_layer_progress.py:74:
+_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
+tests/test_subprocess_layer_progress.py:45: in make_stream
+    return make(mode,stream_layers=True),gate
+_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
+
+mode = 'exit', kwargs = {'stream_layers': True}
+
+    def make(mode="ok", **kwargs):
+        env = dict(os.environ, PREFILL_TEST_MODE=mode, PREFILL_TEST_GATE=str(tmp_path / "gate"))
+>       w = SubprocessPrefillWorker(str(script), [], slots=2, layers=1, kv_heads=1,
+                                   head_dim=2, max_context=8, env=env, **kwargs)
+E       TypeError: SubprocessPrefillWorker.__init__() got an unexpected keyword argument 'stream_layers'
+
+tests/test_subprocess_prefill_failure.py:70: TypeError
+_______________ test_streaming_opt_in_requires_capable_producer ________________
+
+children = (<function children.<locals>.make at 0x797a9901eb90>, [], [], PosixPath('/tmp/pytest-of-gspark/pytest-180/test_streaming_opt_in_requires0/gate'))
+
+    def test_streaming_opt_in_requires_capable_producer(children):
+        make,procs,_,_=children
+        with pytest.raises(RuntimeError,match='progress'):
+>           make(stream_layers=True,startup_timeout=1)
+
+tests/test_subprocess_layer_progress.py:86:
+_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
+
+mode = 'ok', kwargs = {'stream_layers': True, 'startup_timeout': 1}
+
+    def make(mode="ok", **kwargs):
+        env = dict(os.environ, PREFILL_TEST_MODE=mode, PREFILL_TEST_GATE=str(tmp_path / "gate"))
+>       w = SubprocessPrefillWorker(str(script), [], slots=2, layers=1, kv_heads=1,
+                                   head_dim=2, max_context=8, env=env, **kwargs)
+E       TypeError: SubprocessPrefillWorker.__init__() got an unexpected keyword argument 'stream_layers'
+
+tests/test_subprocess_prefill_failure.py:70: TypeError
+___________ test_cancel_then_same_slot_reuse_keeps_new_stream_owned ____________
+
+children = (<function children.<locals>.make at 0x797a9901f520>, [], [], PosixPath('/tmp/pytest-of-gspark/pytest-180/test_cancel_then_same_slot_reu0/gate'))
+
+    def test_cancel_then_same_slot_reuse_keeps_new_stream_owned(children):
+>       w,gate=make_stream(children)
+
+tests/test_subprocess_layer_progress.py:91:
+_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
+tests/test_subprocess_layer_progress.py:45: in make_stream
+    return make(mode,stream_layers=True),gate
+_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
+
+mode = 'ok', kwargs = {'stream_layers': True}
+
+    def make(mode="ok", **kwargs):
+        env = dict(os.environ, PREFILL_TEST_MODE=mode, PREFILL_TEST_GATE=str(tmp_path / "gate"))
+>       w = SubprocessPrefillWorker(str(script), [], slots=2, layers=1, kv_heads=1,
+                                   head_dim=2, max_context=8, env=env, **kwargs)
+E       TypeError: SubprocessPrefillWorker.__init__() got an unexpected keyword argument 'stream_layers'
+
+tests/test_subprocess_prefill_failure.py:70: TypeError
+____________ test_progress_positions_are_absolute_for_cached_prefix ____________
+
+children = (<function children.<locals>.make at 0x797a9901f130>, [], [], PosixPath('/tmp/pytest-of-gspark/pytest-180/test_progress_positions_are_ab0/gate'))
+
+    def test_progress_positions_are_absolute_for_cached_prefix(children):
+>       w,gate=make_stream(children)
+
+tests/test_subprocess_layer_progress.py:104:
+_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
+tests/test_subprocess_layer_progress.py:45: in make_stream
+    return make(mode,stream_layers=True),gate
+_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
+
+mode = 'ok', kwargs = {'stream_layers': True}
+
+    def make(mode="ok", **kwargs):
+        env = dict(os.environ, PREFILL_TEST_MODE=mode, PREFILL_TEST_GATE=str(tmp_path / "gate"))
+>       w = SubprocessPrefillWorker(str(script), [], slots=2, layers=1, kv_heads=1,
+                                   head_dim=2, max_context=8, env=env, **kwargs)
+E       TypeError: SubprocessPrefillWorker.__init__() got an unexpected keyword argument 'stream_layers'
+
+tests/test_subprocess_prefill_failure.py:70: TypeError
+=========================== short test summary info ============================
+FAILED tests/test_subprocess_layer_progress.py::test_progress_reads_actual_shm_before_final_completion
+FAILED tests/test_subprocess_layer_progress.py::test_bad_stream_never_completes_successfully[overlap]
+FAILED tests/test_subprocess_layer_progress.py::test_bad_stream_never_completes_successfully[hole]
+FAILED tests/test_subprocess_layer_progress.py::test_bad_stream_never_completes_successfully[short]
+FAILED tests/test_subprocess_layer_progress.py::test_bad_stream_never_completes_successfully[exit]
+FAILED tests/test_subprocess_layer_progress.py::test_streaming_opt_in_requires_capable_producer
+FAILED tests/test_subprocess_layer_progress.py::test_cancel_then_same_slot_reuse_keeps_new_stream_owned
+FAILED tests/test_subprocess_layer_progress.py::test_progress_positions_are_absolute_for_cached_prefix
+8 failed in 1.47s
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265_atom.py b/opt_prime/examples/pd/http_matrix/evidence265/stream265_atom.py
new file mode 100644
index 00000000..d3ed9d69
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265_atom.py
@@ -0,0 +1,47 @@
+"""Owned hardware check: same RNGD source, ATOM whole/partial import, optional prefetch."""
+import argparse,hashlib,json,os,time
+from pathlib import Path
+import torch
+from transformers import AutoTokenizer
+from opt_prime.accel.pd import PDRequest
+from opt_prime.accel.pd.pd_batching import SubprocessPrefillWorker
+from opt_prime.accel.platforms.atom.engine import AtomRblnEngine
+p=argparse.ArgumentParser()
+for name in ['model','family','label','cache-dir']:p.add_argument('--'+name,required=True)
+p.add_argument('--kv-blocks',type=int,required=True);a=p.parse_args()
+out=Path(f'stream265-{a.label}-atom.json')
+if out.exists():raise RuntimeError('refuse overwrite')
+result={'model':a.model,'width':512,'output_tokens':8,'batch_size':1,'cases':[],'scope':'same-route token parity and B1 ownership, not semantic accuracy or speedup'}
+result['code_sha256']={name:hashlib.sha256((Path('stream265-code/opt_prime')/name).read_bytes()).hexdigest() for name in ['accel/platforms/atom/batching.py','accel/pd/pd_batching.py','accel/pd/prefill_server.py','accel/pd/layer_progress.py']}
+nodes=list(Path('/dev').glob('rbln[0-9]*'));assert len(nodes)==1
+engine=AtomRblnEngine(devices=[int(nodes[0].name[4:])],max_seq_len=9216,batch_size=1,kv_blocks=a.kv_blocks,cache_dir=a.cache_dir)
+worker=None
+try:
+ engine.start(a.model);c=engine.model.config.get_text_config()
+ base=AutoTokenizer.from_pretrained(a.model,local_files_only=True).encode('Seoul is the capital of South Korea. Count one two three.',add_special_tokens=True)
+ prompts=[tuple(base),tuple((base*((512+len(base)-1)//len(base)))[:512])]
+ worker=SubprocessPrefillWorker('./furiosa_prefill_python.sh',['--engine','factory','--engine-factory',f'streaming_factories:{a.family}','--model',a.model,'--width','512','--chip','0'],slots=1,layers=c.num_hidden_layers,kv_heads=c.num_key_value_heads,head_dim=getattr(c,'head_dim',None) or c.hidden_size//c.num_attention_heads,max_context=512,env=dict(os.environ),stream_layers=True)
+ reference=None
+ for stream,prefetch in [(False,False),(True,False),(True,True),(False,True)]:
+  with worker._lock:assert not worker._futures
+  worker.stream_layers=stream
+  b=engine.batcher(prefix_cache=False,prefill_worker=worker,local_prefill=False,remote_prefetch=prefetch)
+  traces=[];original=b._import_progress
+  def observed(r,**kw):
+   before=b.stats['progress_spans'];active=len(b.decoding);done=r.pending.done()
+   original(r,**kw)
+   if b.stats['progress_spans']>before:traces.append(dict(spans=b.stats['progress_spans']-before,active_decode=active,source_done_before=done,source_done_after=r.pending.done()))
+  b._import_progress=observed
+  f=[b.submit(PDRequest(f'{stream}-{prefetch}-{i}',ids,8)) for i,ids in enumerate(prompts)]
+  b.run_until_idle();tokens=[v.result(timeout=1) for v in f]
+  if reference is None:reference=tokens
+  row=dict(stream=stream,prefetch=prefetch,tokens=tokens,tokens_exact=tokens==reference,progress_spans=b.stats['progress_spans'],imports=traces,idle=b.idle)
+  result['cases'].append(row);out.write_text(json.dumps(result,indent=2)+'\n');print(json.dumps(row),flush=True)
+  assert row['tokens_exact'] and b.idle and all(e['active_decode']==0 for e in traces)
+  assert (row['progress_spans']>0)==stream
+ result['stage']='complete'
+except Exception as error:
+ result['stage']='failed';result['error']=repr(error);raise
+finally:
+ if worker is not None:worker.close()
+ engine.close();out.write_text(json.dumps(result,indent=2)+'\n')
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265_control.py b/opt_prime/examples/pd/http_matrix/evidence265/stream265_control.py
new file mode 100644
index 00000000..4d5e603a
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265_control.py
@@ -0,0 +1,16 @@
+"""Test-only notification timing control; source compute and SHM writes unchanged."""
+def deferred(original,engine,request,slab,publish):
+ messages=[]
+ logits=original(engine,request,slab,messages.append)
+ for message in messages:publish(message)
+ return logits
+
+def self_check():
+ trace=[]
+ def source(engine,request,slab,publish):
+  for i in range(3):trace.append(('copy',i));publish({'i':i})
+  trace.append(('logits',None));return 42
+ result=deferred(source,None,None,None,lambda m:trace.append(('notify',m['i'])))
+ assert result==42 and trace==[('copy',0),('copy',1),('copy',2),('logits',None),('notify',0),('notify',1),('notify',2)]
+ if __name__=='__main__':print('deferred control: copies precede logits; notification order preserved after logits')
+self_check()
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265_fault_atom.py b/opt_prime/examples/pd/http_matrix/evidence265/stream265_fault_atom.py
new file mode 100644
index 00000000..0181b74a
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265_fault_atom.py
@@ -0,0 +1,94 @@
+"""Owned hardware check: same RNGD source, ATOM whole/partial import, optional prefetch."""
+import argparse,hashlib,json,os,time
+from pathlib import Path
+import torch
+from transformers import AutoTokenizer
+from opt_prime.accel.pd import PDRequest
+from opt_prime.accel.pd.pd_batching import SubprocessPrefillWorker
+from opt_prime.accel.platforms.atom.engine import AtomRblnEngine
+p=argparse.ArgumentParser()
+for name in ['model','family','label','cache-dir']:p.add_argument('--'+name,required=True)
+p.add_argument('--kv-blocks',type=int,required=True);a=p.parse_args()
+out=Path(f'stream265-{a.label}-fault-atom.json')
+if out.exists():raise RuntimeError('refuse overwrite')
+result={'model':a.model,'width':8192,'output_tokens':16,'batch_size':1,'cases':[],'scope':'actual partial-import cancellation, lane reuse and producer death; not performance'}
+result['code_sha256']={name:hashlib.sha256((Path('stream265-code/opt_prime')/name).read_bytes()).hexdigest() for name in ['accel/platforms/atom/batching.py','accel/pd/pd_batching.py','accel/pd/prefill_server.py','accel/pd/layer_progress.py']}
+nodes=list(Path('/dev').glob('rbln[0-9]*'));assert len(nodes)==1
+engine=AtomRblnEngine(devices=[int(nodes[0].name[4:])],max_seq_len=9216,batch_size=1,kv_blocks=a.kv_blocks,cache_dir=a.cache_dir)
+worker=None
+try:
+ engine.start(a.model);c=engine.model.config.get_text_config()
+ base=AutoTokenizer.from_pretrained(a.model,local_files_only=True).encode('Seoul is the capital of South Korea. Count one two three.',add_special_tokens=True)
+ prompts=[tuple((base*((n+len(base)-1)//len(base)))[:n]) for n in (2048,8192)]
+ def make_worker():
+  return SubprocessPrefillWorker('./furiosa_prefill_python.sh',['--engine','factory','--engine-factory',f'streaming_factories:{a.family}','--model',a.model,'--width','8192','--chip','0'],slots=1,layers=c.num_hidden_layers,kv_heads=c.num_key_value_heads,head_dim=getattr(c,'head_dim',None) or c.hidden_size//c.num_attention_heads,max_context=8192,env=dict(os.environ),stream_layers=True)
+ worker=make_worker()
+
+ from opt_prime.accel.pd.runtime import RequestCancelled
+ def drain(batch,timeout=600):
+  deadline=time.monotonic()+timeout
+  while not batch.idle:
+   assert time.monotonic()<deadline,'owner drain timeout'
+   batch.step()
+ def batcher(cache=False):
+  return engine.batcher(prefix_cache=cache,prefill_worker=worker,local_prefill=False,remote_prefetch=False)
+ worker.stream_layers=False
+ b=batcher();reference=b.submit(PDRequest('reference',prompts[0],16));drain(b);expected=reference.result()
+ worker.stream_layers=True
+ cache=a.family!='gemma' and a.kv_blocks>1
+ b=batcher(cache);original=b._import_progress;trigger=[]
+ def observe_cancel(r,**kw):
+  before=b.stats['progress_spans'];original(r,**kw)
+  if b.stats['progress_spans']>before and not r.pending.done() and not trigger:
+   trigger.append(dict(spans=b.stats['progress_spans'],admitted=bool(getattr(r,'admitted',False))))
+ b._import_progress=observe_cancel
+ old=b.submit(PDRequest('cancel-partial',prompts[1],16));deadline=time.monotonic()+600
+ while not trigger and not old.done():
+  assert time.monotonic()<deadline;b.step()
+ assert trigger and not trigger[0]['admitted'] and not old.done(),'did not intercept incomplete import'
+ assert b.cancel('cancel-partial');b.step()
+ assert not b.blocks.index,'cancelled incomplete prefix published'
+ try:old.result();raise AssertionError('cancelled request succeeded')
+ except RequestCancelled:pass
+ new=b.submit(PDRequest('reuse',prompts[0],16));drain(b)
+ assert new.result()==expected and not b.poisoned
+ result['cases'].append(dict(case='cancel-reuse',trigger=trigger,tokens_exact=True,expected_tokens=expected,actual_tokens=new.result(),idle=b.idle,prefix_cache=cache))
+ out.write_text(json.dumps(result,indent=2)+'\n')
+ b=batcher(cache);original=b._import_progress;trigger=[]
+ def observe_death(r,**kw):
+  before=b.stats['progress_spans'];original(r,**kw)
+  if b.stats['progress_spans']>before and not r.pending.done() and not trigger:
+   trigger.append(dict(spans=b.stats['progress_spans'],admitted=bool(getattr(r,'admitted',False))))
+   worker._proc.kill()
+ b._import_progress=observe_death
+ failed=b.submit(PDRequest('die-partial',prompts[1],16));drain(b)
+ assert trigger and not trigger[0]['admitted'],'did not intercept incomplete import'
+ assert failed.exception() is not None and not b.poisoned and not b.blocks.index
+ assert worker._proc.wait(timeout=10)==-9 and not worker.ready
+ result['cases'].append(dict(case='producer-death',trigger=trigger,error=repr(failed.exception()),idle=b.idle,poisoned=b.poisoned,prefix_cache=cache))
+ # A new source is required after the deliberate death above.
+ worker.close();worker=make_worker()
+ b=batcher();reference_long=b.submit(PDRequest('reference-long',prompts[0],512));drain(b);expected_long=reference_long.result()
+ b=engine.batcher(prefix_cache=False,prefill_worker=worker,local_prefill=False,remote_prefetch=True)
+ survivor=b.submit(PDRequest('survivor',prompts[0],512));victim=b.submit(PDRequest('prefetched-victim',prompts[1],16))
+ deadline=time.monotonic()+600;trigger=[]
+ while not b.idle:
+  assert time.monotonic()<deadline,'prefetch isolation timeout'
+  b.step();r=b._prefetched
+  if not trigger and r is not None and r.pending is not None and not r.pending.done():
+   progress=getattr(r.pending,'kv_progress',None)
+   if progress is not None and progress.coverage.pending:
+    active=next((x for x in b.decoding if x.request.request_id=='survivor'),None)
+    assert active is not None,'survivor ended before source fault'
+    trigger.append(dict(active_tokens=len(active.tokens),victim_slot=r.slot))
+    assert r.slot<0,'prefetched KV unexpectedly owns active decode lane'
+    worker._proc.kill()
+ assert trigger and victim.exception() is not None and survivor.result()==expected_long and not b.poisoned
+ assert worker._proc.wait(timeout=10)==-9 and not worker.ready
+ result['cases'].append(dict(case='prefetch-source-death',trigger=trigger,expected_tokens=expected_long,actual_tokens=survivor.result(),tokens_exact=True,error=repr(victim.exception()),idle=b.idle,poisoned=b.poisoned))
+ result['stage']='complete'
+except Exception as error:
+ result['stage']='failed';result['error']=repr(error);raise
+finally:
+ if worker is not None:worker.close()
+ engine.close();out.write_text(json.dumps(result,indent=2)+'\n')
diff --git a/opt_prime/examples/pd/http_matrix/evidence265/stream265_long_atom.py b/opt_prime/examples/pd/http_matrix/evidence265/stream265_long_atom.py
new file mode 100644
index 00000000..c07b588c
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/evidence265/stream265_long_atom.py
@@ -0,0 +1,50 @@
+"""Owned hardware check: same RNGD source, ATOM whole/partial import, optional prefetch."""
+import argparse,hashlib,json,os,time
+from pathlib import Path
+import torch
+from transformers import AutoTokenizer
+from opt_prime.accel.pd import PDRequest
+from opt_prime.accel.pd.pd_batching import SubprocessPrefillWorker
+from opt_prime.accel.platforms.atom.engine import AtomRblnEngine
+p=argparse.ArgumentParser()
+for name in ['model','family','label','cache-dir']:p.add_argument('--'+name,required=True)
+p.add_argument('--kv-blocks',type=int,required=True);a=p.parse_args()
+out=Path(f'stream265-{a.label}-long-atom.json')
+if out.exists():raise RuntimeError('refuse overwrite')
+result={'model':a.model,'width':8192,'output_tokens':32,'batch_size':1,'cases':[],'scope':'alternating same-route timing, synthetic2K/8K fixed32output, B1 and two queued requests'}
+result['code_sha256']={name:hashlib.sha256((Path('stream265-code/opt_prime')/name).read_bytes()).hexdigest() for name in ['accel/platforms/atom/batching.py','accel/pd/pd_batching.py','accel/pd/prefill_server.py','accel/pd/layer_progress.py']}
+nodes=list(Path('/dev').glob('rbln[0-9]*'));assert len(nodes)==1
+engine=AtomRblnEngine(devices=[int(nodes[0].name[4:])],max_seq_len=9216,batch_size=1,kv_blocks=a.kv_blocks,cache_dir=a.cache_dir)
+worker=None
+try:
+ engine.start(a.model);c=engine.model.config.get_text_config()
+ base=AutoTokenizer.from_pretrained(a.model,local_files_only=True).encode('Seoul is the capital of South Korea. Count one two three.',add_special_tokens=True)
+ prompts=[tuple((base*((n+len(base)-1)//len(base)))[:n]) for n in (2048,8192)]
+ worker=SubprocessPrefillWorker('./furiosa_prefill_python.sh',['--engine','factory','--engine-factory',f'streaming_factories:{a.family}','--model',a.model,'--width','8192','--chip','0'],slots=1,layers=c.num_hidden_layers,kv_heads=c.num_key_value_heads,head_dim=getattr(c,'head_dim',None) or c.hidden_size//c.num_attention_heads,max_context=8192,env=dict(os.environ),stream_layers=True)
+ reference=None
+ for index,(stream,prefetch) in enumerate([(s,p) for p in (False,True) for s in (False,True,False,True,True,False,False,True)]):
+  with worker._lock:assert not worker._futures
+  worker.stream_layers=stream
+  b=engine.batcher(prefix_cache=False,prefill_worker=worker,local_prefill=False,remote_prefetch=prefetch)
+  traces=[];original=b._import_progress
+  def observed(r,**kw):
+   before=b.stats['progress_spans'];active=len(b.decoding);done=r.pending.done()
+   original(r,**kw)
+   if b.stats['progress_spans']>before:traces.append(dict(spans=b.stats['progress_spans']-before,active_decode=active,source_done_before=done,source_done_after=r.pending.done()))
+  b._import_progress=observed
+  arrivals=[[],[]];start=time.perf_counter()
+  f=[b.submit(PDRequest(f'{index}-{i}',ids,32),on_token=lambda token,i=i:arrivals[i].append(time.perf_counter()-start)) for i,ids in enumerate(prompts)]
+  b.run_until_idle();tokens=[v.result(timeout=1) for v in f]
+  if reference is None:reference=tokens
+  elapsed=time.perf_counter()-start
+  assert all(len(x)==32 for x in arrivals)
+  row=dict(index=index,warmup=index%8<2,elapsed_s=elapsed,arrivals_s=arrivals,ttft_s=[x[0] for x in arrivals],stream=stream,prefetch=prefetch,tokens=tokens,tokens_exact=tokens==reference,progress_spans=b.stats['progress_spans'],imports=traces,idle=b.idle)
+  result['cases'].append(row);out.write_text(json.dumps(result,indent=2)+'\n');print(json.dumps(row),flush=True)
+  assert row['tokens_exact'] and b.idle and all(e['active_decode']==0 for e in traces)
+  assert (row['progress_spans']>0)==stream
+ result['stage']='complete'
+except Exception as error:
+ result['stage']='failed';result['error']=repr(error);raise
+finally:
+ if worker is not None:worker.close()
+ engine.close();out.write_text(json.dumps(result,indent=2)+'\n')
diff --git a/opt_prime/examples/pd/http_matrix/experiments/2026-10-24-atom-prefetch253.md b/opt_prime/examples/pd/http_matrix/experiments/2026-10-24-atom-prefetch253.md
new file mode 100644
index 00000000..a541a6f0
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/experiments/2026-10-24-atom-prefetch253.md
@@ -0,0 +1,53 @@
+# B1 ATOM next-request prefill pipeline hypothesis (#253)
+
+- **Date**: 2026-10-24
+- **Task**: B1 ATOM prefetch (aicomp #253)
+- **Host / device**: Analysis of existing RNGD+ATOM host data; no new hardware run
+- **Commit**: aicomp273952a; own worktree perf/253-atom-prefetch; implementation and regression tests added after RED
+
+## Question
+
+Can a bounded next-request prefill overlap current B1 decode without reusing a still-written shared source buffer?
+
+## Setup
+
+Source inspection: AtomBatcher._pd_step submits only while a decode lane is free. A B1 next-request reservation can reuse the existing single shared slot after the current request has been imported; an extra slab is not inherently required. Hold the reservation until import or acknowledged producer completion after cancellation. Admit decoder blocks only on the owner thread after the lane is free. Early prefill uses send_from=0 because the final cache state is not yet known; prefix hits can still be used during late admission but full transfer is required. Keep opt-in and remote-only for the first B1 implementation.
+
+## Command
+
+```bash
+# RED first, then focused and full CPU validation after the GPU queue completed:
+cd /home/gspark/workspace/aicomp-atom-prefetch-253
+CUDA_VISIBLE_DEVICES='' PYTHONPATH=opt_prime /home/gspark/venvs/pt213/bin/python -m pytest opt_prime/tests/test_atom_batching.py -k b1_prefetch -xq
+```
+
+## Results
+
+| config | metric | value | vs. baseline |
+|---|---|---:|---:|
+| scheduler regressions | RED | missing remote_prefetch option | reproduced before implementation |
+| focused AtomBatcher + public factory tests | tests | 35 passed, 4.39s | CPU fake SDK with real tiny LM/KV |
+| full opt_prime suite | tests | 511 passed,34 skipped,253 subtests,150.28s | one existing flex-attention warning |
+| Qwen0.6B | first SSE / rest | 2.641s / 0.344s | heuristic pipeline ratio1.13 |
+| Qwen4B | first SSE / rest | 4.540s / 1.310s | heuristic pipeline ratio1.29 |
+| KONI8B | first SSE / rest | 4.553s / 2.223s | heuristic pipeline ratio1.49 |
+
+Analysis: analysis/serving-matrix-201/atom253-pipeline-hypothesis.json, derived from existing #248 c1/input8192/output32 medians.
+
+## Conclusion
+
+The opt-in implementation and tests check second prefill starts during first decode, exact real-model outputs, reservation bound, cancellation without falsely releasing the writer, producer failure, and safe subsequent request reuse. Heuristic ratios assume perfect overlap and ignore import/resource contention; first SSE is not a pure producer stage, so these are not throughput predictions. Even ideal request pipelining is unlikely to close the Qwen0.6B measured gap by itself. Larger models offer more decode work to overlap. RED was observed before production changes. Real four-model RNGD-to-ATOM validation remains pending.
+
+## Implemented behavior and verification
+
+`AtomBatcher(remote_prefetch=True)` / `serve_batched.py --pd-prefetch-next` is opt-in, single-lane ATOM and remote-only. One queued request may start on the existing source slot while the current request decodes. Decoder cache admission/import remains owner-controlled. The prefetched result stays reserved until import; cancelling a client does not cancel its producer future or falsely release a still-written slot. The owner keeps draining an orphaned producer even when all clients have left. Unsupported routes are rejected before model loading. Default behavior is unchanged. No transport protocol changes or extra slab slot are required.
+
+Commands after RED:
+```bash
+cd /home/gspark/workspace/aicomp-atom-prefetch-253
+CUDA_VISIBLE_DEVICES='' OMP_NUM_THREADS=2 MKL_NUM_THREADS=2 PYTHONPATH=opt_prime /home/gspark/venvs/pt213/bin/python -m pytest opt_prime/tests/test_atom_batching.py opt_prime/tests/test_serving_prefill_cache.py -q
+cd opt_prime
+CUDA_VISIBLE_DEVICES='' OMP_NUM_THREADS=2 MKL_NUM_THREADS=2 PYTHONPATH=. /home/gspark/venvs/pt213/bin/python -m pytest -q
+```
+
+The full run includes the added cancellation assertion that an orphaned producer keeps the batcher non-idle, followed by safe request-ID and source-slot reuse. Tests use a controlled future boundary to expose ordering, real TorchPrefillEngine KV, a fake Rebellions SDK import, and independent greedy outputs. Hardware throughput and a real producer/decode timeline remain unmeasured; no speedup claim. Logs: atom253-{red,cli-red,focused,full}.log in analysis/serving-matrix-201.
diff --git a/opt_prime/examples/pd/http_matrix/experiments/2026-10-24-dense-hybrid258.md b/opt_prime/examples/pd/http_matrix/experiments/2026-10-24-dense-hybrid258.md
new file mode 100644
index 00000000..c1803cab
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/experiments/2026-10-24-dense-hybrid258.md
@@ -0,0 +1,30 @@
+# Dense subprocess KV import into hybrid GPU pools
+
+- **Date**: 2026-10-24
+- **Task**: aicomp #258; four-model serving matrix #201
+- **Host / device**: GPU+RNGD host; CPU regression tests, Torch2.13.0+cu130
+- **Commit**: base273952a plus uncommitted fix/258-dense-hybrid-kv
+
+## Question
+Why does live Gemma RNGD-to-GPU fail on its first request despite grouped Torch producer tests passing?
+
+## Setup
+Actual runtime3e16c33, Gemma3-1B26layers,22sliding/4global, BF16, fourGPUslots/context9216/page64/cacheoff, RNGDwidth8192. First128-token request sends dense[26,1,128,256] KV; write_span incorrectly treats it as first group[22,1,128,256] and owner exits. Original local201-gemma-matrix-a.json and server log retained. Zero completed performance batches; deferred fault queue correctly stopped. Remote ATOM run is independent and unchanged.
+
+Fix maps dense model-layer spans into receiver groups, preserves grouped and single-group/recurrent formats, and validates all group shapes/counts before writes. Protocol unchanged.
+
+## Command
+From aicomp-dense-hybrid-258/opt_prime:
+```bash
+OMP_NUM_THREADS=2 MKL_NUM_THREADS=2 PYTHONPATH=. /home/gspark/venvs/pt213/bin/python -m pytest -q tests/test_pd_dense_hybrid.py
+OMP_NUM_THREADS=2 MKL_NUM_THREADS=2 PYTHONPATH=. /home/gspark/venvs/pt213/bin/python -m pytest -q tests/test_pd_dense_hybrid.py tests/test_pd_hybrid.py tests/test_pd_batching_disagg.py
+CUDA_VISIBLE_DEVICES='' OMP_NUM_THREADS=2 MKL_NUM_THREADS=2 PYTHONPATH=. /home/gspark/venvs/pt213/bin/python -m pytest -q
+```
+
+## Results
+Initial shell test-file path was incorrect; no tests ran. Corrected path before RED run.
+RED:3tests fail: dense mapping shape mismatch, missing group silently accepted, malformed later-group write not validated atomically. First fix run28pass/2fail exposed single-group recurrent compatibility regression; preserved existing single-group tensor semantics. Added actual tiny Gemma dense producer->PDBatcher comparison against independent full-HF greedy beyond sliding window.
+Focused rerun:31passed,1existing flex-attention warning,29.86s. Logs /tmp/dense258-red.log, /tmp/dense258-focused.log, /tmp/dense258-focused-b.log. Full regression:505passed,34skipped,253subtests passed,1existing warning,151.69s; log /tmp/dense258-full.log.
+
+## Conclusion
+Cause reproduced and focused correction passes. Hardware rerun and publication remain; no claim that numerical parity or live Gemma serving is fixed yet.
diff --git a/opt_prime/examples/pd/http_matrix/experiments/2026-10-24-rngd-layer-stream265.md b/opt_prime/examples/pd/http_matrix/experiments/2026-10-24-rngd-layer-stream265.md
new file mode 100644
index 00000000..8c87b9d4
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/experiments/2026-10-24-rngd-layer-stream265.md
@@ -0,0 +1,245 @@
+# RNGD layer-progress import toward within-request KV overlap
+
+- **Date**: 2026-10-24
+- **Task**: Layer-progress RNGD transfer (aicomp #265, original #47)
+- **Host / device**: local unit/regression tests; no new streaming hardware experiment yet
+- **Commit**: isolated perf/265-rngd-stream, base098169b plus dense-hybrid fix cherry-picked as8903b73; uncommitted implementation
+
+## Question
+
+Can completed RNGD layer groups be imported before final prefill/logits without publishing incomplete state or corrupting another request? GPU chunk-copy#254 and ATOM next-request prefetch#255 do not implement this source-to-decoder connection.
+
+## Setup
+
+The public research FullPrefillEngine.prefill_stream has per-group host output, but the current subprocess worker sends only whole-request completion. Work is isolated from the live #263 queue. First unit adds a thread-safe coverage/notification validator and partial dense model-layer import into reserved PagedKV pages. It does not yet alter worker messages, GPU/ATOM admission, or source adapters. No overlap or speedup claim.
+
+Coverage validates integer geometry, source span bounds, overlap across every affected layer before any state mutation, complete layer/token coverage on finish, and no messages after closure. Draining notifications is separate from source completion; neither is a destination transfer fence. Cache import validates every affected hybrid group before writing and leaves lengths/cache publication unchanged. It rejects unreserved pages and overwrites of an admitted/shared prefix.
+
+## Command
+
+```bash
+cd /home/gspark/workspace/aicomp-rngd-stream-265/opt_prime
+OMP_NUM_THREADS=4 MKL_NUM_THREADS=4 TORCH_DEVICE_BACKEND_AUTOLOAD=0 \
+  /home/gspark/venvs/pt213/bin/python -m pytest tests/test_pd_layer_progress.py tests/test_pd_dense_hybrid.py -q
+# Full suite with the same environment:
+OMP_NUM_THREADS=4 MKL_NUM_THREADS=4 TORCH_DEVICE_BACKEND_AUTOLOAD=0 \
+  /home/gspark/venvs/pt213/bin/python -m pytest -q
+```
+
+## Results
+
+- Coverage tests first11failed for missing implementation, then11passed.
+- Partial receiver tests6failed before implementation (controlled removal in correct package working directory:6failed/11passed), then combined21passed/1existing flex-attention warning in5.18s.
+- Additional admitted-prefix regression:1failed/17passed before guard; final focused22passed/1existing warning in4.93s.
+- One initial focused invocation from the repository parent failed collection (`opt_prime.accel` unavailable with backend autoload disabled). Correct package-directory command above resolves it; failure is not an inference result.
+- Initial full regression before the last admitted-prefix guard:555passed/15skipped/253subtests in160.70s. Final full run after guard:556passed/15skipped/253subtests in158.09s.13existing pinned-memory deprecation/flex-attention warnings retained.
+
+Raw logs: /tmp/stream265-red.log, /tmp/stream265-import-red-controlled.log, /tmp/stream265-prefix-red.log, /tmp/stream265-focused-final.log, /tmp/stream265-full.log and /tmp/stream265-full-final.log.
+
+## Conclusion
+
+Only validation/import primitives are implemented. Still required: opt-in progress wire messages, source adapters, owner drain/commit/failure integration, ATOM cache-ownership handling, actual overlap traces, same-route exactness and long-context/large-KV performance. Existing #47 export-only0.1–4GB curve showed chunking slower but reduced incremental staging to~67MB; it does not prove RNGD streaming improvement. Preserve that negative control and measure the new path when connected.
+
+## Opt-in receiver and GPU owner integration
+
+Receiver now negotiates `kv_progress: 1`, validates layer/token coverage and exact final suffix length, exposes borrowed shared-memory slices, and wakes the scheduling owner on progress. The GPU owner imports partial spans without admitting decode or publishing prefix state until complete coverage; CUDA writes are fenced before cancellation can reuse pages. Default whole-result protocol remains unchanged. Real source adapters and ATOM owner integration remain pending: these are controlled-producer tests, not a hardware streaming speedup measurement.
+
+Validation uses the same environment and package working directory as above. Additional commands:
+
+```bash
+OMP_NUM_THREADS=4 MKL_NUM_THREADS=4 TORCH_DEVICE_BACKEND_AUTOLOAD=0 \
+  /home/gspark/venvs/pt213/bin/python -m pytest tests/test_subprocess_layer_progress.py tests/test_pd_stream_admission.py -q
+```
+
+- Real subprocess/SHM protocol tests cover partial visibility, invalid/overlapping/holey coverage, producer exit, cancellation and immediate same-slot reuse, cached suffix offsets, and capability rejection.
+- Real tiny Gemma decoder tests compare controlled streaming and whole-result producers on CPU and CUDA, including partial non-admission, failures, cancellation and reuse.
+- CUDA page8 fixture failed in Triton (`K >= 16`); ordinary whole-result control reproduced the compiler error. That control's outer exception assertion also failed. CUDA tests now use page16; CPU remains page8. No production kernel/default changed.
+- Capacity regression first failed: invalid streaming capacity raised synchronously. It now completes the request Future with an error while preserving producer readiness and successful subsequent requests.
+- Boolean response-ID regression first failed: Python `True == 1` routed malformed progress to another in-flight request. Exact integer type validation now rejects it before lookup.
+- Latest focused run:18 passed,15 warnings,9.44s. Pre-ID-guard full suite:573 passed,15 skipped,253 subtests,27 warnings,165.62s. Final post-guard full suite recorded below when complete.
+
+Evidence logs retain the failing controls and successful reruns. No claim of source-compute/transfer overlap, hardware speedup, ATOM integration, or universal numerical parity.
+
+Final post-ID-guard full regression: **574 passed, 15 skipped, 253 subtests passed**,27 warnings in164.46s. Exit0; `/tmp/stream265-final-regression.log`. Source adapters and ATOM streaming remain unimplemented.
+
+## Source protocol and rngd-full adapter
+
+The production prefill server now advertises capability only for engines with `prefill_stream`. `stream_result` validates the reservation, geometry and nonoverlapping coverage, clips source spans to a cached suffix, copies BF16 CPU data to SHM, then sends notifications. Completion requires exact coverage. The existing KONI/Llama rngd-full adapter exports each completed group before advancing the next group. Whole-prefill behavior is retained for the A/B control.
+
+The serving entry point accepts `--pd-prefill-stream-layers` only with a subprocess producer and Torch decoder; ATOM/RNGD decoder owners are rejected until integrated. Producer capability negotiation rejects older workers. This does not yet connect the separate four-model long-context factory adapters.
+
+- Initial source regressions:5 failures (missing implementation); source plus pipe tests15 passed in2.24s.
+- Production server/receiver pipe integration initially failed because the executable fixture lacked the package on sys.path. Corrected fixture;6 source tests passed in2.73s. The engine alone is stubbed: production server request handling, SHM writes, progress notifications and receiver are exercised.
+- CLI regressions:5 failures before wiring; combined source/server/CLI/receiver/CPU-CUDA owner36 passed,15 warnings in10.57s.
+- Added fully cached suffix and invalid-reservation tests. Full suite and RNGD hardware source parity recorded separately below.
+
+Reproduce focused verification from the package directory with the environment above:
+
+```bash
+python -m pytest tests/test_prefill_source_progress.py tests/test_serving_prefill_cache.py tests/test_subprocess_layer_progress.py tests/test_pd_stream_admission.py -q
+```
+
+The source adapter performs blocking CPU export per group; it permits the receiver to import while later groups run. Neither this sequencing nor CPU tests establish actual kernel/copy overlap or end-to-end performance improvement.
+
+Runtime compatibility audit found that the installed rngd-pd snapshot exposes `_next_token`/`lmh` but not the `_logits` method expected by the existing server. Both whole and streaming adapters now use `_last_logits`: prefer `_logits` when available, otherwise invoke the same existing on-card head and retain all vocabulary logits rather than only argmax. A regression first failed for the missing helper, then all13 source tests passed in2.93s. Numerical parity on actual RNGD remains to be checked; the CPU fixture alone cannot establish it.
+
+Hardware source parity setup: local RNGD chip0, KONI-8B, width128, group2, same full model weights as evidence263. Torch2.10.0+cpu, furiosa-torch2026.4.0, transformers5.8.1. Decoder is not run in this check. Loaded rngd-pd prefill_full.py SHA256 `0ac864695d8c2cedc5fb2ddadbc3d3d782bb21909a83f2b4d82944f32940e8ea`.
+
+Exact launch (source paths are local runtime inputs):
+```bash
+env OMP_NUM_THREADS=4 MKL_NUM_THREADS=4 TORCH_DEVICE_BACKEND_AUTOLOAD=0 \
+ PYTHONPATH=/home/gspark/workspace/aicomp-rngd-stream-265/opt_prime:/tmp/rngd-gpu201-src \
+ /tmp/codex-rngd-gpu-201/bin/python /tmp/stream265-source-hardware.py \
+ > /tmp/stream265-source-hardware.log 2>&1
+```
+
+Full regression collected before the final legacy-head helper:591 passed,15 skipped,253 subtests,27 warnings in165.79s. After the helper, all13 source tests passed in2.93s; the full run does not cover that final added test.
+
+### Actual RNGD attempt: failed before parity
+
+The process exited1. The group2 width128 graph compiled in74s, but the legacy FullPrefillEngine constructor failed while building its on-card LM head: rngd_pd.kernels.dsl_b1 imports missing `furiosa.native_llm_common`. Neither whole-prefill nor streaming inference ran; no KV/logit comparison or speedup result exists. The real runtime failure is retained in stream265-source-hardware.log. Next integration must use the already validated four-model long-context factory adapters, including KONI's partitioned graph with host FP32 norm/head, rather than treating this legacy path as hardware-validated. The `_last_logits` fallback is CPU-tested only and does not solve the constructor dependency.
+
+## Validated four-model factory connection
+
+Added an explicit trusted `--engine factory --engine-factory module:callable` loader using the same FP32 safetensors/config inputs as the prior research worker. Shared-memory geometry and width are checked before constructing the engine. `examples/pd/streaming_factories.py` wraps the previously measured Qwen, Gemma and KONI producers: the whole-result method is unchanged, and streaming preserves padding, layer order, per-layer attention inputs and each family's final head arithmetic. KONI uses its existing partitioned graph and host FP32 norm/head, avoiding the legacy native head dependency.
+
+Dependencies: aicomp `04dab7cb6961103f9404c99e9fcbcaadac14b098` (`feat/234-live-model-pd`) serving_matrix and qwen3_rngd example directories, plus the measured rngd_pd runtime snapshot. Local qwen prefill/spec copies have identical SHA256 to that committed example. These examples are explicit experimental dependencies, not yet bundled into a production package.
+
+Test-first results: factory loader6failed before implementation→6passed. Adapter tests6failed before implementation; first run2failed/10passed (Qwen fixture used a scalar instead of layer-parameter tuple; Gemma imported helpers before empty-input validation), corrected. Source/adapter/factory tests25passed. Production server/factory IPC added for both whole and streaming modes; combined27passed in6.47s. Hardware results below are separate.
+
+Hardware source matrix: each model gets its tokenizer's short prompt plus an input exactly filling its compiled width. For each, compare whole output against streamed SHM with send_from=0, half the prompt and the entire prompt (empty suffix). Assert exact BF16 K/V and exact logits, record notification timestamps and first-token IDs. Width128 for Qwen0.6/Qwen4/Gemma, width512 for KONI (minimum validated partition width). These widths do not establish long-context performance.
+
+Exact driver: `/tmp/stream265-source-queue.py`, invoking `/tmp/stream265-factory-hardware.py` in the isolated Furiosa2026.4 environment. Files refuse overwriting previous results. Same-route RNGD→GPU follow-up uses `/tmp/stream265-gpu-queue.py` and `/tmp/stream265-gpu-hardware.py`, eight output tokens, two concurrent requests, page64, caches off; whole versus streamed producer is the only mode change.
+
+Hardware source results: **24/24 exact K/V and logits** across all four models (6 conditions each); max absolute logit error0 throughout. All nonempty suffixes emitted their first progress notification before final logits returned. All four child processes exited0. This proves source/SHM parity at tested widths128/512 only, not long-context decoder parity, pipeline overlap or speedup. Artifacts: stream265-{qwen06b,qwen4b,gemma,koni}-source.{json,log}, stream265-source-queue.json and dependency hashes. CPU times recorded in callbacks are not device-overlap measurements.
+
+First real GPU integration failed before decoding: server passed a tuple to the Qwen factory's whole-prefill method, which concatenates a list for padding. Source-only comparisons passed because their driver passed lists. The production factory pipe regression reproduced it (whole mode failed, streaming mode passed); non-Torch server dispatch now passes a list consistently. Final combined tests27passed in4.82s. Original GPU failure JSON/log and queue result are preserved with `-failed-list` names; a fresh GPU queue follows. This demonstrates why direct producer parity does not substitute for actual server/decoder validation.
+
+Actual RNGD→GPU integration completed: **8/8 output-token pairs exact** (two concurrently submitted requests per model, fixed8 output tokens). Whole versus streamed source modes both drained successfully. Streaming span imports: Qwen0.6=56, Qwen4=72, Gemma=52, KONI=64; whole mode0 for every model. Input lengths were short and128 (Qwen/Gemma), short and512 (KONI). This is same-route transport/scheduler parity, not nativeGPU equivalence or answer quality. Requests deliberately use fixed8 tokens without model EOS stop IDs; Gemma's short output contains turn markers, so this test must not be interpreted as normal-EOS quality evaluation. No speedup claim from these single runs. All owned subprocesses exited and GPU compute-process listing was empty before the full regression run started.
+
+Final factory integration regression: **606 passed,15 skipped,253 subtests passed**,27 warnings in166.21s, exit0. Raw log stream265-factory-full.log. This includes the final tuple/list correction, real factory pipe tests and all existing source/owner tests.
+
+Next performance matrix uses one capability-negotiated producer per model, changes request mode only after its future map and decoder are drained, and alternates modes after two warmup runs. Inputs2048/8192, compiled width8192, output32, concurrency2, page64, caches off. Three measured batches/mode. Host import trace records whether source completion was observed before/after destination import; it is not a device-kernel overlap trace. Drivers prepared as stream265-long-gpu.py and stream265-long-queue.py; results must be assessed separately from the completed short-width parity results.
+
+## Long-context alternating same-engine results (runtime171e8c4)
+
+Command: `python3 /tmp/stream265-long-queue.py > /tmp/stream265-long-queue.log 2>&1`. Exact queue and driver are retained in evidence265. Each model uses one capability-negotiated source process; changing `stream_layers` requires an empty source future map and drained decoder. Source width8192, decoder context8320/page64, mixed2048/8192-token synthetic repeated-text inputs submitted together, fixed32 output tokens without EOS stopping, no prefix cache. First two runs (whole/stream) are warmups; measured order whole/stream/stream/whole/whole/stream gives3 batches/mode. Engine startup and GPU model loading are excluded. Hardware: local RNGD chip0 and CUDA GPU0 (A10080GB); no concurrent benchmark/test jobs.
+
+| Model | Whole median batch completion(s) | Stream median(s) | Reduction | TTFT reduction, first2048 / second8192 request |
+|---|---:|---:|---:|---:|
+| Qwen3-0.6B |3.637588|2.613433|28.15%|34.02% /33.58%|
+| Qwen3-4B |7.305905|5.689632|22.12%|25.55% /25.98%|
+| Gemma3-1B |3.936250|3.767887|4.28%|4.45% /4.22%|
+| KONI-8B |7.598914|5.855674|22.94%|18.54% /25.32%|
+
+All32 batches including warmups drained; all64 generated sequences (32tokens each) match their model's initial whole-result reference. All four processes exited0. TTFT includes queueing in this two-request workload, not isolated latency for a single input length. The auditor independently recomputes medians from raw records and checks warmup placement, token sequences, token arrival times and trace counts.
+
+Across measured streaming batches, GPU imports completed before the source Future completed for all168/216/156/192 spans respectively (732 total). This verifies early destination import in the actual pipeline. It is a host-observed completion trace, not a device-kernel/DMA overlap trace. It does not isolate the benefit of earlier import from avoiding whole-KV host assembly; a deferred-notification control would separate those effects.
+
+Limits: three samples/mode, one host, fixed compiled width8192 and concurrency2. Gemma improvement is small. This is fixed-output transport/scheduler parity, not semantic accuracy, normal-EOS behavior or native-vendor performance. Long-context byte-for-byte source KV comparison and streaming hardware fault tests remain to be extended; the direct source KV/logit exactness matrix above used widths128/512. ATOM partial owner integration remains pending. Whole transfer remains the default.
+
+Artifacts: stream265-*-long-gpu.{json,log}, stream265-long-queue.json, stream265-long-summary.json, audit_stream265_long.py, stream265-long-gpu.py and stream265-long-queue.py. After completion, GPU compute-process listing and RNGD device-user checks were empty.
+
+## Long-context KV exactness
+
+`python3 /tmp/stream265-long-source-queue.py > /tmp/stream265-long-source-queue.log 2>&1` completed all four models with exit0. Driver stream265-long-source.py uses compiled width8192 and prompts2048/8192, each with send_from=0/half/full. All24 conditions have exact BF16 K/V and exact logits, max absolute logit error0. This closes the long byte-level source-parity gap for these deterministic synthetic inputs. Artifacts: stream265-*-long-source.{json,log} and stream265-long-source-queue.json. Source implementation matches b28408d; independent source hashes are in each JSON.
+
+## ATOM partial ownership integration
+
+Cherry-picked the existing B1 prefetch prerequisite3e16c33 (#255) as2e69570. Append-only conflicts in STATUS and serving tests were resolved preserving both sets of entries/tests. An initial relative-path mistake left conflict markers for a failed collection; corrected invocation43passed. No production inference result is associated with that invocation error.
+
+Added per-layer block import using existing BF16→DLFloat conversion and SDK KV writes. Selected-layer geometry is checked before writes; hybrid local state maps to its lane and retains the prompt tail, while global state maps to its reserved block. The owner wakes on progress, imports only into reserved lanes, waits for complete coverage before admission, and leaves prefetch data untouched until its destination lane is available. Multi-lane hybrid caches defer imports until final admission because idle local cache slots can still be written by the decode graph. The actual B1 path supports early import.
+
+Found and reproduced an existing prefix publication bug: remote reservation set `admitted` before prefill succeeded, allowing a failed/incomplete prompt block to enter the prefix index. Local and remote paths now mark it only after successful prefill/import. Cancellation during partial import cannot publish an incomplete prefix; cancellation after valid admission may still retain its complete prefix.
+
+Test-first evidence:4failed/1passed before partial support; initial focused60passed. Extended identical-KV whole/stream decode, cancellation/reuse, SDK-write poisoning and hybrid lane/block mapping passed48tests. Multi-lane hybrid deferral regression failed before the guard; final49tests passed. ATOM CLI acceptance regression1failed/4passed before wiring, final26related tests passed. Full regression:626passed,15skipped,253subtests passed,27warnings in168.94s.
+
+Actual ATOM check uses a separately allocated owned Pod, one RNGD and one ATOM, existing context9216/B1 compiled artifacts, sourcewidth512, short/512 inputs queued together and fixed8 output tokens. One live producer is reused across (whole, no-prefetch), (stream, no-prefetch), (stream,prefetch), (whole,prefetch), switching only when drained. Each import records active decode count; it must be zero for B1. New source package is isolated in the owned scratch directory; runtime base2e69570 plus candidate Atom/CLI changes, exact code SHA256 recorded in each result. Qwen0.6 completed all4modes token-exact; remaining models running at this checkpoint. No ATOM speedup or long-context claim yet.
+
+Actual four-model ATOM result: all16 mode batches drained and all32 generated sequences exactly match the same-route whole/no-prefetch reference. Whole/stream × prefetch off/on, short/512 inputs, fixed8 outputs. Stream span totals across both streaming modes: Qwen0.6=112, Qwen4=144, Gemma=104, KONI=128; respectively44/36/99/96 spans completed host import before the source Future was done. All imports observed zero active decode requests, preserving B1 cache ownership. All four child/copy exits0. The owned Pod was deleted by the queue, and an independent get --ignore-not-found returned empty with exit0. These measurements establish integration parity and early host-observed import, not long-context ATOM speedup or device DMA overlap.
+
+Reproduction drivers: stream265_atom.py, run-stream265-atom.sh, stream265-long-source.py. ATOM runtime is2e69570 plus candidate changes identified by SHA256 in each JSON. Full test log stream265-atom-full.log:626passed,15skipped,253subtests. Remaining: long ATOM timing, streaming hardware cancellation/producer failure, and device-overlap attribution.
+
+## Long ATOM alternating timing extension
+
+Runtime33d6cb0, isolated package hashes recorded per result. Owned Pod stream265long: one RNGD+one ATOM, B1 compiled context9216, sourcewidth8192, inputs2048/8192 queued together, fixed32 outputs, cache off. For each prefetch off/on stratum, modes whole/stream/whole/stream/stream/whole/whole/stream; first two warmups, three measured batches per mode. One live producer per model, switch only while drained; output tokens compared with initial same-route whole reference. Drivers stream265_long_atom.py and run-stream265-long-atom.sh, orchestration `python3 /tmp/stream265-long-atom-queue.py`. No performance result yet. Initial script upload used an SSH-config default port and failed before any benchmark; explicit accelerator-host port22 upload succeeded.
+
+Follow-up checkpoint: existing long ATOM queue session64332 remained live; owned Pod phase Running and source process CPU time increased during initialization. No completed long ATOM batch yet; do not restart from observation timeouts. Prepared stream265_fault_atom.py for actual early-import cancellation/reuse and producer death, with explicit assertions that interception occurs before source completion/admission. Driver is syntax-checked only and has not run; it is not hardware evidence. Rechecked all24 long source exactness conditions and16 short ATOM mode batches from raw JSON to update ACCEPTANCE.md/stream265-acceptance.json, preserving earlier native-parity/content failures.
+
+## Actual GPU streaming fault extension
+
+Local GPU compute-process listing and RNGD device-user listing were empty before launch. Independent of the remote ATOM timing host. Runtime33d6cb0, sourcewidth8192, source chip0/GPU0, decoder two slots/context9216/page64/cacheoff. `python3 /tmp/stream265-fault-queue.py > /tmp/stream265-fault-queue.log 2>&1`, session27276, invokes stream265-fault-gpu.py for each model. Test intercepts an incomplete import, cancels it and compares a replacement request against same-route whole-source tokens. Then it compares an active512-token decode against its same-route reference while killing the source after partial import of a second8K prompt. Assertions require an actual partial-before-completion interception and an active survivor at the kill point. All failures retained; no result yet.
+
+GPU fault driver first attempt failed in test instrumentation: after cancellation/reuse token comparison, it read ATOM-only `batch.poisoned` on PDBatcher, raising AttributeError. Qwen0.6 raw failure retained. Stopped only owned queue PID and its Qwen4 child; child exited through finally with KeyboardInterrupt, worker/device cleanup verified by empty GPU/RNGD users. Removed ATOM-only state assertions (GPU has no such quarantine field). Fresh driver/queue v2 write distinct `*-fault-gpu-v2.*` paths; no previous evidence overwritten. This is a harness fix, not a production engine fix.
+
+Completed first long ATOM model Qwen3-0.6B:16batches drained and all32 sequences exact. Three measured batches/mode: prefetch-off whole5.125748s→stream3.297927s (35.66% reduction); prefetch-on4.877637s→2.963296s (39.25%). Other models still pending; no four-model ATOM performance claim yet.
+
+GPU fault v2 checkpoint: Qwen0.6,Qwen4,Gemma each exited0, completed both cancellation/reuse and partial-producer-death tests, and drained. At producer death, the surviving512-token request had emitted5/6/4 tokens respectively; runtime assertions verified all512 tokens match the no-fault same-route reference. KONI remains live in source initialization; no completed claim for it. audit_stream265_faults.py requires allfour successful process exits and active_tokens strictly between0 and512; synthetic fixture accepted and a finished-survivor counterexample rejected. ATOM long Qwen4 source remains live; next ATOM fault scripts uploaded to owned scratch but no fault Pod allocated while timing continues.
+
+Final GPU fault v2 result: allfour child exits0; auditor confirms allfour complete cancellation/reuse and partial-producer-death cases. Surviving512-token decode had5/6/4/10 tokens at source death (Qwen0.6/Qwen4/Gemma/KONI), and driver asserts its entire output equals the same-route no-fault reference. Failed request raises source-exit error; owners drain. Prefix caches off. This proves tested source/owner failure handling, not device-failure recovery, prefix restoration or native numerical equivalence. Raw JSON stores assertion outcomes/triggers, not full512-token sequences; the exact executed driver contains the equality assertions. After queue exit, local GPU compute listing and RNGD device-user listing were empty. Independent auditor rerun reproduces stream265-fault-summary.json byte-for-byte.
+
+ATOM fault preparation extended (not run): after partial-import cancellation/reuse and producer death, start a fresh source and compare active512-token decode against its same-route reference while killing a prefetched8K request after progress appears. Assert victim has no destination lane yet and survivor is active. Store complete expected/actual token arrays for reuse and survivor cases. Updated isolated driver uploaded; running timing script/package is unchanged. Working notes refreshed to remove stale pre-implementation status and obsolete owned-Pod pointers.
+
+Qwen4 long ATOM source startup diagnostic: read-only inspection inside the owned Pod reports source RSS38245120KiB, swap0,29threads; total Pod memory50786693120bytes. OOM/oom_kill counts0, memory pressure avg10/60/300 all0. CPU usage continues increasing, but this alone does not establish successful initialization; no Qwen4 timed batch yet. Existing startup timeout remains1800s, queue unchanged. Host-side /proc/io was denied; used only the owned container's /proc instead. Diagnostic snapshot stream265-qwen4b-init-diagnostic.json. No profiler attached and no live runtime/code changed.
+
+Added two controlled-producer regressions for partial prefetch error/cancel while another request decodes: no partial destination import into the active lane, cancelled source reservation retained until its writer finishes, same BF16-source reference output, owner drains without poisoning. Focused command `OMP_NUM_THREADS=4 MKL_NUM_THREADS=4 TORCH_DEVICE_BACKEND_AUTOLOAD=0 /home/gspark/venvs/pt213/bin/python -m pytest -q tests/test_atom_stream_progress.py tests/test_atom_batching.py` from worktree opt_prime:39passed in5.00s, log stream265-prefetch-fault-final.log. An initial append used the wrong relative path and failed before modifying tests; that initial invocation ran only pre-existing tests and is not new-test evidence. No production code changed. Qwen4 long ATOM now emitted actual timed batches after startup; measurements still in progress.
+
+Qwen3-4B long ATOM completed with child/copy exits0, all16 batches/32sequences same-route exact and drained; every import observes zero active B1 decode. Three measured batches/mode: prefetch-off whole10.826179s→stream8.524409s (21.26% reduction), prefetch-on9.738162s→7.507991s (22.90%). Gemma starts next; KONI queued. Startup is excluded from these timings. These results do not establish native-vendor equivalence or full four-model ATOM performance yet.
+
+Prepared audit_stream265_atom_faults.py: requires four successful child/copy exits, all three fault cases per model, actual partial-progress triggers, idle/unpoisoned owners, active survivor at source death and victim without a reserved lane, plus exact retained16/512-token arrays. Self-check accepts a complete synthetic fixture and rejects a single altered survivor token. This is auditor validation only; real ATOM fault runs remain pending. Existing long timing session64332 remains live, Gemma source initialization observed at2m27s with CPU time11m54s; no restart.
+
+## GPU notification-timing control
+
+To distinguish earlier destination import from avoiding whole-KV assembly, test-only stream265_control.py retains the production stream_result function and all per-layer compute/SHM writes; deferred mode buffers only progress descriptors until final source logits return, then publishes them in original order. Early mode delegates unmodified. Whole mode remains the existing control. No production package changes. A pure self-check verifies copy/logit/notification order and return-value preservation. A source launcher chooses mode from a fixed12-batch plan using sequential request IDs (two requests/batch); no wire API changes.
+
+Four models, width8192, inputs2048/8192 queued together, fixed32 outputs, c2/page64/cacheoff; one warmup per mode followed by3 measured batches/mode in a balanced ordering. Same source process and GPU model per model; switch only while drained. Actual source/control hashes recorded. Command `python3 /tmp/stream265-control-queue.py > /tmp/stream265-control-queue.log 2>&1`. Local GPU/RNGD device users were empty before launch. Remote ATOM timing continues on a different host. This control can attribute host-pipeline timing, not prove device DMA overlap. No result yet.
+
+First control result, Qwen0.6:12batches/24sequences exact, child exit0. Median completion whole3.464331s, early2.552257s, deferred2.676551s (three measured batches/mode). Early is4.64% faster than deferred; deferred is22.74% faster than whole. Thus the total improvement cannot be attributed solely to within-request early notification. Deferred retains layer-wise assembly/copy behavior and can still overlap one request's import with another request's prefill in this c2 workload. Early-vs-deferred estimates the incremental timing effect of within-request notification; deferred-vs-whole includes other source/host-path differences and is not a pure device-overlap measurement. Other models still running; no universal attribution claim.
+
+Gemma long ATOM completed16batches/32sequences exact and drained, child/copy exits0, zero active B1 decode at every partial import. Prefetch-off whole4.116702s→stream3.713744s (9.79% reduction), prefetch-on3.807711s→3.380978s (11.21%);3measured batches/mode. KONI starts next. Qwen4 GPU notification control also completes12batches/24sequences exact: whole7.298562s, early5.691523s, deferred5.866724s; early2.99% faster than deferred. These first two control models both show most total whole-vs-stream gain persists with deferred within-request notification. Remaining control models pending.
+
+Gemma GPU notification control completes12batches/24sequences exact, child exit0: whole median3.835957s, early3.711786s, deferred3.726879s. Early-vs-deferred difference is only0.40%; early samples3.71179/3.73507/3.67427s overlap deferred3.73673/3.72688/3.68459s, so these three repetitions do not establish a reliable additional early-notification gain for Gemma. Qwen0.6/Qwen4 early/deferred sample ranges are separated in this recorded run, but only three samples/model/mode and one c2 workload; no general statistical or device-overlap claim. KONI control and long ATOM source both remain live. Control auditor self-check accepted a synthetic balanced matrix and rejected an incorrect mode ordering.
+
+## Completed four-model notification control
+
+Allfour child exits0,48batches/96sequences including warmups exact and drained. Auditor verifies fixed mode ordering, warmups, three measured batches/mode, source span counts, token arrival times and exact output arrays; deterministic summary saved in stream265-control-summary.json. GPU compute-process and local RNGD user listings empty after queue exit.
+
+|Model|Whole median(s)|Early(s)|Deferred(s)|Early reduction vs deferred|Deferred reduction vs whole|
+|---|---:|---:|---:|---:|---:|
+|Qwen0.6|3.464331|2.552257|2.676551|4.64%|22.74%|
+|Qwen4|7.298562|5.691523|5.866724|2.99%|19.62%|
+|Gemma|3.835957|3.711786|3.726879|0.40%|2.84%|
+|KONI|7.453318|5.768218|5.869349|1.72%|21.25%|
+
+Conclusion limited to this c2 synthetic2K/8K/fixed32 workload: most Qwen/KONI whole-vs-stream gain remains without within-request early notifications. The deferred control still changes source KV assembly and destination per-layer import relative to whole mode, and still permits cross-request overlap. It therefore does not isolate source assembly alone or prove device DMA overlap. Early-vs-deferred is the narrower incremental notification-timing comparison. Three samples/mode; Gemma ranges overlap. Preserve whole default; do not describe total speedup as an overlap percentage.
+
+## Completed long ATOM matrix
+
+Allfour child/copy exits0;64batches/128sequences including warmups exact and drained. Auditor verifies full mode order, per-stratum warmups, three measured batches/mode, arrivals, source spans and zero active B1 decode during import. Final KONI medians: prefetch-off whole12.908097s→stream10.430965s (19.19%), prefetch-on10.638944s→8.847242s (16.84%). Exact medians and TTFT reductions are in stream265-long-atom-summary.json. Across allfour models reductions are35.66/21.26/9.79/19.19% without prefetch and39.25/22.90/11.21/16.84% with prefetch (Qwen0.6/Qwen4/Gemma/KONI). These are paired whole-vs-stream comparisons within each prefetch stratum, not the independent prefetch benefit.
+
+Cleanup: delete initially printed while Pod was still Terminating; did not treat that as absence. Subsequent wait-for-delete exited0 and namespace listing returned no resources; queue session64332 exited0. Only then allocated owned stream265fault (same resource limits), waited Ready and started `python3 /tmp/stream265-fault-atom-queue.py > /tmp/stream265-fault-atom-queue.log 2>&1`, session53032. Three-case fault driver described above is now running; no hardware fault result yet. Isolated production package unchanged at33d6cb0 hashes.
+
+Actual ATOM fault first result: Qwen0.6 child/copy exits0, all three cases complete and owners drain. Cancel/reuse intercepted after1 imported span, before admission; replacement16-token output equals retained same-route reference and incomplete prefix index stayed empty. Independent source-death case also intercepted after1span with no admission or owner poisoning. Prefetch-source-death occurred while survivor had13tokens and victim slot=-1; all512 retained survivor tokens exactly equal reference, owner unpoisoned. Qwen4 now running. Local and remote driver SHA256 match7ec939b50a94ddce9aee21a6489d2e91a88f11d298e97659bf94df7bf8423c5b; manifest retained. Raw output_tokens16 describes the initial case, while the prefetch case explicitly retains512-token arrays. No full four-model fault completion claim yet.
+
+## Higher-concurrency/longer-output GPU extension
+
+Local GPU/RNGD compute/device users empty before launch. Same production runtime and same source factory, compiledwidth8192, four queued prompts2048/8192/2048/8192, fixed128output,4decoder/source slots, context8320/page64/cacheoff. Whole/stream alternating sequence unchanged:two warmups then three measured batches/mode. Same source process/model per model; compare every output against initial whole reference. Test driver stream265-c4-gpu.py derives from the prior longGPU driver; command `python3 /tmp/stream265-c4-queue.py > /tmp/stream265-c4-queue.log 2>&1`. This extends the previous c2/fixed32 workload; independent host from the active remote ATOM fault queue. No result yet.
+
+First c4/fixed128 GPU result: Qwen0.6 completes8batches/32sequences exact, child exit0. Three measured batches/mode median whole7.982224s→stream6.327924s (20.72% lower); median output throughput64.143→80.911tok/s, defined as512 generated tokens per batch divided by completion time. Four TTFT reductions[29.67277789085594, 32.494779240547636, 28.901218598702016, 29.00023215188736]. Auditor verifies four128-token arrays, mode order, warmups, token arrivals and spans; remaining models pending. This is a distinct workload from c2/fixed32 and not a directly paired concurrency-scaling comparison.
+
+Qwen4 c4/fixed128 GPU completed8batches/32sequences exact, child exit0; auditor passes. Median whole15.477492s→stream12.933404s (16.44% lower); median output throughput33.080→39.587tok/s. Gemma/KONI pending. This is same-route whole-vs-stream parity; previous nativeGPU-vs-RNGD numerical differences are not waived.
+
+Gemma c4/fixed128 GPU completed8batches/32sequences exact, child exit0 and auditor passes: whole median9.206593s→stream8.998546s (2.26% lower). KONI remains queued/running. Added a review map to STREAMING.md identifying source export, SHM/pipe protocol, Torch/ATOM ownership and CLI files; all relative links verified. Documentation explicitly distinguishes this same-host research-factory path from native furiosa-llm KV or cross-host streaming and states notification-control attribution limits. Production unchanged.
+
+## Completed c4/fixed128 GPU matrix
+
+Allfour child exits0;32batches/128sequences including warmups exact and drained. Independent auditor recomputes summaries and verifies mode ordering, warmups, four128-token arrays, arrivals and span traces. Local GPU compute-process and RNGD device-user listings empty after queue exit.
+
+|Model|Whole median(s)|Stream median(s)|Completion reduction|
+|---|---:|---:|---:|
+|qwen06b|7.982224|6.327924|20.72%|
+|qwen4b|15.477492|12.933404|16.44%|
+|gemma|9.206593|8.998546|2.26%|
+|koni|16.025822|12.925748|19.34%|
+
+Scope: one host, fixed compiledwidth8192, mixed2K/8K/2K/8K prompts, c4, fixed128 output without EOS stopping, caches off, three measured batches/mode after two warmups. This extends c2/fixed32 parity and timing, not native numerical equivalence or semantic answer quality. Gemma improvement remains small. Whole remains the default. Raw files stream265-*-c4-gpu.{json,log}, queue and summary, drivers and audit_stream265_c4.py retained.
diff --git a/opt_prime/examples/pd/http_matrix/experiments/2026-10-24-stream265-atom-faults.md b/opt_prime/examples/pd/http_matrix/experiments/2026-10-24-stream265-atom-faults.md
new file mode 100644
index 00000000..c9195ce4
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/experiments/2026-10-24-stream265-atom-faults.md
@@ -0,0 +1,43 @@
+# Four-model actual ATOM streaming fault isolation
+
+- **Date**: 2026-10-24
+- **Task**: Within-request layer streaming (aicomp #265), PR266
+- **Host / device**: RNGD+ATOM host; one allocated RNGD and one ATOM, B1; existing context9216 artifacts. Same SDK environments as the long-ATOM streaming record.
+- **Commit**: production runtime33d6cb0; evidence worktree5978c35 before this update. Runtime file hashes retained in each model JSON; driver SHA256 in stream265-atom-fault-driver-manifest.json.
+
+## Question
+
+Can a partial stream be cancelled or its producer killed without publishing incomplete prefixes, poisoning the ATOM owner, or changing a surviving decode?
+
+## Setup
+
+Isolated deployed package `stream265-code`, sourcewidth8192, one source slot, compiled decode context9216/B1. Prompts2048/8192. Cancel/reuse generates16tokens; active-survivor isolation generates512tokens. Qwen models enable prefix caching with3blocks; Gemma uses the corrected global-attention artifact and cacheoff/3blocks; KONI cacheoff/1block. All tests use the same-route no-fault reference. Device allocation is discovered inside the owned Pod, never assumed from another Pod.
+
+## Command
+
+Run inside the allocated Pod from the experiment directory, using the archived wrapper and driver:
+
+```bash
+bash run-stream265-fault-atom.sh --label qwen06b --model Qwen/Qwen3-0.6B --family qwen --kv-blocks 3 --cache-dir /home/gspark/workspace/codex-serving-matrix-201/atom-artifacts
+bash run-stream265-fault-atom.sh --label qwen4b --model Qwen/Qwen3-4B --family qwen --kv-blocks 3 --cache-dir /home/gspark/workspace/codex-serving-matrix-201/atom-artifacts
+bash run-stream265-fault-atom.sh --label gemma --model unsloth/gemma-3-1b-it --family gemma --kv-blocks 3 --cache-dir /home/gspark/workspace/codex-serving-matrix-201/atom-artifacts-global225
+bash run-stream265-fault-atom.sh --label koni --model KISTI-KONI/KONI-Llama3.1-8B-Instruct-20241024 --family koni --kv-blocks 1 --cache-dir /home/gspark/workspace/codex-serving-matrix-201/atom-artifacts
+python3 evidence265/audit_stream265_atom_faults.py evidence265
+```
+
+Wrapper records the interpreter and environment paths. Executed driver SHA256 is7ec939b50a94ddce9aee21a6489d2e91a88f11d298e97659bf94df7bf8423c5b. Keep matching model artifacts and source factories from the main streaming experiment.
+
+## Results
+
+| Model | Cases passed | Replacement tokens exact | Survivor tokens exact | Survivor tokens emitted at producer death |
+|---|---:|---:|---:|---:|
+| Qwen3-0.6B | 3 | 16/16 | 512/512 | 13 |
+| Qwen3-4B | 3 | 16/16 | 512/512 | 6 |
+| Gemma3-1B | 3 | 16/16 | 512/512 | 10 |
+| KONI-8B | 3 | 16/16 | 512/512 | 5 |
+
+Cases: cancel after partial import then reuse; kill producer after partial import before admission; kill a prefetched producer while another request decodes. Prefix-index assertions are in the driver. All source-death owners remain unpoisoned and idle after draining. The prefetched victim has no admitted slot. All child/copy exit codes0; full auditor validates retained token arrays, triggers, ownership and completion. Raw `evidence265/stream265-*-fault-atom.json` and matching logs retain results; header output_tokens16 refers to the initial case, while survivor arrays contain512tokens.
+
+## Conclusion
+
+All12 actual fault cases pass on the measured four-model paths. This proves the recorded streaming lifecycle cases, not native-backend numerical equivalence, hybrid prefix/host restoration, device DMA overlap, or arbitrary multi-lane behavior. The owned fault Pod was still present immediately after the last child finished; explicit wait-for-delete then empty namespace listing confirmed cleanup. Queue exited0. A later HTTP run uses a separate owned Pod.
diff --git a/opt_prime/examples/pd/http_matrix/experiments/2026-10-24-stream265-atom-http-progress.md b/opt_prime/examples/pd/http_matrix/experiments/2026-10-24-stream265-atom-http-progress.md
new file mode 100644
index 00000000..661c59fa
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/experiments/2026-10-24-stream265-atom-http-progress.md
@@ -0,0 +1,172 @@
+# ATOM HTTP streaming: three models verified, KONI incomplete
+
+- **Date**: 2026-10-24
+- **Task**: RNGD layer streaming (aicomp #265); HTTP reliability (#267)
+- **Host / device**: RNGD+ATOM benchmark host; one RNGD and one allocated ATOM
+- **Commit**: production 33d6cb08ec806f693dbecfd7d630331a3bd1bb2f; HTTP-only listener candidate uses backlog 128
+- **Environment**: Furiosa 2026.4 source environment; ATOM harness Torch 2.11.0+cpu / Transformers 5.8.1
+
+## Setup and command
+
+Source width 8192, BF16, context 9216, decoder B1, concurrency 16,
+caches disabled. Inputs 128/512/2048/8192, outputs 32/128. Eight warmups,
+128 retained-token correctness responses and 128 timed SSE text responses per
+successful mode. One timed batch per condition. Fixed output length ignores EOS.
+Use the existing source factories and ATOM configuration from STREAMING.md;
+model-specific configs and raw artifacts are retained by the source hashes in
+`evidence265/http267-atom-source-hashes.json`.
+
+Recorded research-driver invocation, with site values supplied externally:
+
+```bash
+python3 analysis/serving-matrix-201/run_http267_atom.py \
+  --host "$ACCELERATOR_SSH_TARGET" --pod "http267-$MODEL_LABEL" \
+  --model "$MODEL_LABEL"
+```
+
+## Verified results
+
+Fresh independent audit checks whole and stream for Qwen3-0.6B, Qwen3-4B and
+Gemma3-1B: 48 conditions, 48 warmups, 768 full-token checks and 768 timed SSE text
+checks. Whole/stream prompts and reference arrays match exactly in all eight
+conditions per model. Qwen3-4B and Gemma native modes also completed and were
+independently audited. Summaries are in evidence265.
+
+At input 8192/output 128, throughput in tokens/s:
+
+| Model | Native ATOM | Whole KV | Layer streaming |
+|---|---:|---:|---:|
+| Qwen3-0.6B | Not compared in this table | 33.0665 | 43.3496 |
+| Qwen3-4B | 12.2876 | 12.6716 | 14.5315 |
+| Gemma3-1B | 57.1134 | 39.4864 | 43.5681 |
+
+Qwen3-4B streaming improves whole-transfer throughput by 10.8–27.4% across the
+measured conditions; Gemma improves by 2.6–15.6%. Gemma remains slower than
+native ATOM in all eight conditions. Native uses one ATOM; PD adds one RNGD,
+so these are not equal-resource or cost comparisons. Qwen3-4B native/PD has
+pre-EOS differences in two conditions. Same-source transfer parity does not
+establish native equivalence or semantic answer quality.
+
+## Retained failure and remaining scope
+
+KONI native ATOM failed the original 180-second socket response wait at
+8192/128/c16. Seven earlier correctness conditions (112 responses) and eight
+warmups completed; no timed batch completed. Server remained alive at the
+failure snapshot, with zero OOM events and zero listener drops/overflows.
+This does not establish a queueing-only cause. Whole/stream were not launched.
+A distinct 600-second native diagnostic completed; it does not erase or fix the
+original deadline failure. The failed-run summary is retained in evidence265.
+
+The listener candidate eliminated observed overflow/drop counters in a paired
+Qwen3-0.6B run, but the original connection-reset cause is not conclusively
+proven. It is diagnostic-only and is not integrated into this PR's runtime.
+
+Separate width-1024 experiments (#268) are also not part of this PR: Qwen3-0.6B
+has a heldout answer regression and KONI has width-dependent token divergence.
+Production source width/default behavior is unchanged here. Repeated timing,
+ATOM compiled batch >1, and direct device-DMA overlap remain outstanding.
+
+
+## KONI native 600-second diagnostic completed
+
+The separate native child and artifact copies exited0. Independent audit passes
+all eight conditions: eight warmups,128 full-token checks and128 timed SSE text
+checks (264 responses). Prompts and all eight reference token arrays match the
+original failed180-second run. End-of-run listener drops/overflows and OOM event
+deltas are zero; the server was alive before normal cleanup.
+
+At8K input/128 output, native ATOM throughput is7.7847tok/s, median first SSE
+130.6080s and median completion139.7550s. Maximum first SSE is253.9345s and maximum
+completion263.0788s; five timed requests wait over180s for first SSE and six finish
+after180s. This is a single ATOM execution slot with16 queued requests. It shows
+long latency also exists without PD; the evidence does not separate queue wait
+from prefill time or prove the original nonstreamed timeout's precise cause.
+
+The600-second value controls socket wait, not a guaranteed total request deadline.
+Nonstreamed correctness checks retain token arrays but not individual durations.
+One timed batch per condition is diagnostic, not statistical performance evidence.
+The original180-second failure remains failed. RNGD-to-ATOM whole subsequently completed as recorded below; streaming also completed as recorded below.
+
+Reproduce the diagnostic with run_http267_deadline_atom.py, --model koni,
+--pod http267d-koni and the existing site-specific --host. It uses the same model
+and hardware configuration, and benchmark267_deadline.py --request-timeout 600.
+See evidence265/http267d-koni-native-complete-summary.json for all eight timing
+rows and the raw-result SHA256. Runtime remains33d6cb0; no serving code changed.
+
+
+## KONI whole KV diagnostic completed
+
+Fresh independent audit confirms native and whole each pass eight conditions,
+eight warmups, 128 retained-token checks and 128 timed SSE text checks.
+Both children and artifact copies exited zero; raw hashes match the summary.
+Runtime remains 33d6cb0, socket wait 600 seconds, B1/c16, caches off.
+
+| Input/output tokens | Native tok/s | Whole KV tok/s |
+|---|---:|---:|
+| 128/32 | 14.6499 | 5.9430 |
+| 128/128 | 14.7951 | 10.8468 |
+| 512/32 | 12.8533 | 5.8954 |
+| 512/128 | 14.2666 | 10.7350 |
+| 2048/32 | 8.5544 | 5.5431 |
+| 2048/128 | 12.4293 | 10.3935 |
+| 8192/32 | 3.3584 | 4.6693 |
+| 8192/128 | 7.7847 | 9.3113 |
+
+At 8K/128, whole improves observed throughput by 19.6%; median completion falls
+139.755 to 117.284 seconds, maximum 263.079 to 219.946 seconds. Shorter inputs
+remain slower with PD. This is one measured batch per condition; native uses one
+ATOM while PD adds one RNGD. Neither statistical nor equal-resource benefit is
+established. The original 180-second failure is retained and remains unresolved.
+
+Native/whole references match six of eight conditions. The first mismatch is at
+zero-based token 40 for 128/128 and token 37 for 8192/128. EOS-aware comparison
+also matches only six: no EOS occurs in these fixed-length references. Passing
+same-route response checks does not establish native or semantic equivalence.
+See evidence265/http267d-koni-native-whole-summary.json and the companion EOS
+summary for hashes, individual timings and comparison details.
+
+Before the next phase, the owned Pod had 2265 seconds remaining versus 2842 seconds
+used by the completed whole phase. After child/copy completion and absence of
+benchmark/engine processes were checked, only that Pod was recreated. No active
+measurement was restarted. Stream now uses a different physical ATOM card of
+the same class, a limitation for subsequent timing comparisons. That streaming phase subsequently completed as recorded below.
+
+
+## Completed KONI streaming and four-model whole/stream coverage
+
+All three KONI children and both artifact copies per child exited zero; outer
+queue exited zero. Independent raw audit validates each route's eight conditions,
+eight warmups, 128 full-token checks and 128 timed SSE text checks. Whole/stream
+reference arrays match all eight conditions. Native/whole remains six of eight;
+the two pre-EOS differences remain. Original 180-second failure remains retained.
+Completed Pod absence was verified after parent cleanup.
+
+| Input/output tokens | Native tok/s | Whole KV tok/s | Streaming tok/s |
+|---|---:|---:|---:|
+| 128/32 | 14.6499 | 5.9430 | 6.7293 |
+| 128/128 | 14.7951 | 10.8468 | 11.3686 |
+| 512/32 | 12.8533 | 5.8954 | 6.8371 |
+| 512/128 | 14.2666 | 10.7350 | 11.3826 |
+| 2048/32 | 8.5544 | 5.5431 | 6.7273 |
+| 2048/128 | 12.4293 | 10.3935 | 11.4099 |
+| 8192/32 | 3.3584 | 4.6693 | 6.6058 |
+| 8192/128 | 7.7847 | 9.3113 | 10.8298 |
+
+At 8K/128, streaming has 16.3% greater observed throughput than whole transfer,
+39.1% greater than native. Median completion is 100.698 seconds versus 117.284
+whole and 139.755 native; median first SSE is 91.565 versus 108.164 and 130.608.
+Streaming maximum completion is 189.105 seconds, so a 180-second total response
+SLO is still not met. Shorter inputs remain slower than native. One timed batch
+per condition and different physical ATOM cards limit timing conclusions; PD adds
+one RNGD versus native. Stream OOM events and listener drop/overflow deltas are
+zero; server alive at final snapshot before normal teardown. No DMA-overlap claim.
+
+Together with the preceding Qwen3-0.6B, Qwen3-4B and Gemma3-1B results, all four
+models now have completed B1/c16 ATOM whole/stream HTTP controls, eight exact
+reference pairs per model. This does not establish native equivalence, semantic
+answer quality, batch-four behavior or equal-resource performance superiority.
+
+Raw three-route JSON and summary with hashes, terminal and copy evidence are in
+evidence265/http267d-koni-atom-c16-{native,whole,stream}.json and
+http267d-koni-three-route-summary.json. Reproduce with the diagnostic runner above;
+stream uses the corresponding whole result as --reference. Runtime stays33d6cb0.
diff --git a/opt_prime/examples/pd/http_matrix/experiments/2026-10-24-stream265-c16.md b/opt_prime/examples/pd/http_matrix/experiments/2026-10-24-stream265-c16.md
new file mode 100644
index 00000000..095ad876
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/experiments/2026-10-24-stream265-c16.md
@@ -0,0 +1,43 @@
+# Four-model streaming with sixteen queued requests
+
+- **Date**: 2026-10-24
+- **Task**: Within-request layer streaming (#265), four-model matrix (#201), PR266
+- **Host / device**: GPU+RNGD host; one NVIDIA A10080GB PCIe, driver580.178.04, one RNGD; Torch2.13 environment and isolated Furiosa source interpreter from the existing streaming setup.
+- **Commit**: production runtime33d6cb0; experiments executed from evidence head5978c35. Later evidence commitb59e7fc does not change runtime. Source server/factory SHA256 retained per raw result.
+
+## Question
+
+Does the same-source whole/stream behavior remain exact and beneficial when16requests queue behind4 execution slots?
+
+## Setup
+
+One source process/model, sourcewidth8192,4source/decoder slots, context8320/page64, cachesoff. Sixteen prompts alternate2048/8192tokens eight times; fixed128output without EOS stopping. Whole/stream warmups, then measured mode order whole/stream/stream/whole/whole/stream. Every request output compares to its corresponding first whole reference. This tests queue pressure with4execution slots, not a16-lane graph. Same local device allocation as the earlier c4 experiment; no competing owned local workload at launch.
+
+## Command
+
+Executed driver and queue are archived under evidence265. The queue records its exact environment and model/checkpoint paths.
+
+```bash
+python3 /tmp/stream265-c16-queue.py > /tmp/stream265-c16-queue.log 2>&1
+python3 evidence265/audit_stream265_c16.py evidence265
+python3 evidence265/selfcheck_stream265_c16_audit.py
+```
+
+Place the archived queue/driver at their recorded paths and use the source registration wrapper from the main streaming experiment. The queue launches each model sequentially with the recorded pt213 interpreter. Auditor verifies all16x128-token arrays, exact per-request parity, arrivals, mode/warmup order, progress counts,4-slot metadata and allfour child exits. It exits2 while any model is incomplete. Its synthetic self-check rejects corrupted tokens, shortened TTFT arrays, changed slots, failed child exits and missing models; these synthetic cases are verifier checks, not hardware measurements.
+
+## Results
+
+Allfour child exits0,32batches/512sequences including warmups exact and drained. Three measured batches per mode:
+
+|Model|Whole median(s)|Stream median(s)|Completion reduction|
+|---|---:|---:|---:|
+|qwen06b|24.919067|18.640325|25.20%|
+|qwen4b|52.204738|41.753038|20.02%|
+|gemma|27.363664|26.725551|2.33%|
+|koni|53.679366|42.452937|20.91%|
+
+Raw JSON retains every request's128tokens, token-arrival timestamps and host import traces. Samples, medians and per-request TTFTs are in evidence265/stream265-c16-summary.json. Queue exited0; GPU compute-process and RNGD device-user listings empty before the subsequent HTTP run.
+
+## Conclusion
+
+Allfour models retain same-route parity under this queued workload. Qwen/KONI completion reductions are20–25%; Gemma is2.33%. These three-sample synthetic fixed-output results do not establish native numerical equivalence, semantic answer quality, HTTP behavior, device DMA overlap, or universal speedup. The earlier deferred-notification control shows total gains cannot be attributed entirely to within-request overlap. Whole transfer remains default. GPU/ATOM HTTPc16 work is tracked separately and is not part of these results.
diff --git a/opt_prime/examples/pd/http_matrix/experiments/2026-10-24-stream265-gemma-http.md b/opt_prime/examples/pd/http_matrix/experiments/2026-10-24-stream265-gemma-http.md
new file mode 100644
index 00000000..3040a624
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/experiments/2026-10-24-stream265-gemma-http.md
@@ -0,0 +1,37 @@
+# Gemma HTTP c16 layer streaming
+
+- **Date**: 2026-10-24
+- **Task**: RNGD layer streaming (aicomp #265), partial four-model HTTP matrix
+- **Host / device**: GPU benchmark host; A10080GB PCIe, driver580.178.04; PD additionally one RNGD, Furiosa2026.4 environment
+- **Commit**: production33d6cb08ec806f693dbecfd7d630331a3bd1bb2f; launch head5978c35d9d478a62c6e30819f03e7962925c1ae1. Later commits evidence/tests only. Manifest retained in evidence265/stream265-http-c16-manifest.json.
+
+## Question
+
+Does Gemma layer streaming preserve whole-source output and improve HTTP latency under sixteen queued requests?
+
+## Setup and command
+
+Gemma3-1B BF16,4execution slots,4source slots,sourcewidth8192,context9216,cachesoff. Inputs128/512/2048/8192,outputs32/128,c16. Each mode8warmups,128retained-token checks,128timed SSE text checks. One measured batch per condition. Native uses one GPU; PD adds one RNGD. Exact driver and its environment/dependencies retained in evidence265/run_stream265_http_c16.py; it invokes the issue248 benchmark and llmd240 HTTP module, whose hashes are in the manifest.
+
+```bash
+/home/gspark/venvs/pt213/bin/python analysis/serving-matrix-201/run_stream265_http_c16.py --run
+```
+
+## Results
+
+Allthree modes exit0 and independently pass audit_result. Whole/stream reference arrays match exactly in8/8conditions. Native/whole strict references match4/8: first differences at index8 for512input and24 for2048input, but firstEOS is index2 on both paths. Generation EOS IDs[1,106]; trimming through and including firstEOS yields8/8 equality. At8192 there is noEOS in32/128tokens and outputs remain exact. This post-hoc distinction preserves strict mismatches and does not waive earlier Gemma content/numerical failures.
+
+| Input | Output | Native TTFT s | Whole TTFT s | Stream TTFT s | Stream throughput change vswhole % |
+|---:|---:|---:|---:|---:|---:|
+| 128 | 32 | 1.44 | 12.78 | 12.50 | 2.27 |
+| 128 | 128 | 5.01 | 12.70 | 12.63 | 1.51 |
+| 512 | 32 | 1.66 | 12.83 | 12.42 | 3.17 |
+| 512 | 128 | 5.19 | 12.83 | 12.49 | 1.56 |
+| 2048 | 32 | 3.08 | 12.92 | 12.51 | 2.52 |
+| 2048 | 128 | 6.68 | 12.84 | 12.53 | 1.97 |
+| 8192 | 32 | 12.65 | 13.65 | 12.65 | 2.10 |
+| 8192 | 128 | 13.94 | 13.48 | 13.11 | 2.19 |
+
+## Conclusion
+
+Observed throughput+1.51–3.17% is small and needs repeated measurements before claiming reliable improvement. Do not generalize Qwen's larger gains to Gemma. Native remains substantially faster for short prompts; fixedwidth cost remains unisolated. No device DMA-overlap, equal-resource advantage or model-quality claim. KONI GPU HTTP and remaining ATOM candidate cases are still active. Raw files stream265-gemma-http-c16-{native,whole,stream}.json, derived observations and EOS comparison are in evidence265.
diff --git a/opt_prime/examples/pd/http_matrix/experiments/2026-10-24-stream265-gpu-http-complete.md b/opt_prime/examples/pd/http_matrix/experiments/2026-10-24-stream265-gpu-http-complete.md
new file mode 100644
index 00000000..1e8f9ab7
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/experiments/2026-10-24-stream265-gpu-http-complete.md
@@ -0,0 +1,44 @@
+# Four-model GPU HTTP streaming matrix completed
+
+- **Date**: 2026-10-24
+- **Task**: RNGD layer streaming (aicomp #265)
+- **Host / device**: GPU benchmark host; A10080GB PCIe,driver580.178.04; PD additionally one RNGD,Furiosa2026.4 environment
+- **Commit**: production33d6cb08ec806f693dbecfd7d630331a3bd1bb2f;launch head5978c35d9d478a62c6e30819f03e7962925c1ae1. Later changes evidence/tests only; captured hashes in evidence265/stream265-http-c16-manifest.json.
+
+## Question and setup
+
+Compare native GPU, whole RNGD KV handoff and layer streaming for Qwen3-0.6B,Qwen3-4B,Gemma3-1B,KONI-8B. BF16,sourcewidth8192,context9216,cachesoff,c16 behind4execution slots;inputs128/512/2048/8192,outputs32/128. Each of12modes has8warmups,128retained-token checks and128timed SSE text checks. One measured batch per condition, not statistical speedup evidence. Native uses one GPU; PD adds one RNGD. Exact driver, configuration and dependencies documented in the preceding Qwen/Gemma HTTP records.
+
+## Command
+
+```bash
+/home/gspark/venvs/pt213/bin/python analysis/serving-matrix-201/run_stream265_http_c16.py --run
+```
+
+## Results
+
+Queue session41030 exited0. All12child modes exit0 and independently pass audit_result. Totals:96conditions,96warmups,1536retained-token checks,1536timed SSE text checks. Whole/stream reference arrays identical for allfour models across all8conditions. Independent summary http265-four-model-gpu-summary.json in evidence265.
+
+| Model | Native/whole exact references | Whole/stream exact references | Remaining numerical distinction |
+|---|---:|---:|---|
+| Qwen3-0.6B |8/8|8/8|Measured fixtures only|
+| Qwen3-4B |4/8|8/8|Four differences beforeEOS;#256 remains|
+| Gemma3-1B |4/8|8/8|All strict differences afterEOS;throughEOS8/8 on these fixtures;earlier#233/#263 failures remain|
+| KONI-8B |5/8|8/8|Three differences beforeEOS;#261 remains|
+
+KONI first divergent indices64/56/98 for128/2048/8192input with128output. EOS union128001/128009 verified; noEOS in these references. Short fixture and both fulloutputs exactly reproduce the earlier#261diagnosis. Head-onlyFP32 remains rejected as a general fix. Exact fixtures and hashes retained.
+
+| KONI input | Output | Native TTFT s | Whole TTFT s | Stream TTFT s | Stream throughput change vswhole % |
+|---:|---:|---:|---:|---:|---:|
+| 128 | 32 | 1.33 | 25.79 | 20.55 | 23.78 |
+| 128 | 128 | 4.20 | 25.70 | 20.69 | 20.94 |
+| 512 | 32 | 1.76 | 25.29 | 20.23 | 24.72 |
+| 512 | 128 | 4.80 | 25.33 | 20.68 | 22.29 |
+| 2048 | 32 | 4.39 | 26.05 | 20.60 | 24.35 |
+| 2048 | 128 | 7.39 | 25.69 | 19.95 | 28.73 |
+| 8192 | 32 | 21.29 | 29.50 | 22.12 | 33.93 |
+| 8192 | 128 | 22.67 | 28.24 | 20.85 | 33.29 |
+
+## Conclusion
+
+KONI's observed stream throughput gain20.94–33.93% joins the larger Qwen gains; Gemma's1.51–3.17% needs repeats before a reliable improvement claim. No equal-resource advantage, device DMA overlap, native numerical equivalence or answer quality is established by this matrix. ATOM remaining models and width-cost experiments continue; overall four-model objective remains active. Once GPU queue exited and the RNGD device-user check was empty, separate issue268source probes started with Qwen0.6width512. Those measurements are not part of these unchanged HTTP runs.
diff --git a/opt_prime/examples/pd/http_matrix/experiments/2026-10-24-stream265-qwen-http.md b/opt_prime/examples/pd/http_matrix/experiments/2026-10-24-stream265-qwen-http.md
new file mode 100644
index 00000000..82103ff0
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/experiments/2026-10-24-stream265-qwen-http.md
@@ -0,0 +1,47 @@
+# Qwen HTTP c16 streaming comparison
+
+- **Date**: 2026-10-24
+- **Task**: RNGD layer streaming (aicomp #265), partial four-model HTTP matrix
+- **Host / device**: GPU benchmark host; A10080GB PCIe, driver580.178.04; PD additionally one RNGD with Furiosa2026.4 environment.
+- **Commit**: production33d6cb08ec806f693dbecfd7d630331a3bd1bb2f; launch head5978c35d9d478a62c6e30819f03e7962925c1ae1. Later commits evidence/tests only; captured hashes in evidence265/stream265-http-c16-manifest.json.
+
+## Question
+
+Does layer streaming preserve same-producer HTTP outputs at c16, and how does latency vary with input length?
+
+## Setup and command
+
+BF16, four GPU execution slots and four PD source slots. Fixed source width8192, context9216, caches off. Each mode:8 warmups,128 retained-token correctness responses and128 timed SSE text responses. One measured batch per condition. Native uses one GPU; PD adds one RNGD.
+
+```bash
+/home/gspark/venvs/pt213/bin/python analysis/serving-matrix-201/run_stream265_http_c16.py --run
+```
+
+Executed from the research workspace. Retained driver uses benchmark.py from the issue248 worktree and llmd240.py HTTP module; hashes in manifest. Driver runs four models sequentially; only completed Qwen cases appear here. No runtime edits during runs.
+
+## Results
+
+Six modes exit0 and independently pass audit_result. Both models have identical whole/stream reference arrays. Qwen0.6 native/whole references match all8 conditions; Qwen4 native/whole differ before EOS in4/8 conditions (retained difference JSON). Same-route success does not resolve cross-backend numerical equivalence.
+
+| Model | Input | Output | TTFT native / whole / stream (s) | Throughput native / whole / stream (tok/s) |
+|---|---:|---:|---|---|
+| qwen06b | 128 | 32 | 1.18 / 9.00 / 6.29 | 171.9 / 29.3 / 40.8 |
+| qwen06b | 128 | 128 | 4.17 / 8.91 / 7.28 | 185.6 / 105.7 / 123.7 |
+| qwen06b | 512 | 32 | 1.42 / 8.92 / 6.51 | 150.2 / 29.3 / 39.5 |
+| qwen06b | 512 | 128 | 4.61 / 8.77 / 7.45 | 172.6 / 105.9 / 121.0 |
+| qwen06b | 2048 | 32 | 2.91 / 9.62 / 7.09 | 81.9 / 27.5 / 36.5 |
+| qwen06b | 2048 | 128 | 5.84 / 9.83 / 7.88 | 144.4 / 98.2 / 115.2 |
+| qwen06b | 8192 | 32 | 12.89 / 11.72 / 9.44 | 20.1 / 24.5 / 28.2 |
+| qwen06b | 8192 | 128 | 13.98 / 11.87 / 10.67 | 68.7 / 87.1 / 92.8 |
+| qwen4b | 128 | 32 | 1.48 / 23.60 / 18.43 | 138.1 / 11.4 / 14.5 |
+| qwen4b | 128 | 128 | 5.22 / 23.29 / 18.40 | 147.3 / 43.5 / 54.0 |
+| qwen4b | 512 | 32 | 1.92 / 23.41 / 18.44 | 112.0 / 11.4 / 14.4 |
+| qwen4b | 512 | 128 | 5.69 / 23.66 / 18.62 | 139.6 / 42.6 / 53.2 |
+| qwen4b | 2048 | 32 | 4.13 / 24.50 / 19.60 | 58.7 / 10.9 / 13.5 |
+| qwen4b | 2048 | 128 | 7.92 / 24.62 / 19.46 | 108.1 / 41.3 / 51.7 |
+| qwen4b | 8192 | 32 | 20.26 / 28.52 / 23.09 | 12.9 / 10.1 / 12.3 |
+| qwen4b | 8192 | 128 | 21.94 / 26.95 / 21.51 | 45.5 / 38.3 / 46.8 |
+
+## Conclusion
+
+Qwen4 stream improves observed whole-path throughput22.1–27.2% in this run. At8192/128 its46.77tok/s is near native45.46tok/s, with one additional accelerator. Short inputs remain substantially slower than native GPU. Fixed-width padding is a candidate explanation supported by code inspection, not isolated causal measurement. These single batches do not prove statistical speedup, equal-resource advantage, model quality or device DMA overlap. Gemma/KONI HTTP cases and ATOM candidate matrix remain in progress. Original ATOM reset is tracked separately in#267, not waived by GPU results.
diff --git a/opt_prime/examples/pd/http_matrix/experiments/2026-10-25-default-http201.md b/opt_prime/examples/pd/http_matrix/experiments/2026-10-25-default-http201.md
new file mode 100644
index 00000000..09c42353
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/experiments/2026-10-25-default-http201.md
@@ -0,0 +1,27 @@
+# Native GPU default HTTP module qualification
+
+- Date: 2026-10-25; related #201 and PR266.
+- Runtime: 03054a124d95fb999780f7fd6cac26d6c33cb89b, clean before execution. No production changes.
+- Hardware/runtime: same A10080GB PCIe as the preceding overridden-module run, driver580.178.04, torch2.13.0+cu130, Transformers5.15.0. All four selected checkpoints and native decoder arguments unchanged.
+
+## Result
+
+Direct execution of serve_batched.py, with the branch's default opt_prime.accel.serve.llmd module, passes all32 native GPU conditions: input512/2048, output32/128, concurrency1/4, four models. Total176responses:16warmups,80retained-token checks and80timed SSE-text checks. Independent inspection of saved arrays/counts and recalculation of throughput/median latency pass. All16reference arrays equal both the preceding overridden-module references and earlier c16 native references.
+
+The native successful-request coverage therefore no longer depends solely on the historical HTTP override. This is qualification of these exact conditions on this experimental branch, not current-main support or an all-path readiness/cancellation result. Each condition has one timed batch; no HTTP overhead or speedup claim.
+
+## Controlled change and reproduction
+
+Keep frozen models, BF16, slots4, context9216, paged KV/page64, local chunk256, cache off and GPU0 unchanged. Old argv was Python -c importlib/runpy bootstrap plus server flags. New argv is [PYTHON, serve_batched.py, same server flags]. No sys.modules replacement. Metadata and all server flags were checked equal; GPU UUIDs were checked equal locally. The default module resolves to the expected package path under the stated PYTHONPATH and has SHA256e3449979b88d03edb6d5bf0d402a708e5d98054fdf203e22b480f3d7e6fd759a. The historical override instead adds MSG_NOSIGNAL writes and uses an older readiness predicate.
+
+```bash
+PYTHONPATH="$RUNTIME/opt_prime:$RUNTIME/opt_prime/examples/pd" OMP_NUM_THREADS=4 MKL_NUM_THREADS=4 "$PYTHON" "$HARNESS" --model "$MODEL" --mode native --server-config "$DEFAULT_HTTP_CONFIG" --lengths 512 2048 --outputs 32 128 --concurrency 1 4 --repeats 1 --request-timeout 180 --output "$NEW_RESULT"
+```
+
+Use unchanged benchmark201_native_http.py from commit052f84f, experiments/evidence274, custom API profile. Harness hash, default module hash, serving script hash, all result hashes and timings are in `../evidence265/default201-gpu-summary.json`. Exact site-local executable argv/configs are retained under default201-gpu-queue.json and default201-{model}-gpu-config.json. No production runtime source was edited.
+
+Four children and coordinator exited0. Final GPU compute-process query empty; both devices14MiB idle memory. This result supersedes the override-only limitation for the successful native GPU middle-length cells recorded in `2026-10-25-native-gpu-mid201.md`; it does not retroactively qualify the earlier c16, fault, cache-on or heterogeneous tests with the default module.
+
+## Remaining work
+
+Heterogeneous-route c1/c4 intermediate lengths, cache-on combinations and default-module lifecycle behavior remain separate. Qwen4 ATOM B4 export, Gemma export/cache provenance and cross-engine numerical/quality limitations remain unresolved. Overall #201 is not closed.
diff --git a/opt_prime/examples/pd/http_matrix/experiments/2026-10-25-native-gpu-mid201.md b/opt_prime/examples/pd/http_matrix/experiments/2026-10-25-native-gpu-mid201.md
new file mode 100644
index 00000000..d53dc0d4
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/experiments/2026-10-25-native-gpu-mid201.md
@@ -0,0 +1,39 @@
+# Native GPU intermediate-length coverage
+
+- Date: 2026-10-25; related #201 and PR266.
+- Runtime: eaf7daa219a6cea68db4ec5fc08850edc848b271, clean before execution; no production changes.
+- Hardware: one A10080GB PCIe on the GPU host; no RNGD/ATOM allocated. Four models run sequentially after empty compute-process checks. All four children and coordinator exited0; final compute-process query empty, both GPUs14MiB idle memory.
+- Configuration: BF16, four decoder slots, paged KV/page64, local chunk256, context9216, producer/decoder caches off. Same frozen checkpoints/configuration as the earlier c16 native GPU matrix; torch2.13.0+cu130, Transformers5.15.0. Driver identity retained in the local queue artifact.
+
+## Coverage and results
+
+A schema-scoped inventory of177 archived HTTP JSON files found no native one-GPU correctness batch at input512/2048 and concurrency1/4. Added four models × two input lengths × two output lengths(32/128) × two concurrencies(1/4):32conditions. Per model:4warmups,20nonstream token comparisons,20timed SSE text comparisons. Total176responses.
+
+All conditions passed. An independent audit compared actual saved token arrays and text with same-server references, verified prompt identity and counts, and recalculated throughput/median first-SSE/E2E from response rows. All16reference arrays also match the corresponding earlier c16 native references. This is within-native parity, not native/PD equivalence or semantic quality.
+
+|Model|Conditions|Token checks|SSE checks|2K/128 c1 tok/s|2K/128 c4 tok/s|
+|---|---:|---:|---:|---:|---:|
+|qwen06b|8|20/20|20/20|50.24|143.18|
+|qwen4b|8|20/20|20/20|37.44|102.97|
+|gemma|8|20/20|20/20|40.41|118.85|
+|koni|8|20/20|20/20|44.71|117.01|
+
+One timing batch per condition. Throughput is aggregate client output tokens divided by batch wall time; no repeated or equal-resource speedup claim. Complete per-condition timings, raw-result hashes and source hashes are in `../evidence265/mid201-gpu-summary.json`.
+
+## Reproduction and provenance
+
+Use the unchanged benchmark201_native_http.py at ai-computing/aicomp commit052f84f, experiments/evidence274 (SHA256e1ea87a51343d1af83c40aaa9c41ae2a20e9864aa8b53bd475d047d175c41433), with each original stream265 native server config and its frozen model. Keep the process-local HTTP module from the prior c16 run: SHA2569f97e5a9b270dc5719e339748a6d6bdccbe6dfdf5267c1dd4451bab123687975. It supplies the MSG_NOSIGNAL writer and an older readiness predicate. Thus these numbers do not certify the unmodified default HTTP path or readiness behavior. Exact site-local queue argv/config hashes are retained under mid201r1-gpu-queue.json; full logs/configs remain outside the public repository.
+
+```bash
+PYTHONPATH="$RUNTIME/opt_prime:$RUNTIME/opt_prime/examples/pd" OMP_NUM_THREADS=4 MKL_NUM_THREADS=4 "$PYTHON" "$HARNESS" --model "$MODEL" --mode native --server-config "$NATIVE_CONFIG" --lengths 512 2048 --outputs 32 128 --concurrency 1 4 --repeats 1 --request-timeout 180 --output "$NEW_RESULT"
+```
+
+The first coordinator omitted PYTHONPATH and stopped before Qwen0.6 inference with ModuleNotFoundError for opt_prime.accel.pd.contracts. The failed run remains archived under mid201-*; the corrected environment run uses mid201r1-* and did not change runtime, request options, timeout or correctness criteria. This launcher failure is not a model inference failure.
+
+## Limits
+
+Cache-on, heterogeneous-route intermediate c1/c4 combinations, default HTTP route and engine-owned export/cache compatibility remain separate work. Earlier Qwen4/KONI cross-engine numerical differences and Gemma strict-format failures remain. These successful native GPU cells do not close #201.
+
+## Output-length summary correction (2026-10-30)
+
+The original c4 throughput column selected32output tokens per request because aggregate128tokens overwrote a same-named per-request field in the summary. The corrected2K/128c4 values above select128tokens per request (512tokens total). Raw measurements, timing arithmetic and correctness results are unchanged. Summary JSON now retains both `output_tokens_per_request` and aggregate `output_tokens`. Native c1 values and the separate PD GPU/ATOM tables were checked against raw condition fields and remain unchanged.
diff --git a/opt_prime/examples/pd/http_matrix/experiments/2026-10-25-pd-gpu-mid201.md b/opt_prime/examples/pd/http_matrix/experiments/2026-10-25-pd-gpu-mid201.md
new file mode 100644
index 00000000..91c94b7a
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/experiments/2026-10-25-pd-gpu-mid201.md
@@ -0,0 +1,41 @@
+# Default HTTP RNGD-to-GPU intermediate-length coverage
+
+- Date: 2026-10-25; related #201, #265 and PR266.
+- Runtime: a06a36f0fc5b577b339d94d27cf1626a3616b365, no production source changes.
+- Hardware: one RNGD and one A10080GB PCIe, driver580.178.04. Decoder torch2.13.0+cu130/Transformers5.15.0; producer torch2.10.0+cpu/Transformers5.8.1/furiosa-torch2026.4.0.
+
+## Results
+
+All four selected frozen models complete whole and stream HTTP paths using the branch's default HTTP module, without the historical HTTP override. Inputs512/2048, outputs32/128, concurrency1/4:64conditions/352responses, comprising32warmups,160saved-token comparisons and160timed SSE-text comparisons. Independent audit verifies saved arrays, counts, metadata, prompt identity and recomputed timing summaries. All8children and coordinator exit0.
+
+Whole/stream reference arrays match4/4 per model; all same-route references also match the corresponding earlier c16 references. Native/PD equality is a separate result:
+
+|Model|Whole/stream refs equal|Native/PD fixed refs equal|2K/128 c4 whole tok/s|2K/128 c4 stream tok/s|
+|---|---:|---:|---:|---:|
+|qwen06b|4/4|4/4|60.97|84.17|
+|qwen4b|4/4|2/4|33.76|40.33|
+|gemma|4/4|0/4|55.86|56.82|
+|koni|4/4|3/4|32.90|38.67|
+
+Qwen4's two128-token conditions first differ from native at zero-based token50(input512) and49(input2048); neither sequence reaches configured EOS151643/151645 within128tokens. KONI's2K/128 condition first differs at index56 with neither EOS128001nor128009 present. These pre-EOS differences remain unresolved. Gemma's full fixed-length arrays differ in all4conditions, but EOS[1,106] occurs at index2 in both paths and output through EOS is identical4/4; first differences at8/24 are after EOS. That narrow prompt result does not override earlier strict-quality failures.
+
+Timings are single sequential batches per condition, not a repeated speedup or overlap proof. Whole and stream use the same nominal resources; native uses fewer accelerators. The selected table shows measured aggregate output throughput only. All per-condition first-SSE/E2E/estimated-TPOT values, source hashes and raw artifact hashes are in `../evidence265/pdmid201-gpu-summary.json`. Whole always ran before stream; order effects are not isolated.
+
+## Reproduction
+
+Four decoder slots, BF16, context9216, paged KV/page64, local chunk256, caches off. Research RNGD producers use width8192/chip0 with the existing process-local fusion4/Mesh2 wrapper and streaming_factories adapters. This is not the vendor native LLM backend. Exact producer/HTTP/harness source hashes are in the summary. Frozen models and producer dependencies are the same as prior c16 streaming evidence. Before each arm, require no GPU compute process and no device-file holder for the selected RNGD; never terminate another workload.
+
+Use benchmark201_native_http.py from commit052f84f experiments/evidence274 (custom HTTP profile), with direct serve_batched.py configs and existing producer wrapper/factories. The stream config adds --pd-prefill-stream-layers and uses the whole result as reference:
+
+```bash
+PYTHONPATH="$RUNTIME/opt_prime:$RUNTIME/opt_prime/examples/pd:$RNGD_SOURCE:$SERVING_MATRIX" OMP_NUM_THREADS=4 MKL_NUM_THREADS=4 TORCH_DEVICE_BACKEND_AUTOLOAD=0 HF_HUB_OFFLINE=1 TRANSFORMERS_OFFLINE=1 "$PYTHON" "$HARNESS" --model "$MODEL" --mode pd --server-config "$PD_CONFIG" --lengths 512 2048 --outputs 32 128 --concurrency 1 4 --repeats 1 --startup-timeout 1800 --request-timeout 180 --output "$NEW_RESULT"
+# Stream only: add --reference "$WHOLE_RESULT".
+```
+
+Exact site-local argv/environment/config hashes and per-arm exit statuses: pdmid201-gpu-queue.json. Raw files are pdmid201-{model}-{whole,stream}.json. Preserve them and the previous native controls; full site-local configs/logs are not published.
+
+## Limits
+
+Within-route and whole/stream parity do not establish native equivalence or general answer quality. This does not newly prove DMA or compute/copy overlap. Cache-on, ATOM intermediate c1/c4 coverage and supported engine export/cache integration remain separate. The four-model goal is not complete.
+
+Cleanup verified after coordinator exit: GPU compute process query empty (exit0), selected RNGD device files have no holders (fuser exit1, no output). No unrelated process was terminated.
diff --git a/opt_prime/examples/pd/http_matrix/experiments/2026-10-30-atom-edge201-final.md b/opt_prime/examples/pd/http_matrix/experiments/2026-10-30-atom-edge201-final.md
new file mode 100644
index 00000000..2fb496fc
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/experiments/2026-10-30-atom-edge201-final.md
@@ -0,0 +1,55 @@
+# ATOM short/long HTTP comparison with final HTTP module
+
+- Date: 2026-10-30; related #201, #265 and PR266.
+- HTTP source SHA256: `e3449979b88d03edb6d5bf0d402a708e5d98054fdf203e22b480f3d7e6fd759a`.
+- Runtime: historical stream265 B1 engine and research RNGD producers with final PR266 HTTP override. This is not whole-runtime current-main or vendor native furiosa-llm qualification.
+- One ATOM decode lane; PD adds one RNGD. c4 means queued clients, not hardware batch four.
+- Decoder: torch2.9.1+cpu, Transformers4.57.6, optimum-rbln0.10.2, rebel-compiler0.11.0. Producer: torch2.10.0+cpu, Transformers5.19.0, furiosa-torch2026.4.0.
+
+## Coverage and output checks
+
+Four models, three routes (native/whole/stream), input128/8192, output32/128, concurrency1/4: 12 arms,96 conditions,528 responses (48warmups,240full-token checks,240timed SSE-text checks). All within-route checks pass. Independent raw auditing verifies exact warmup/condition identities, actual tokens/text, resource counts and timing arithmetic. All16 whole/stream reference pairs match. Eleven selfchecks include corrupt responses, duplicate warmups/arms, absent artifacts, wrong resources and invalid throughput. All children and coordinator exit0; UID-scoped Pod deletion succeeds and a fresh query confirms absence.
+
+Native/PD equivalence is separate. Qwen0.6B and Qwen4B match4/4 reference arrays each. Gemma matches2/4 full arrays: its two128-input references first differ at index5 after shared EOS index2, with EOS-inclusive prefixes identical. Both8K cases match. KONI's32-output references match, but128-output references differ before EOS at index40(input128) and37(input8192); no configured EOS appears within those128 tokens. These differences remain failures of full native equivalence, not transport-mode regressions or semantic quality passes.
+
+## Timing samples
+
+At input8192/output128/c4 (512 generated tokens in each timed batch):
+
+| Model | Native tok/s | Whole tok/s | Stream tok/s | Native/PD references equal |
+|---|---:|---:|---:|---:|
+| qwen06b | 50.65 | 33.11 | 44.24 | 4/4 |
+| qwen4b | 12.30 | 12.72 | 14.47 | 4/4 |
+| gemma | 57.68 | 40.19 | 43.87 | 2/4 |
+| koni | 7.78 | 9.32 | 10.44 | 2/4 |
+
+One timed batch per cell, ordered native then whole then stream. Stream is faster than whole in these cells, with identical whole/stream references. Native uses fewer accelerators; KONI additionally has different native/PD outputs, so its native comparison is not an equivalent-output speedup. These timings do not establish stable gains or device DMA overlap.
+
+Single-client8K/128 measurements (native / whole / stream):
+
+| Model | First SSE (s) | Estimated TPOT (ms) |
+|---|---:|---:|
+| qwen06b | 1.089 / 2.420 / 1.554 | 11.26 / 11.31 / 11.32 |
+| qwen4b | 4.970 / 4.601 / 3.219 | 42.76 / 42.74 / 42.85 |
+| gemma | 0.806 / 1.778 / 1.516 | 10.94 / 10.56 / 10.78 |
+| koni | 7.302 / 4.600 / 2.702 | 72.04 / 71.87 / 71.72 |
+
+Most observed route latency differences occur before first SSE, while estimated TPOT is similar. For Qwen4B,8K first SSE drops4.970→3.219s from native to stream; the earlier2K comparison instead favored native1.022s over stream2.534s. This suggests input length matters for route selection, but repeat measurements and resource cost are required before setting a threshold. SSE may coalesce tokens; these intervals do not isolate producer compute, D2H, packing, transport, import or queuing. The earlier2K and this8K runs used different assigned physical ATOMs; cross-run deltas are not solely attributable to prompt length.
+
+## Reproduction and evidence
+
+Use the archived benchmark267_deadline.py and atom_server265_http.py configurations, context9216, producer width8192, caches off, one decode lane and maximum output128. Qwen and corrected Gemma use3 KV blocks; KONI uses1. Gemma uses the research global225 hybrid artifact, not a generally supported export. Metadata hashes and compiled filenames/sizes are in the existing `../evidence265/atom-mid201-artifact-metadata.json`; they are not full compiled-binary hashes. Exact runtime source hashes, per-arm raw hashes, all condition timings and EOS differences are in `../evidence265/atom-edge201-final-summary.json`.
+
+```bash
+"$PYTHON" "$HARNESS" --model "$MODEL" --mode "$MODE" --server-config "$CONFIG" --lengths 128 8192 --outputs 32 128 --concurrency 1 4 --slots 1 --repeats 1 --startup-timeout 1800 --request-timeout 600 --output "$NEW_RESULT"
+# MODE=native for ATOM alone, pd for whole/stream.
+# Stream config adds --stream-layers; add --reference "$WHOLE_RESULT" to the harness.
+python ../evidence265/audit_atom_edge201.py "$ARCHIVED_ARTIFACT_DIRECTORY"
+python ../evidence265/selfcheck_atom_edge201.py "$ARCHIVED_ARTIFACT_DIRECTORY"
+```
+
+Archive inputs: edge201-atom1030-queue.json; edge201-atom-final-{model}-{native,whole,stream}.json; mid201-atom-final-artifact-metadata.json; mid201-atom-final-koni-eos.json. Historical stream265 c16 raw controls are optional and remain unavailable rather than passing when absent. Model aliases: qwen06b/qwen4b/gemma/koni. Site-local commands, device bindings and full logs remain in the private raw archive.
+
+## Remaining scope
+
+Cache-on, genuinely supported model export paths, semantic quality, repeated performance and current-main integration remain separate. The earlier native numerical/quality counterexamples are not resolved by this fixed-prompt sweep. #201 remains open. After verified cleanup, a separate Qwen ATOM cache experiment was allocated; its results are not included here.
diff --git a/opt_prime/examples/pd/http_matrix/experiments/2026-10-30-atom-mid201-final.md b/opt_prime/examples/pd/http_matrix/experiments/2026-10-30-atom-mid201-final.md
new file mode 100644
index 00000000..b784ccfc
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/experiments/2026-10-30-atom-mid201-final.md
@@ -0,0 +1,48 @@
+# ATOM intermediate-length HTTP comparison with final HTTP module
+
+- Date: 2026-10-30; related #201, #265 and PR266.
+- HTTP source: PR266 a3497e5, SHA256 `e3449979b88d03edb6d5bf0d402a708e5d98054fdf203e22b480f3d7e6fd759a`.
+- Engine: historical stream265 B1 engine and research RNGD producer, with the final HTTP module explicitly loaded. This does not qualify the entire current-main runtime or vendor native furiosa-llm.
+- Hardware: one ATOM decode lane; PD additionally uses one RNGD on the same host. Native uses fewer accelerators. c4 means four queued clients, not hardware batch four.
+- Decoder: torch2.9.1+cpu, Transformers4.57.6, optimum-rbln0.10.2, rebel-compiler0.11.0. Producer: torch2.10.0+cpu, Transformers5.19.0, furiosa-torch2026.4.0.
+
+## Results
+
+All four selected checkpoints complete native, whole and stream routes at input512/2048, output32/128 per request, concurrency1/4: 12 arms, 96 conditions, 528 responses (48 warmups, 240 retained-token comparisons and 240 timed SSE-text comparisons). The independent auditor checks actual saved arrays/text, counts, condition identities, resource metadata and timing arithmetic. All children and the coordinator exit0. UID-scoped Pod cleanup exits0; a fresh cluster query confirms the owned Pod is absent.
+
+Whole/stream references match4/4 for each model. Native equivalence is separate. At input2048, output128 per request, c4 (512 output tokens total):
+
+| Model | Native tok/s | Whole tok/s | Stream tok/s | Native/PD references equal |
+|---|---:|---:|---:|---:|
+| qwen06b | 110.71 | 47.75 | 63.00 | 2/4 |
+| qwen4b | 21.55 | 15.35 | 17.16 | 2/4 |
+| gemma | 80.97 | 43.08 | 43.52 | 4/4 |
+| koni | 12.41 | 10.45 | 10.86 | 4/4 |
+
+These are single-batch measurements, ordered native then whole then stream. They do not establish a stable speedup or device DMA overlap. Native remains faster in this cell for all four models.
+
+Qwen0.6B differs from native at the second token for both512-input cases, before EOS; both2K cases match. Qwen4B's32-token cases match, but128-token cases first differ at index50(input512) and61(input2048), before either configured EOS. Gemma and KONI references match4/4 across all three routes here. Gemma reaches EOS at index2 in all four references; fixed-length suffix equality is not answer-quality evidence. Prior quality and numerical counterexamples remain unresolved.
+
+The single-client2K/128 comparison localizes most added PD latency before the first SSE. For Qwen4B, native/whole/stream first-SSE times are1.022/3.411/2.534s while estimated TPOT is38.77/38.58/38.60ms. Four-model details are in `../evidence265/atom-mid201-single-client-latency.json`. SSE can coalesce tokens; these timings do not separate producer compute, conversion, transport, import or scheduling.
+
+## Reproduction and verification
+
+Use the archived benchmark267_deadline.py and atom_server265_http.py configurations with context9216, compiled producer width8192, caches off, one ATOM lane, maximum output128. Qwen and corrected Gemma use three KV blocks; KONI uses one. Gemma uses the research global225 corrected artifact, not a generally supported export. Small config hashes and compiled filenames/sizes are retained in `../evidence265/atom-mid201-artifact-metadata.json`; these are not full compiled-binary hashes. Exact source hashes are in the summary.
+
+```bash
+"$PYTHON" "$HARNESS" --model "$MODEL" --mode "$MODE" --server-config "$CONFIG" --lengths 512 2048 --outputs 32 128 --concurrency 1 4 --slots 1 --repeats 1 --startup-timeout 1800 --request-timeout 600 --output "$NEW_RESULT"
+# MODE=native for ATOM alone, pd for whole/stream.
+# Stream config adds --stream-layers; add --reference "$WHOLE_RESULT" to the harness.
+python3 evidence265/audit_atom_mid201.py "$ARTIFACT_DIR"
+python3 evidence265/selfcheck_atom_mid201.py "$ARTIFACT_DIR"
+```
+
+Site-local configs, raw results and the queue retain exact argv, checkpoint paths, source/config hashes, Pod UID and exits. Raw names are mid201-atom-final-{model}-{native,whole,stream}.json; queue is mid201-atom-final1030-queue.json. The auditor also uses retained artifact/EOS metadata and optional historical references. Missing historical controls are recorded as unavailable, never as matching. Reproduction requires access to these research artifacts; site-local paths and logs are not published.
+
+Ten auditor selfchecks cover corrupt warmup/token/SSE data, missing conditions/raw, bad resource/timing metadata, duplicate arms, absent historical controls and request/aggregate output-length separation. Correctness checks use actual arrays rather than success flags alone.
+
+## Limits and next work
+
+This completes this cache-off intermediate-length B1 comparison only. It does not certify cache-on behavior, B4 support, native vendor engine availability or general answer quality. Supported Gemma export and Qwen4 B4 integration remain separate owner work. Short/long input128/8192 at c1/c4 still need matched ATOM controls.
+
+Existing prefill_s includes source computation plus conversion/SHM publication. Subprocess transfer_s is left at zero, so it cannot establish zero transfer cost. Future profiling must retain separate source/publication/receiver/import/first-emit intervals and account for overlap rather than summing concurrent durations.
diff --git a/opt_prime/examples/pd/http_matrix/experiments/2026-10-30-native-gpu-edge201.md b/opt_prime/examples/pd/http_matrix/experiments/2026-10-30-native-gpu-edge201.md
new file mode 100644
index 00000000..b198e410
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/experiments/2026-10-30-native-gpu-edge201.md
@@ -0,0 +1,36 @@
+# Native GPU short/long low-concurrency HTTP coverage
+
+- Date: 2026-10-30; related #201,#265 and PR266.
+- Runtime:162c2f6c969a876318c7723973e21289d302e9da, branch defaultHTTP. Production sources unchanged from the preceding default201 run; source hashes in the evidence summary.
+- Hardware:one A10080GB PCIe on the GPU host; driver580.178.04,torch2.13.0+cu130,Transformers5.15.0. No RNGD/ATOM device used.
+
+## Results
+
+All four selected checkpoints complete input128/8192, output32/128 per request, concurrency1/4: 32 conditions, 176 responses (16 warmups, 80 retained-token checks, 80 timed SSE-text checks). Actual saved arrays/text,lengths,metadata and timing arithmetic independently audited. All 16 reference arrays match the corresponding historical c16 native references. All fourchild exits0; coordinator exits0; fresh compute-process query empty after completion.
+
+At8192 input tokens,128 output tokens per request, c4 (512 total output tokens):
+
+|Model|Aggregate tok/s|Median first SSE(s)|Median E2E(s)|
+|---|---:|---:|---:|
+|qwen06b|60.84|3.348|7.428|
+|qwen4b|40.92|5.408|11.274|
+|gemma|58.83|3.270|7.542|
+|koni|40.46|5.848|11.603|
+
+One timed batch per condition, not stable speedup evidence. These are native GPU controls; they do not certify heterogeneous native equivalence, answer quality or cache-on behavior. Fixed-length output ignores EOS. FirstSSE and estimatedTPOT are client metrics; SSE can coalesce tokens.
+
+## Reproduction
+
+Use benchmark201_native_http.py with each selected frozen checkpoint and the default serve_batched.py/llmd.py modules. Configs are identical to the default201 middle-length controls: BF16, four decoder slots, paged KV/page64, chunk256, context9216, caches off, one GPU. Use an idle allocated GPU; stop if another workload occupies it,never terminate unrelated processes.
+
+```bash
+PYTHONPATH="$RUNTIME/opt_prime:$RUNTIME/opt_prime/examples/pd" OMP_NUM_THREADS=4 MKL_NUM_THREADS=4 "$PYTHON" "$HARNESS" --model "$MODEL" --mode native --server-config "$NATIVE_CONFIG" --lengths 128 8192 --outputs 32 128 --concurrency 1 4 --repeats 1 --request-timeout 180 --output "$NEW_RESULT"
+python3 evidence265/audit_gpu_edge201.py "$ARTIFACT_DIR"
+python3 evidence265/selfcheck_gpu_edge201.py "$ARTIFACT_DIR"
+```
+
+Raw files are edge201-{model}-gpu-native.json;queue edge201-gpu-queue.json preserves exact argv/config/source hashes and exitstatuses. The auditor also requires matching stream265 c16 native controls and default201 configs from the same artifact archive. Full site-local paths/configs/logs are not published. The portable auditor accepts an artifact directory; incomplete queue state is explicitly retained rather than treated as complete.
+
+## Verifier checks
+
+Eight selfchecks pass,including duplicate complete arms, corrupted warmup/token/SSE data, wrong throughput, missing conditions and missing raw results. The duplicate/warmup negative cases initially reproduced verifier defects; fixed the unique planned-model set check and actual warmup comparison. Prior GPU middle/default/PD raw results were re-audited successfully with the same fixes. Request length and aggregate output tokens remain separate fields. No production engine change.
diff --git a/opt_prime/examples/pd/http_matrix/experiments/2026-10-30-pd-gpu-edge201.md b/opt_prime/examples/pd/http_matrix/experiments/2026-10-30-pd-gpu-edge201.md
new file mode 100644
index 00000000..f2ac115e
--- /dev/null
+++ b/opt_prime/examples/pd/http_matrix/experiments/2026-10-30-pd-gpu-edge201.md
@@ -0,0 +1,42 @@
+# RNGD-to-GPU short/long HTTP controls
+
+- Date: 2026-10-30; related #201, #265 and PR266.
+- Runtime: ef8cc0974ca74b950fe236c2bebaaba4cd276d16, direct serve_batched.py with the default HTTP module.
+- Hardware: one A10080GB PCIe and one RNGD on the GPU host. The native control uses one GPU and no RNGD.
+- Decoder: torch2.13.0+cu130, Transformers5.15.0, driver580.178.04. Producer: torch2.10.0+cpu, Transformers5.8.1, furiosa-torch2026.4.0. The producer environment differs from the separate RNGD-to-ATOM run.
+
+## Results
+
+All four selected models complete whole/stream controls at input128/8192, output32/128 per request, concurrency1/4: 8 arms, 64 conditions and352 responses (32 warmups,160 retained-token checks,160 timed SSE-text checks). The independent auditor checks actual arrays/text, lengths, condition identities, resource metadata and timing arithmetic. Whole/stream references match4/4 per model. All children and the coordinator exit0. Fresh queries confirm no GPU compute processes and no holders on the selected local RNGD device files; the separate remote ATOM sweep remains active.
+
+At input8192, output128 per request and c4 (512 output tokens total):
+
+| Model | Native tok/s | Whole tok/s | Stream tok/s | Native/PD references equal |
+|---|---:|---:|---:|---:|
+| qwen06b | 60.84 | 63.48 | 75.41 | 4/4 |
+| qwen4b | 40.92 | 30.34 | 36.29 | 2/4 |
+| gemma | 58.83 | 55.63 | 56.54 | 4/4 |
+| koni | 40.46 | 30.79 | 39.71 | 2/4 |
+
+The native controls are the same-day edge201 GPU runs. These are single timed batches, with whole before stream and native controls run earlier. PD adds an RNGD, so these are not equal-resource efficiency or stable-speedup claims. The Qwen0.6B long-input point favors streaming PD, while its128-input point favors native (184.53 vs91.54 tok/s at output128/c4). The other long-input throughput points do not exceed native. Median E2E and aggregate throughput can rank differently; all per-condition first-SSE/E2E/estimated-TPOT metrics are retained in the comparison JSON.
+
+Qwen4B matches native for both128-input references, but both8K references first differ at index12 before EOS. KONI matches both32-output references; its128-output references first differ at index64(input128) and98(input8192), before EOS128001/128009. Qwen0.6B and Gemma match native4/4 here. Gemma reaches EOS at index2 for input128 and has no EOS within the measured8K outputs. Fixed-length equality does not establish general answer quality or negate earlier numerical/quality counterexamples. Historical same-route c16 references also match4/4 for every arm.
+
+## Reproduction
+
+Use the same frozen checkpoints and research source wrapper as the preceding pdmid201 run: compiled width8192, fusion4/Mesh2, BF16 decoder, four decode slots, context9216, paged KV/page64, local chunk256, both caches off. This is the research RNGD engine, not vendor native furiosa-llm.
+
+```bash
+PYTHONPATH="$RUNTIME/opt_prime:$RUNTIME/opt_prime/examples/pd:$RNGD_SOURCE:$SERVING_MATRIX" OMP_NUM_THREADS=4 MKL_NUM_THREADS=4 TORCH_DEVICE_BACKEND_AUTOLOAD=0 HF_HUB_OFFLINE=1 TRANSFORMERS_OFFLINE=1 "$PYTHON" "$HARNESS" --model "$MODEL" --mode pd --server-config "$CONFIG" --lengths 128 8192 --outputs 32 128 --concurrency 1 4 --repeats 1 --startup-timeout 1800 --request-timeout 180 --output "$NEW_RESULT"
+# Stream config adds --pd-prefill-stream-layers; add --reference "$WHOLE_RESULT" to the harness.
+python3 evidence265/audit_pd_gpu_edge201.py "$ARTIFACT_DIR"
+python3 evidence265/selfcheck_pd_gpu_edge201.py "$ARTIFACT_DIR"
+```
+
+Use benchmark201_native_http.py and an idle allocated GPU/RNGD. The coordinator stops rather than modifying another workload if the GPU process query is nonempty or the selected RNGD has holders. Exact argv, config/source hashes, versions, exits and device identity are retained in pdedge201-gpu-queue.json. Raw names are pdedge201-{model}-{whole,stream}.json; the auditor also requires edge201 native and matching historical stream265 c16 controls. Site-local paths/configs/logs are not published. Summary and raw hashes are in `../evidence265/pdedge201-gpu-summary.json`; native comparison and EOS positions are in `../evidence265/pdedge201-gpu-comparison.json`.
+
+## Verification and limits
+
+Ten selfchecks cover a valid partial subset, request/aggregate output-length separation, duplicate complete arms, corrupt warmup/token/SSE/throughput/resource fields, missing conditions and missing raw data. No raw measurements or production engine behavior changed.
+
+This completes these cache-off short/long HTTP controls only. Native equivalence, answer quality, cache-on behavior, supported exports and reliable performance under repeated load remain separate. CPU/client timestamps do not prove device DMA overlap. The full four-model goal is not complete.
diff --git a/opt_prime/examples/pd/serve_batched.py b/opt_prime/examples/pd/serve_batched.py
index 7390fd2f..bc4c898b 100644
--- a/opt_prime/examples/pd/serve_batched.py
+++ b/opt_prime/examples/pd/serve_batched.py
@@ -29,6 +29,9 @@
 
 def prefill_worker(a, slots, config):
     """the PD prefill side from the flags, or None (aggregated serving)"""
+    stream_layers = getattr(a, "pd_prefill_stream_layers", False)
+    if stream_layers and (a.backend not in ("torch", "atom") or not a.pd_prefill_python or a.pd_prefill_device):
+        raise ValueError("PD layer streaming requires a subprocess producer and Torch or ATOM decoder")
     overlap = getattr(a, "pd_prefill_copy_overlap", False)
     if overlap and (not a.pd_prefill_device or a.backend != "torch" or
                     torch.device(a.pd_prefill_device).type != "cuda" or torch.device(a.device).type != "cuda"):
@@ -52,7 +55,7 @@ def prefill_worker(a, slots, config):
         return SubprocessPrefillWorker(a.pd_prefill_python, shlex.split(a.pd_prefill_args) + ["--model", a.model],
                                        slots=slots, layers=c.num_hidden_layers, kv_heads=c.num_key_value_heads,
                                        head_dim=getattr(c, "head_dim", None) or c.hidden_size // c.num_attention_heads,
-                                       max_context=a.max_context, env=env)
+                                       max_context=a.max_context, env=env, stream_layers=stream_layers)
     return None
 
 
@@ -71,6 +74,10 @@ def remote_tier(a, model):
 
 
 def build(a):
+    prefetch = getattr(a, "pd_prefetch_next", False)
+    if prefetch and (a.backend != "atom" or a.slots != 1 or not a.pd_remote_only or
+                     not (a.pd_prefill_device or a.pd_prefill_python)):
+        raise ValueError("next prefill requires single-lane ATOM remote-only PD")
     tok = AutoTokenizer.from_pretrained(a.model)
     events = None
     if a.kv_events:
@@ -92,7 +99,7 @@ def build(a):
         engine.start(a.model)
         b, model = engine.batcher(prefix_cache=a.prefix_cache, host_kv_gib=a.kv_host_memory_gb, kv_events=events,
                                   prefill_worker=prefill_worker(a, a.slots, engine.model.config),
-                                  local_prefill=not a.pd_remote_only), engine.model
+                                  local_prefill=not a.pd_remote_only, remote_prefetch=prefetch), engine.model
     else:
         model = AutoModelForCausalLM.from_pretrained(a.model, dtype=getattr(torch, a.dtype)).to(a.device).eval()
         kw = dict(slots=a.slots, max_context=a.max_context, kv=a.kv, page_size=a.page_size,
@@ -137,8 +144,12 @@ def build(a):
     ap.add_argument("--pd-prefill-python", default=None, help="PD: prefill_server under this interpreter")
     ap.add_argument("--pd-prefill-args", default="--engine torch --device cpu")
     ap.add_argument("--pd-prefill-pythonpath", default=None)
+    ap.add_argument("--pd-prefill-stream-layers", action="store_true",
+                    help="experimental: import layer KV during capable subprocess prefill (Torch or ATOM decoder)")
     ap.add_argument("--pd-remote-only", action="store_true",
                     help="PD on ATOM: every prompt is prefilled by the PD prefill side (no cost routing to ATOM)")
+    ap.add_argument("--pd-prefetch-next", action="store_true",
+                    help="B1 ATOM remote-only PD: prefill one queued prompt while the current request decodes")
     ap.add_argument("--remote-kv", default=None, help="JSON with Mooncake store setup arguments (shared KV, #128)")
     ap.add_argument("--remote-kv-keys", default="lmcache", choices=["lmcache", "optimus"])
     ap.add_argument("--remote-kv-model", default=None, help="model name in the keys (the vLLM/LMCache side's name)")
diff --git a/opt_prime/examples/pd/streaming_factories.py b/opt_prime/examples/pd/streaming_factories.py
new file mode 100644
index 00000000..19b9c626
--- /dev/null
+++ b/opt_prime/examples/pd/streaming_factories.py
@@ -0,0 +1,97 @@
+"""Opt-in streaming adapters for the validated four-model research producers.
+
+Requires the existing serving_matrix modules and Qwen prefill module on PYTHONPATH.
+Factories preserve each producer's whole-result path for same-engine A/B controls.
+Use --engine factory --engine-factory streaming_factories:qwen (or gemma/koni).
+"""
+import torch
+
+
+class Streaming:
+    def __init__(self, engine, stream):
+        self.engine, self.stream = engine, stream
+
+    def prefill(self, tokens):
+        return self.engine.prefill(tokens)
+
+    @torch.inference_mode()
+    def prefill_stream(self, tokens, emit):
+        return self.stream(self.engine, tokens, emit)
+
+
+def _ids(engine, tokens):
+    ids = list(tokens)
+    if not 0 < len(ids) <= engine.width or any(type(t) is not int or t < 0 for t in ids):
+        raise ValueError('invalid tokens or prompt exceeds compiled width')
+    return ids
+
+
+def qwen_stream(e, tokens, emit):
+    ids = _ids(e, tokens)
+    n = len(ids)
+    x = e.embedding[ids + [ids[-1]] * (e.width - n)].to(torch.bfloat16).to(e.device)
+    for i, layer in enumerate(e.layers):
+        x, kv = e.module(x, *e.fixed, *layer, e.rotation, e.mask)
+        k = kv[0].cpu()[..., :n].transpose(-1, -2).unsqueeze(0)
+        v = kv[1].cpu()[..., :n].transpose(-1, -2).unsqueeze(0)
+        emit(i, 0, k, v)
+    e.sync(e.chip)
+    z = x.cpu()[n - 1].float()
+    z = z * torch.rsqrt(z.square().mean() + e.s['rms_norm_eps']) * e.norm
+    return z @ e.head.T
+
+
+def gemma_stream(e, tokens, emit):
+    ids = _ids(e, tokens)
+    from gemma_parts import initial_hidden, final_logits
+    n = len(ids)
+    if any(t >= e.config['vocab_size'] for t in ids):
+        raise ValueError('invalid token ID')
+    pad = e.config.get('pad_token_id')
+    x = initial_hidden(e.weights['model.embed_tokens.weight'], ids + [0 if pad is None else pad] * (e.width - n)).to(e.device)
+    for i, layer in enumerate(e.params):
+        x, k, v = e.compiled(x, *layer, *e.positions[e.local[i]])
+        emit(i, 0, k.cpu()[:n].permute(1, 0, 2).unsqueeze(0),
+             v.cpu()[:n].permute(1, 0, 2).unsqueeze(0))
+    e.sync(e.chip)
+    return final_logits(x.cpu()[n - 1], e.weights['model.norm.weight'],
+                        e.weights.get('lm_head.weight', e.weights['model.embed_tokens.weight']), e.config['rms_norm_eps'])
+
+
+def koni_stream(e, tokens, emit):
+    ids = _ids(e, tokens)
+    n = len(ids)
+    if any(t >= e.config['vocab_size'] for t in ids):
+        raise ValueError('invalid token ID')
+    stage = e.engine.begin_device_prefill(ids)
+    layer = 0
+    while True:
+        done = stage.advance()
+        k = stage.Ks[-1].cpu()[..., :n].transpose(-1, -2)
+        v = stage.Vs[-1].cpu()[:, :, :n]
+        emit(layer, 0, k, v)
+        layer += k.shape[0]
+        if done:
+            break
+    e.sync(e.chip)
+    z = stage.x.cpu()[n - 1].float()
+    z = z * torch.rsqrt(z.square().mean() + e.config['rms_norm_eps']) * e.norm.float()
+    return z @ e.head.float().T
+
+
+def qwen(config, weights, width, chip=0):
+    from long_width_variant import make_prefill
+    from rngd_pd.engines.fsdk import sync
+    engine = make_prefill(config, weights, width, chip)
+    engine.sync = sync
+    return Streaming(engine, qwen_stream)
+
+
+def gemma(config, weights, width, chip=0):
+    from gemma_prefill import Prefill
+    return Streaming(Prefill(config, weights, width, chip), gemma_stream)
+
+
+def koni(config, weights, width, chip=0):
+    from koni_prefill import Prefill
+    return Streaming(Prefill(config, weights, width, chip), koni_stream)
diff --git a/opt_prime/opt_prime/accel/pd/layer_progress.py b/opt_prime/opt_prime/accel/pd/layer_progress.py
new file mode 100644
index 00000000..a13fd219
--- /dev/null
+++ b/opt_prime/opt_prime/accel/pd/layer_progress.py
@@ -0,0 +1,63 @@
+"""Validated, bounded-by-coverage notifications for a reserved KV slab.
+
+This tracks source writes only. A consumer must finish its own transfers before
+publishing the cache or releasing the reservation; source completion is not a fence.
+"""
+from threading import Lock
+
+
+class LayerProgress:
+    def __init__(self, layers, start, end):
+        if any(type(x) is not int for x in (layers,start,end)) or layers<=0 or not 0<=start<=end:
+            raise ValueError('invalid layer-progress geometry')
+        self.layers,self.start,self.end=layers,start,end
+        self._coverage=[[] for _ in range(layers)]
+        self._pending=[]
+        self._closed=False
+        self._lock=Lock()
+
+    def publish(self, layer_start, layer_end, token_start, token_end):
+        span=(layer_start,layer_end,token_start,token_end)
+        with self._lock:
+            if self._closed:
+                raise ValueError('layer progress is closed')
+            if (any(type(x) is not int for x in span)
+                or not 0<=layer_start<layer_end<=self.layers
+                or not self.start<=token_start<token_end<=self.end):
+                raise ValueError('invalid layer-progress span')
+            for layer in range(layer_start,layer_end):
+                if any(token_start<b and a<token_end for a,b in self._coverage[layer]):
+                    raise ValueError('overlapping layer-progress span')
+            # Validate the complete message before changing any layer's coverage.
+            for layer in range(layer_start,layer_end):
+                self._coverage[layer].append((token_start,token_end))
+            self._pending.append(span)
+
+    def drain(self):
+        with self._lock:
+            pending,self._pending=self._pending,[]
+            return pending
+
+    @property
+    def pending(self):
+        with self._lock:
+            return bool(self._pending)
+
+    @property
+    def finished(self):
+        with self._lock:
+            return self._closed
+
+    def finish(self):
+        with self._lock:
+            if self._closed:
+                raise ValueError('layer progress is closed')
+            for intervals in self._coverage:
+                position=self.start
+                for start,end in sorted(intervals):
+                    if start!=position:
+                        raise ValueError('incomplete layer-progress coverage')
+                    position=end
+                if position!=self.end:
+                    raise ValueError('incomplete layer-progress coverage')
+            self._closed=True
diff --git a/opt_prime/opt_prime/accel/pd/paged.py b/opt_prime/opt_prime/accel/pd/paged.py
index 63f10330..f766fb0d 100644
--- a/opt_prime/opt_prime/accel/pd/paged.py
+++ b/opt_prime/opt_prime/accel/pd/paged.py
@@ -445,16 +445,64 @@ def span_index(self, slot, start, end):
         return self.physical([slot] * (end - start), torch.arange(start, end, device=self.device))
 
     def write_span(self, slot, start, keys, values):
-        """keys/values [layers, kv_heads, n, head_dim] for positions start .. start+n of `slot` (a list of those, one
-        per KV group, for hybrid models), each group in one indexed copy (the PD admission of KV that another engine
-        prefilled)"""
-        keys = keys if isinstance(keys, (list, tuple)) else [keys]
-        values = values if isinstance(values, (list, tuple)) else [values]
-        idx = self.span_index(slot, start, start + keys[0].shape[2])
+        """Import [layers, kv_heads, n, head_dim] KV at `start`: either model-layer order for uniform geometry,
+        or one tensor per receiver group. Hybrid dense inputs are split by model-layer indices; grouped inputs
+        also support differing head geometries. Validate all shapes before copying any group."""
+        if isinstance(keys, torch.Tensor) and isinstance(values, torch.Tensor) and len(self.groups) == 1:
+            keys, values = [keys], [values]
+        if isinstance(keys, torch.Tensor) and isinstance(values, torch.Tensor):
+            layers = max(self.layer_group) + 1
+            if keys.ndim != 4 or values.shape != keys.shape or keys.shape[0] != layers:
+                raise ValueError("dense KV span must contain every model layer with matching K/V shapes")
+            # Subprocess producers export model-layer order; pools group attention geometries.
+            keys = [keys if g.layers == tuple(range(layers)) else keys[list(g.layers)] for g in self.groups]
+            values = [values if g.layers == tuple(range(layers)) else values[list(g.layers)] for g in self.groups]
+        if not isinstance(keys, (list, tuple)) or not isinstance(values, (list, tuple)):
+            raise ValueError("KV spans must both be dense tensors or grouped sequences")
+        if len(keys) != len(self.groups) or len(values) != len(self.groups):
+            raise ValueError("KV span group count must match the receiver")
+        n = None
+        for g, k, v in zip(self.groups, keys, values):
+            if not isinstance(k, torch.Tensor) or not isinstance(v, torch.Tensor) or k.ndim != 4:
+                raise ValueError("each KV group must contain four-dimensional tensors")
+            n = k.shape[2] if n is None else n
+            if k.shape != (len(g.layers), g.kv_heads, n, g.head_dim) or v.shape != k.shape:
+                raise ValueError("KV span shapes must match receiver groups and share a token count")
+        idx = self.span_index(slot, start, start + n)
         for pk, pv, k, v in zip(self.gk, self.gv, keys, values):
             pk[:, :, idx] = k.to(self.device, pk.dtype, non_blocking=True)
             pv[:, :, idx] = v.to(self.device, pv.dtype, non_blocking=True)
 
+    def write_layer_span(self, slot, start, layer_start, keys, values):
+        """Import a dense model-layer subset into reserved pages, without publishing it.
+
+        The caller owns the reservation until all transfers finish. Length, prefix
+        admission and recurrent state are deliberately unchanged by partial writes.
+        """
+        if (any(type(x) is not int for x in (slot, start, layer_start))
+                or not 0 <= slot < len(self.owned) or start < 0 or layer_start < 0
+                or not isinstance(keys, torch.Tensor) or not isinstance(values, torch.Tensor)
+                or keys.ndim != 4 or values.shape != keys.shape
+                or keys.shape[0] == 0 or keys.shape[2] == 0):
+            raise ValueError("invalid partial KV span")
+        if start < self.length[slot]:
+            raise ValueError("partial KV span cannot overwrite an admitted prefix")
+        end = start + keys.shape[2]
+        if end > min(self.max_context, len(self.owned[slot]) * self.page_size):
+            raise ValueError("partial KV span exceeds reserved pages")
+        layers = range(layer_start, layer_start + keys.shape[0])
+        for layer in layers:
+            if layer not in self.layer_group:
+                raise ValueError("partial KV span contains an unknown attention layer")
+            group = self.groups[self.layer_group[layer][0]]
+            if keys.shape[1] != group.kv_heads or keys.shape[3] != group.head_dim:
+                raise ValueError("partial KV geometry does not match receiver group")
+        idx = self.span_index(slot, start, end)
+        for offset, layer in enumerate(layers):
+            pk, pv = self.layer_kv(layer)
+            pk[:, idx] = keys[offset].to(self.device, pk.dtype)
+            pv[:, idx] = values[offset].to(self.device, pv.dtype)
+
     def read_span(self, slot, start, end):
         """[layers, kv_heads, end - start, head_dim] keys and values of `slot` (the prefill side's export); a list per
         KV group for hybrid models"""
diff --git a/opt_prime/opt_prime/accel/pd/pd_batching.py b/opt_prime/opt_prime/accel/pd/pd_batching.py
index 281ec149..fbb435d8 100644
--- a/opt_prime/opt_prime/accel/pd/pd_batching.py
+++ b/opt_prime/opt_prime/accel/pd/pd_batching.py
@@ -22,7 +22,7 @@
 `platforms.atom.batching.AtomBatcher(prefill_worker=...)` decodes PD requests on ATOM (#123).
 """
 from collections import deque
-from concurrent.futures import Future, wait, FIRST_COMPLETED, ThreadPoolExecutor
+from concurrent.futures import Future, ThreadPoolExecutor
 from dataclasses import dataclass
 import queue
 import threading
@@ -35,6 +35,7 @@
 
 from opt_prime.accel.pd.batching import ContinuousBatcher, prefill_chunk
 from opt_prime.accel.pd.runtime import RequestCancelled
+from opt_prime.accel.pd.layer_progress import LayerProgress
 from opt_prime.accel.pd.sampling import select_token
 from opt_prime.accel.pd.torch_models import describe_model
 
@@ -323,13 +324,39 @@ def close(self):
         self._proc.join(timeout=30)
 
 
+class _KVProgress:
+    """Source coverage plus borrowed SHM views, valid for this request's reservation."""
+    def __init__(self, slab, start, end):
+        self.coverage = LayerProgress(slab.shape[1], start, end)
+        kv = torch.from_numpy(slab[:, :, :, :end - start].view("int16")).view(torch.bfloat16)
+        self.keys, self.values = kv[0], kv[1]
+        self.notify = lambda: None
+
+    def publish(self, *span):
+        self.coverage.publish(*span)
+        self.notify()
+
+    def drain(self):
+        start = self.coverage.start
+        return [(lo, first, self.keys[lo:hi, :, first - start:last - start],
+                 self.values[lo:hi, :, first - start:last - start])
+                for lo, hi, first, last in self.coverage.drain()]
+
+
 class SubprocessPrefillWorker:
     """`opt_prime.accel.pd.prefill_server` run by `python` (another environment); one KV slot per decode lane in a
     shared-memory slab. A result's keys/values are bfloat16 views of the lane's slot: valid until that lane is
-    submitted again, which the decode side does only after importing them."""
+    submitted again, which the decode side does only after importing them.
+
+    Opt-in layer streaming requires ready capability ``kv_progress: 1`` and messages
+    ``{id, kv_span: [layer_start, layer_end, token_start, token_end]}`` after each CPU write.
+    Token offsets are absolute; slab storage starts at send_from. Published spans must remain
+    immutable until completion. Producers must process requests serially, including cancelled
+    requests, so an old request cannot overwrite a reused slot after its successor starts.
+    """
 
     def __init__(self, python, engine_args, *, slots, layers, kv_heads, head_dim, max_context, env=None,
-                 startup_timeout=1800):
+                 startup_timeout=1800, stream_layers=False):
         import json, subprocess
         import numpy as np
         from multiprocessing import shared_memory
@@ -339,6 +366,7 @@ def __init__(self, python, engine_args, *, slots, layers, kv_heads, head_dim, ma
         cmd = [python, "-m", "opt_prime.accel.pd.prefill_server", "--shm", self._shm.name, "--slots", str(slots),
                "--layers", str(layers), "--kv-heads", str(kv_heads), "--head-dim", str(head_dim),
                "--max-context", str(max_context), *engine_args]
+        self.stream_layers = stream_layers
         self._json = json
         self._futures, self._next, self._lock = {}, 0, threading.Lock()
         self._write_lock = threading.Lock()
@@ -368,6 +396,10 @@ def submit(self, request, send_from=0, slot=None) -> Future:
         if slot is None or not 0 <= slot < self.slab.shape[0]:
             raise ValueError("SubprocessPrefillWorker needs a valid decode lane (slot)")
         f = Future()
+        if self.stream_layers and (type(send_from) is not int or
+                not 0 <= send_from <= len(request.input_ids) <= self.slab.shape[-2]):
+            f.set_exception(ValueError("invalid streamed KV suffix"))
+            return f
         s = request.sampling
         write_error = None
         # Keep pipe writes ordered without holding the response-map lock across blocking I/O.
@@ -376,14 +408,19 @@ def submit(self, request, send_from=0, slot=None) -> Future:
                 error = self._failed
                 key, self._next = self._next, self._next + 1
                 if error is None:
+                    if self.stream_layers:
+                        f.kv_progress = _KVProgress(self.slab[slot], send_from, len(request.input_ids))
                     self._futures[key] = (f, slot, send_from)
             if error is not None:
                 f.set_exception(error)
                 return f
             try:
-                line = self._json.dumps(dict(id=key, tokens=list(request.input_ids), send_from=send_from, slot=slot,
-                                             sampling=dict(temperature=s.temperature, top_p=s.top_p, top_k=s.top_k,
-                                                           seed=s.seed, stop_token_ids=list(s.stop_token_ids))))
+                message = dict(id=key, tokens=list(request.input_ids), send_from=send_from, slot=slot,
+                               sampling=dict(temperature=s.temperature, top_p=s.top_p, top_k=s.top_k,
+                                             seed=s.seed, stop_token_ids=list(s.stop_token_ids)))
+                if self.stream_layers:
+                    message["stream_layers"] = True
+                line = self._json.dumps(message)
                 self._proc.stdin.write(line + "\n")
                 self._proc.stdin.flush()
             except (OSError, ValueError, TypeError) as error:
@@ -413,11 +450,15 @@ def _receive(self, ready):
                 if not isinstance(msg, dict):
                     continue
                 if msg.get("ready"):
+                    if self.stream_layers and (type(msg.get("kv_progress")) is not int or msg["kv_progress"] != 1):
+                        raise ValueError("producer does not support KV progress version 1")
                     if not ready.done():
                         ready.set_result(True)
                     continue
                 if "id" not in msg:
                     continue                                  # structured SDK logs
+                if type(msg["id"]) is not int:
+                    raise ValueError("invalid prefill response ID")
                 with self._lock:
                     entry = self._futures.get(msg["id"])
                     failed = self._failed
@@ -426,6 +467,13 @@ def _receive(self, ready):
                 if entry is None:
                     raise ValueError("unknown response ID")
                 f, slot, start = entry
+                progress = getattr(f, "kv_progress", None)
+                if "kv_span" in msg:
+                    if (progress is None or set(msg) != {"id", "kv_span"}
+                            or not isinstance(msg["kv_span"], list) or len(msg["kv_span"]) != 4):
+                        raise ValueError("invalid KV progress message")
+                    progress.publish(*msg["kv_span"])
+                    continue
                 error = None
                 if "error" in msg:
                     error = RuntimeError(f"prefill subprocess: {msg['error']}")
@@ -433,6 +481,10 @@ def _receive(self, ready):
                     n = msg["n"]
                     if type(n) is not int or not 0 <= n <= self.slab.shape[-2]:
                         raise ValueError("invalid KV span length")
+                    if progress is not None:
+                        if n != progress.coverage.end - start:
+                            raise ValueError("streamed KV completion length differs from requested suffix")
+                        progress.coverage.finish()
                     kv = torch.from_numpy(self.slab[slot, :, :, :, :n].view("int16")).view(torch.bfloat16)
                     out = PrefillOut(msg["token"], start, kv[0], kv[1], msg.get("cached", 0),
                                      msg.get("prefill_s", 0.0))
@@ -522,7 +574,8 @@ def __init__(self, model, worker, **kw):
             raise ValueError("PD batching needs kv='paged'")
         super().__init__(model, **kw)
         self.worker = worker
-        self.stats.update(prefills=0, transferred_tokens=0, decode_cached_tokens=0, prefill_cached_tokens=0,
+        self._prefill_ready = threading.Event()
+        self.stats.update(progress_spans=0, prefills=0, transferred_tokens=0, decode_cached_tokens=0, prefill_cached_tokens=0,
                           prefill_s=0.0, transfer_s=0.0)
 
     def step(self):
@@ -540,6 +593,11 @@ def step(self):
                 r.slot = self.free.pop(0)
                 self.kv.length[r.slot] = r.prefilled = cached
                 r.pending = self.worker.submit(r.request, send_from=cached, slot=r.slot)   # cached blocks not sent
+                r.pending.add_done_callback(lambda _: self._prefill_ready.set())
+                progress = getattr(r.pending, "kv_progress", None)
+                if progress is not None:
+                    progress.notify = self._prefill_ready.set
+                self._prefill_ready.set()                    # include progress emitted before binding the notifier
                 self.prefilling.append(r)
         self._admit_prefilled(block=not self.decoding)
         if self.decoding:
@@ -549,28 +607,60 @@ def step(self):
             if events:
                 self.kv_events.publish(events)
 
+    @torch.inference_mode()
+    def _import_progress(self, r):
+        progress = getattr(r.pending, "kv_progress", None)
+        if progress is None:
+            return
+        if self.kv.rstate:
+            raise ValueError("layer-progress import does not support recurrent state")
+        touched = False
+        try:
+            for layer, start, keys, values in progress.drain():
+                touched = True
+                self.kv.write_layer_span(r.slot, start, layer, keys, values)
+                self.stats['progress_spans'] += 1
+        finally:
+            # Source slab reads and destination writes must finish before cancellation can reuse the pages.
+            if touched and torch.device(self.device).type == "cuda":
+                torch.cuda.current_stream(self.device).synchronize()
+
     def _admit_prefilled(self, block):
         pending = [r for r in self.prefilling if r.pending is not None]
         if not pending:
             return
-        if block:                                             # nothing to decode: wait for the first prefill
-            wait([r.pending for r in pending], timeout=0.05, return_when=FIRST_COMPLETED)
+        if block:
+            # Clear before inspecting state: a concurrent publication then either remains visible or wakes us.
+            self._prefill_ready.clear()
+            if not any(r.pending.done() or (getattr(r.pending, "kv_progress", None) is not None
+                       and r.pending.kv_progress.coverage.pending) for r in pending):
+                self._prefill_ready.wait(timeout=0.05)
         for r in pending:
-            if not r.pending.done():
-                continue
             with self._lock:
-                if r not in self.prefilling:                  # cancelled meanwhile: lane already released
+                if r not in self.prefilling:
                     continue
-                self.prefilling.remove(r)
             try:
-                out = r.pending.result()
+                if r.pending.done():
+                    out = r.pending.result()                 # failures never import remaining notifications
+                else:
+                    self._import_progress(r)
+                    continue
+                # Drain after observing completion, including progress published between the last drain and result.
+                self._import_progress(r)
+                progress = getattr(r.pending, "kv_progress", None)
+                if progress is not None and not progress.coverage.finished:
+                    raise RuntimeError("streamed KV coverage was not validated before completion")
+                with torch.inference_mode():
+                    if progress is None:
+                        self.kv.write_span(r.slot, out.start, out.keys, out.values)
             except BaseException as error:
                 with self._lock:
+                    self.prefilling.remove(r)
                     self._finish(r, error)
                 continue
+            with self._lock:
+                self.prefilling.remove(r)
             ids = r.request.input_ids
-            with torch.inference_mode():
-                self.kv.write_span(r.slot, out.start, out.keys, out.values)
             self.kv.length[r.slot] = r.prefilled = len(ids)
             self.kv.commit_prompt(r.slot)
             if self.kv.rstate:
diff --git a/opt_prime/opt_prime/accel/pd/prefill_server.py b/opt_prime/opt_prime/accel/pd/prefill_server.py
index 5595f51e..0816fb68 100644
--- a/opt_prime/opt_prime/accel/pd/prefill_server.py
+++ b/opt_prime/opt_prime/accel/pd/prefill_server.py
@@ -100,16 +100,121 @@ def prefill(self, tokens):
         stage = self.engine.begin_device_prefill(list(tokens))
         while not stage.advance(self.engine.ngroups):
             pass
-        logits = torch.from_numpy(self.engine._logits(stage.x, n))
+        logits = self._last_logits(stage.x, n)
         # per layer group: keys [NG, kv_heads, head_dim, C], values [NG, kv_heads, C, head_dim]
         keys = torch.cat([k.cpu()[..., :n] for k in stage.Ks]).transpose(-1, -2)
         values = torch.cat([v.cpu()[:, :, :n] for v in stage.Vs])
         return keys, values, logits
 
+    def prefill_stream(self, tokens, emit):
+        """Export each completed group before scheduling the next group."""
+        import torch
+        n = len(tokens)
+        if not 0 < n <= self.widths[-1]:
+            raise ValueError("prompt exceeds compiled capacity or is empty")
+        stage = self.engine.begin_device_prefill(list(tokens))
+        layer = 0
+        while True:
+            done = stage.advance(1)
+            k, v = stage.Ks[-1], stage.Vs[-1]
+            keys = k.cpu()[..., :n].transpose(-1, -2)
+            values = v.cpu()[:, :, :n]
+            emit(layer, 0, keys, values)
+            layer += keys.shape[0]
+            if done:
+                break
+        return self._last_logits(stage.x, n)
+
+    def _last_logits(self, hidden, n):
+        import torch
+        engine = self.engine
+        if callable(getattr(engine, "_logits", None)):
+            return torch.from_numpy(engine._logits(hidden, n))
+        # Older rngd-pd exposes the same on-card head only through _next_token.
+        out = engine.lmh(hidden[n - 1], engine.lm_norm_d, engine.lm_eps_d,
+                         engine.lm_nh_d, engine.wlm_d)
+        logits = out[0] if isinstance(out, (tuple, list)) else out
+        return logits.detach().cpu().float().reshape(-1)[:engine.V]
+
+
+def stream_result(engine, req, slab, publish):
+    """Copy validated source spans into reserved SHM before notifying the receiver.
+
+    Engines call emit(layer_start, token_start, K, V) with dense [L,H,N,D] spans
+    and return final logits. Calls are synchronous; source buffers may be reused
+    after emit returns. Absolute token spans are clipped to the requested suffix.
+    """
+    import numpy as np
+    import torch
+    from opt_prime.accel.pd.layer_progress import LayerProgress
+    slot, start, end = req["slot"], req["send_from"], len(req["tokens"])
+    if (type(req["id"]) is not int or type(slot) is not int
+            or not 0 <= slot < slab.shape[0] or type(start) is not int
+            or not 0 <= start <= end <= slab.shape[-2] or end == 0):
+        raise ValueError("invalid streamed request reservation")
+    coverage = LayerProgress(slab.shape[2], start, end)
+
+    def emit(layer, first, keys, values):
+        if (type(layer) is not int or type(first) is not int
+                or keys.ndim != 4 or values.shape != keys.shape):
+            raise ValueError("invalid source KV span")
+        layers, heads, count, dim = keys.shape
+        if (layers <= 0 or count <= 0 or not 0 <= layer < layer + layers <= slab.shape[2]
+                or heads != slab.shape[3] or dim != slab.shape[-1]
+                or not 0 <= first < first + count <= end):
+            raise ValueError("invalid source KV geometry")
+        lo, hi = max(start, first), first + count
+        if lo >= hi:
+            return
+        # Validate before touching any published memory. Failed copies abort this request.
+        coverage.publish(layer, layer + layers, lo, hi)
+        arrays = [t[:, :, lo - first:hi - first].to("cpu", torch.bfloat16)
+                  .contiguous().view(torch.int16).numpy().view(np.uint16) for t in (keys, values)]
+        dst = slab[slot, :, layer:layer + layers, :, lo - start:hi - start]
+        dst[0], dst[1] = arrays
+        coverage.drain()
+        publish(dict(id=req["id"], kv_span=[layer, layer + layers, lo, hi]))
+
+    logits = engine.prefill_stream(list(req["tokens"]), emit)
+    coverage.finish()
+    return logits
+
+
+def factory_engine(a):
+    """Load a trusted CLI factory using the same FP32 checkpoint inputs as research workers."""
+    from importlib import import_module
+    from pathlib import Path
+    from safetensors import safe_open
+    path = Path(a.model)
+    config = json.loads((path / "config.json").read_text())
+    width = int(a.width)
+    head_dim = config.get("head_dim") or config["hidden_size"] // config["num_attention_heads"]
+    if (not 0 < width <= a.max_context or a.slots <= 0
+            or (a.layers, a.kv_heads, a.head_dim) !=
+            (config["num_hidden_layers"], config["num_key_value_heads"], head_dim)):
+        raise ValueError("factory width or shared-memory geometry does not match checkpoint")
+    module, sep, name = (a.engine_factory or "").partition(":")
+    if not sep or not module or not name:
+        raise ValueError("engine factory must be module:callable")
+    factory = getattr(import_module(module), name)
+    if not callable(factory):
+        raise ValueError("engine factory must be callable")
+    weights = {}
+    for shard in sorted(path.glob("*.safetensors")):
+        with safe_open(shard, framework="pt") as f:
+            for key in f.keys():
+                if key in weights:
+                    raise ValueError("duplicate tensor in checkpoint shards")
+                weights[key] = f.get_tensor(key).float()
+    if not weights:
+        raise ValueError("factory requires local safetensors weights")
+    return factory(config, weights, width=width, chip=a.chip)
+
 
 def main():
     ap = argparse.ArgumentParser()
-    ap.add_argument("--engine", required=True, choices=["torch", "rngd-qwen", "rngd-full"])
+    ap.add_argument("--engine", required=True, choices=["torch", "rngd-qwen", "rngd-full", "factory"])
+    ap.add_argument("--engine-factory", help="trusted local module:callable; used only with --engine factory")
     ap.add_argument("--model", required=True); ap.add_argument("--model-id", default=None)
     ap.add_argument("--shm", required=True); ap.add_argument("--slots", type=int, required=True)
     ap.add_argument("--layers", type=int, required=True); ap.add_argument("--kv-heads", type=int, required=True)
@@ -141,9 +246,12 @@ def main():
         engine = TorchPrefillEngine(model, max_context=a.max_context, chunk=a.chunk)
     elif a.engine == "rngd-full":
         engine = _RngdFull(a)
+    elif a.engine == "factory":
+        engine = factory_engine(a)
     else:
         engine = _RngdQwen(a)
-    print(json.dumps({"ready": True}), flush=True)
+    streaming = callable(getattr(engine, "prefill_stream", None))
+    print(json.dumps({"ready": True, **({"kv_progress": 1} if streaming else {})}), flush=True)
     for line in sys.stdin:
         req = json.loads(line)
         try:
@@ -151,11 +259,20 @@ def main():
             from opt_prime.accel.pd import PDRequest
             sampling = _sampling(req.get("sampling"))
             tokens, start = tuple(req["tokens"]), int(req["send_from"])
+            if req.get("stream_layers"):
+                if req["stream_layers"] is not True or not streaming:
+                    raise ValueError("engine does not support requested layer streaming")
+                logits = stream_result(engine, req, slab,
+                                       lambda msg: print(json.dumps(msg), flush=True))
+                token = select_token(logits, sampling, len(tokens))
+                print(json.dumps(dict(id=req["id"], token=int(token), n=len(tokens) - start,
+                                      cached=0, prefill_s=time.perf_counter() - t0)), flush=True)
+                continue
             if a.engine == "torch":
                 out = engine.prefill(PDRequest(f"p{req['id']}", tokens, 1, sampling), start)
                 keys, values, token, cached = out.keys, out.values, out.token, out.prefill_cached
             else:
-                keys, values, logits = engine.prefill(tokens)
+                keys, values, logits = engine.prefill(list(tokens))
                 keys, values = keys[:, :, start:], values[:, :, start:]
                 token, cached = select_token(logits, sampling, len(tokens)), 0
             n = keys.shape[2]
diff --git a/opt_prime/opt_prime/accel/platforms/atom/batching.py b/opt_prime/opt_prime/accel/platforms/atom/batching.py
index 1fa67d0b..496ac64e 100644
--- a/opt_prime/opt_prime/accel/platforms/atom/batching.py
+++ b/opt_prime/opt_prime/accel/platforms/atom/batching.py
@@ -246,19 +246,30 @@ def _reload(self, h, block, n):
     def import_span(self, lane, start, keys, values):
         """PD: write KV computed elsewhere ([layers, kv_heads, n, head_dim], any float dtype) into the lane's block at
         positions start..start+n; the SDK's KV tensors are (key, value) per layer in bfloat16"""
+        if keys.ndim != 4 or len(self.metas) != keys.shape[0] * 2:
+            raise InvalidKVImport("KV export geometry does not match compiled cache")
+        self.import_layer_span(lane, start, 0, keys, values)
+
+    def import_layer_span(self, lane, start, layer_start, keys, values):
+        """Write a model-layer subset into an already reserved lane; never publish cache state."""
         import numpy as np
         if keys.ndim != 4 or values.ndim != 4:
             raise InvalidKVImport("KV export must have layer/head/token/dimension axes")
+        if (any(type(i) is not int for i in (lane, start, layer_start))
+                or not 0 <= lane < self.lanes or start < 0
+                or not 0 <= layer_start < layer_start + keys.shape[0] <= len(self.metas) // 2):
+            raise InvalidKVImport("invalid KV layer span or lane")
         block, n = self.lane_block[lane], keys.shape[2]
         if (self.hybrid_cache or self.single_block) and start:
             raise InvalidKVImport("hybrid or single-block PD requires a full prefix from position zero")
-        if keys.shape != values.shape or len(self.metas) != keys.shape[0] * 2:
+        if keys.shape != values.shape:
             raise InvalidKVImport("KV export geometry does not match compiled cache")
-        stage = self._staging()
+        lo, hi = 2 * layer_start, 2 * (layer_start + keys.shape[0])
+        metas, stage = self.metas[lo:hi], self._staging()[lo:hi]
         if len({buf.dtype for buf in stage}) != 1:
             raise ValueError("mixed physical KV formats are unsupported")
         destinations = []
-        for m, buf in zip(self.metas, stage):
+        for m, buf in zip(metas, stage):
             if (keys.shape[1], keys.shape[3]) != (buf.shape[1], buf.shape[3]):
                 raise InvalidKVImport("KV export heads/dimensions do not match compiled cache")
             local = self.hybrid_cache and m.get("layer_type") == "sliding_attention"
@@ -274,7 +285,7 @@ def import_span(self, lane, start, keys, values):
             data = bf16_to_dlfloat(kv.to(torch.bfloat16).contiguous().view(torch.int16).numpy().view(np.uint16))
         else:
             data = kv.float().numpy()
-        for i, (m, buf) in enumerate(zip(self.metas, stage)):
+        for i, (m, buf) in enumerate(zip(metas, stage)):
             dest, offset, kept = destinations[i]
             buf[dest, :, offset:offset + kept] = data[i // 2, i % 2, :, n - kept:]
             self.io_rt.set_kv_cache_for_layer(m["name"], dest, offset, kept, buf)
@@ -288,7 +299,7 @@ class AtomBatcher(ContinuousBatcher):
     supports_sampling = True          # the decode graph returns logits; sampling runs on the host
 
     def __init__(self, model, *, pad_token_id=None, prefix_cache=False, host_kv_gib=0.0, kv_events=None,
-                 prefill_worker=None, remote_inflight=None, local_prefill=True):
+                 prefill_worker=None, remote_inflight=None, local_prefill=True, remote_prefetch=False):
         """prefix_cache / host_kv_gib need a model compiled with kv_blocks > batch_size (own block tables;
         prefix_cache needs kv_blocks >= batch_size + 2).
         prefill_worker (PD, #120): prompts are prefilled elsewhere (`pd.pd_batching.PrefillWorker` and engines) and their
@@ -298,12 +309,17 @@ def __init__(self, model, *, pad_token_id=None, prefix_cache=False, host_kv_gib=
         (in-flight remote prefills + 1) x remote time per prompt (prefill + KV import) against local (prompts
         queued here + 1) x ATOM prefill time; local prefills run one per iteration, as in aggregated serving. A
         slow prefill device is therefore used only for overflow, and a fast one takes most prompts.
-        local_prefill=False keeps every prefill remote (decode never stalls on a prompt; TPOT first)."""
+        local_prefill=False keeps every prefill remote (decode never stalls on a prompt; TPOT first).
+        remote_prefetch=True (B1 remote-only): keep one full-prompt result ahead of decode. Cache admission still
+        happens on the owner after the lane is free; early prefills cannot omit a cached prefix from transfer."""
         rc = model.rbln_config
         self.hybrid_cache = getattr(rc, "cache_impl", None) == "hybrid"
         if self.hybrid_cache and (prefix_cache or host_kv_gib):
             raise ValueError("hybrid sliding cache restoration is not implemented for prefix/host cache")
         self.lanes = rc.batch_size
+        if remote_prefetch and (self.lanes != 1 or prefill_worker is None or local_prefill):
+            raise ValueError("remote_prefetch requires single-lane remote-only PD")
+        self.remote_prefetch, self._prefetched = remote_prefetch, None
         decoders = getattr(model, "decoders", None) or {}
         if self.lanes not in decoders:
             raise ValueError(f"no compiled decode graph for batch {self.lanes} (have {sorted(decoders)})")
@@ -320,7 +336,8 @@ def __init__(self, model, *, pad_token_id=None, prefix_cache=False, host_kv_gib=
         self.waiting, self.prefilling, self.decoding = deque(), deque(), []
         self.by_id = {}
         self._lock = threading.Lock()
-        self.stats = dict(iterations=0, prefill_chunks=0, decode_steps=0, decode_rows=0)
+        self._prefill_ready = threading.Event()
+        self.stats = dict(iterations=0, prefill_chunks=0, decode_steps=0, decode_rows=0, progress_spans=0)
         self.poisoned = False
         self.kv_events = kv_events
         self.worker = prefill_worker
@@ -343,6 +360,10 @@ def __init__(self, model, *, pad_token_id=None, prefix_cache=False, host_kv_gib=
                 for lane in range(self.lanes):         # every lane owns a page before the B-lane graph runs
                     self._prefill(lane, [self.pad])
 
+    @property
+    def idle(self):
+        return super().idle and self._prefetched is None
+
     @property
     def hit_tokens(self):
         return self.blocks.hit_tokens if self.blocks else 0
@@ -401,12 +422,12 @@ def step(self):
     def _prefill_request(self, r):
         ids = r.request.input_ids
         start = self.blocks.admit(r.slot, ids) if self.blocks is not None else 0
-        r.admitted = True
         out = self._prefill(r.slot, ids, start)
         self.stats['prefill_chunks'] += 1
         logits = out.logits[0, -1].float()
         if not torch.isfinite(logits).all():
             raise RuntimeError("non-finite ATOM logits")
+        r.admitted = True
         padded = int(getattr(out, "padded_cache_lengths", 0) or 0)
         self.kv.length[r.slot] = r.prefilled = len(ids) + padded
         self.prefilling.remove(r)                    # PD: requests waiting on remote prefills may be ahead of it
@@ -432,18 +453,52 @@ def _decode_step(self):
         self._decoded(rows, [self.kv.length[r.slot] for r in rows], logits)
 
     def _finish(self, r, error):
+        if self._prefetched is r and (r.pending is None or r.pending.done()):
+            self._prefetched = None
         if self.blocks is not None and r.slot >= 0:
             self.blocks.release(r.slot, r.request.input_ids if getattr(r, "admitted", False) else None)
         super()._finish(r, error)
 
     # ---- PD: prefill elsewhere, decode here ----
+    def _watch_prefill(self, r):
+        r.pending.add_done_callback(lambda _: self._prefill_ready.set())
+        progress = getattr(r.pending, "kv_progress", None)
+        if progress is not None:
+            progress.notify = self._prefill_ready.set
+        self._prefill_ready.set()
+
+    def _import_progress(self, r, *, final=False):
+        progress = getattr(r.pending, "kv_progress", None)
+        if progress is None:
+            return
+        if r.slot < 0 or r not in self.prefilling:
+            return                              # prefetch has no destination until the current decode releases it
+        if self.hybrid_cache and self.lanes > 1 and not final:
+            return                              # idle hybrid lanes still own local KV; defer until joining decode
+        if (progress.coverage.layers * 2 != len(self.blocks.metas)
+                or progress.coverage.end != len(r.request.input_ids)):
+            raise InvalidKVImport("streamed coverage does not match the request cache")
+        for layer, start, keys, values in progress.drain():
+            if (self.hybrid_cache or self.blocks.single_block) and (start != 0 or keys.shape[2] != len(r.request.input_ids)):
+                raise InvalidKVImport("hybrid or single-block layer streaming requires full-prefix layer spans")
+            self.blocks.import_layer_span(r.slot, start, layer, keys, values)
+            self.stats['progress_spans'] += 1
+
+    def _reject_prefill(self, r, error):
+        with self._lock:
+            self.prefilling.remove(r)
+            self._finish(r, error)
+
     def _pd_step(self):
-        from concurrent.futures import FIRST_COMPLETED, wait
         with self._lock:
+            if self._prefetched is not None and self._prefetched.future.done() and self._prefetched.pending.done():
+                self._prefetched = None                     # cancelled writer has now relinquished the source slot
             admitted, local = [], None
             remote = sum(1 for r in self.prefilling if r.pending is not None)
             queued = 0                                 # prompts that will be prefilled here before this one
             while self.free and self.waiting:
+                if self._prefetched is not None and self.waiting[0] is not self._prefetched:
+                    break                                  # a cancelled producer still owns its source buffer
                 go_remote = remote < self.remote_inflight or not self.local_prefill
                 if not self.local_prefill:
                     pass
@@ -467,42 +522,58 @@ def _pd_step(self):
         try:
             for r in admitted:                       # decode-side prefix (device copy / host reload) is not sent
                 start = self.blocks.admit(r.slot, r.request.input_ids)
-                r.admitted = True
-                r.pending = self.worker.submit(r.request, send_from=start, slot=r.slot)
-                self.stats_pd["remote"] += 1
+                if r.pending is None:
+                    r.pending = self.worker.submit(r.request, send_from=start, slot=r.slot)
+                    self.stats_pd["remote"] += 1
+                self._watch_prefill(r)
             if local is not None:
                 self.stats_pd["local"] += 1
                 t0 = time.perf_counter()
                 self._prefill_request(local)
                 self._local_s = _ema(self._local_s, time.perf_counter() - t0)
             pending = [r for r in self.prefilling if r.pending is not None]
+            if self._prefetched is not None and self._prefetched.future.done():
+                pending.append(self._prefetched)              # wait for a cancelled writer without busy-spinning
             if pending and not self.decoding:
-                wait([r.pending for r in pending], timeout=0.05, return_when=FIRST_COMPLETED)
+                self._prefill_ready.clear()
+                if not any(r.pending.done() or (r.slot >= 0 and getattr(r.pending, "kv_progress", None) is not None
+                           and r.pending.kv_progress.coverage.pending) for r in pending):
+                    self._prefill_ready.wait(timeout=0.05)
             for r in pending:
-                if not r.pending.done():
-                    continue
                 with self._lock:
                     if r not in self.prefilling:
                         continue
-                    self.prefilling.remove(r)
+                if not r.pending.done():
+                    try:
+                        self._import_progress(r)
+                    except InvalidKVImport as error:
+                        self._reject_prefill(r, error)
+                    continue
                 try:
                     out = r.pending.result()
                     if (self.hybrid_cache or self.blocks.single_block) and (out.start != 0 or out.keys.shape[2] != len(r.request.input_ids)):
                         raise ValueError("hybrid or single-block PD requires the full prompt KV")
                 except BaseException as error:        # this request fails; the lanes go on
-                    with self._lock:
-                        self._finish(r, error)
+                    self._reject_prefill(r, error)
                     continue
-                r.pending = None
                 ids = r.request.input_ids
                 t0 = time.perf_counter()
                 try:
-                    if out.keys.shape[2]:
+                    progress = getattr(r.pending, "kv_progress", None)
+                    if progress is not None:
+                        if not progress.coverage.finished:
+                            raise InvalidKVImport("streamed coverage was not validated before completion")
+                        self._import_progress(r, final=True)
+                    elif out.keys.shape[2]:
                         self.blocks.import_span(r.slot, out.start, out.keys, out.values)
                 except InvalidKVImport as error:
-                    with self._lock:
-                        self._finish(r, error)
+                    self._reject_prefill(r, error)
                     continue
+                with self._lock:
+                    self.prefilling.remove(r)
+                r.pending = None
+                if self._prefetched is r:
+                    self._prefetched = None                 # only release after the owner has imported the KV
                 if self.hybrid_cache:
                     self.decoder.dec_attn_mask[r.slot].zero_()
                     self.decoder.dec_attn_mask[r.slot, :len(ids)] = 1
@@ -510,11 +581,14 @@ def _pd_step(self):
                 self._remote_s = _ema(self._remote_s, (out.prefill_s or 0.0) + (out.transfer_s or 0.0) +
                                       time.perf_counter() - t0)
                 self.kv.length[r.slot] = r.prefilled = len(ids)
+                r.admitted = True
                 self.stats['prefill_chunks'] += 1
                 if self._emit(r, out.token):
                     self.decoding.append(r)
             if self.decoding:
                 self._decode_step()
+            if self.remote_prefetch:
+                self._prefetch_next()
         except BaseException:
             self.poisoned = True
             raise
@@ -522,3 +596,18 @@ def _pd_step(self):
             events = self.blocks.drain_events()
             if events:
                 self.kv_events.publish(events)
+
+    def _prefetch_next(self):
+        from concurrent.futures import Future
+        with self._lock:
+            if self._prefetched is not None or not self.decoding or not self.waiting:
+                return
+            r = self._prefetched = self.waiting[0]
+        try:
+            # Cache admission waits for the decode lane; reserve one full-prompt result in the existing source slot.
+            r.pending = self.worker.submit(r.request, send_from=0, slot=0)
+        except BaseException as error:
+            r.pending = Future()
+            r.pending.set_exception(error)
+        self._watch_prefill(r)
+        self.stats_pd["remote"] += 1
diff --git a/opt_prime/tests/test_atom_batching.py b/opt_prime/tests/test_atom_batching.py
index 2434a808..dd3523d0 100644
--- a/opt_prime/tests/test_atom_batching.py
+++ b/opt_prime/tests/test_atom_batching.py
@@ -434,3 +434,80 @@ def submit(self, request, send_from=0, slot=None):
     assert good.result() == _greedy(model,q,5)
     assert b.blocks.free == [0] and b.blocks.lane_block == [None]
     assert not b.by_id and not b.poisoned
+
+
+@pytest.mark.parametrize('outcome', ['success', 'cancel', 'error'])
+@pytest.mark.parametrize('cache', [False, True])
+def test_b1_prefetch_overlaps_decode_and_holds_source_until_import(fake_rebel, outcome, cache):
+    """A reserved prefill must neither wait for the decode lane nor reuse a still-written source slot."""
+    import copy
+    from concurrent.futures import Future
+    from opt_prime.accel.pd.pd_batching import TorchPrefillEngine
+
+    class ControlledProducer:
+        def __init__(self):
+            self.calls = []
+
+        def submit(self, request, send_from=0, slot=None):
+            assert send_from == slot == 0
+            future = Future()
+            self.calls.append((request, future))
+            return future
+
+    model = _llama()
+    fake = _blocks_model(fake_rebel, model, batch=1, blocks=3 if cache else 1)
+    producer = ControlledProducer()
+    engine = TorchPrefillEngine(copy.deepcopy(model), max_context=128, chunk=16, page_size=16)
+    b = AtomBatcher(fake, prefix_cache=cache, host_kv_gib=1.0 if cache else 0,
+                    prefill_worker=producer, local_prefill=False, remote_prefetch=True)
+    p, q = _prompts(2, 40, 41, seed=253)
+    first = b.submit(PDRequest('first', p, 8))
+    second = b.submit(PDRequest('second', p, 5))
+    third = b.submit(PDRequest('third', q, 5))
+    b.step()
+    request, pending = producer.calls[0]
+    pending.set_result(engine.prefill(request))
+    b.step()
+    assert not first.done() and b.decoding
+    assert len(producer.calls) == 2, 'next producer must start before the first decode finishes'
+    request, pending = producer.calls[1]
+    if outcome == 'success':
+        pending.set_result(engine.prefill(request))
+    elif outcome == 'error':
+        pending.set_exception(RuntimeError('producer failed'))
+    else:
+        assert b.cancel('second')
+        assert b.cancel('third')
+        b.step()
+        with pytest.raises(RequestCancelled):
+            second.result()
+        with pytest.raises(RequestCancelled):
+            third.result()
+        assert not pending.cancelled(), 'cancellation must not imply that the remote writer has stopped'
+    while not first.done():
+        assert len(producer.calls) == 2, 'one retained prefill must bound producer reservations'
+        b.step()
+    assert first.result() == _greedy(model, p, 8)
+    if outcome == 'cancel':
+        assert not b.idle, 'the owner must keep draining an orphaned producer after every client has left'
+        third = b.submit(PDRequest('third', q, 5))
+        b.step()
+        assert len(producer.calls) == 2, 'cancelled writer still owns its shared source slot'
+        pending.set_result(engine.prefill(request))
+    for _ in range(30):
+        b.step()
+        if len(producer.calls) == 3:
+            break
+    assert len(producer.calls) == 3
+    request, pending = producer.calls[2]
+    pending.set_result(engine.prefill(request))
+    b.run_until_idle()
+    if outcome == 'success':
+        assert second.result() == _greedy(model, p, 5)
+        if cache:
+            assert b.hit_tokens >= 32
+    elif outcome == 'error':
+        with pytest.raises(RuntimeError, match='producer failed'):
+            second.result()
+    assert third.result() == _greedy(model, q, 5)
+    assert b.free == [0] and not b.by_id and not b.poisoned
diff --git a/opt_prime/tests/test_atom_stream_progress.py b/opt_prime/tests/test_atom_stream_progress.py
new file mode 100644
index 00000000..5f0ed13c
--- /dev/null
+++ b/opt_prime/tests/test_atom_stream_progress.py
@@ -0,0 +1,177 @@
+"""Partial ATOM cache state stays private and never overwrites an active lane."""
+import copy
+from concurrent.futures import Future
+from dataclasses import replace
+import numpy as np
+import pytest
+import torch
+from opt_prime.accel.pd import PDRequest
+from opt_prime.accel.pd.pd_batching import TorchPrefillEngine,_KVProgress
+from opt_prime.accel.platforms.atom.batching import AtomBatcher,InvalidKVImport
+from test_atom_batching import FakeBlocks,_llama,_prompts,fake_rebel
+
+
+class Producer:
+    def __init__(self,model):
+        self.engine=TorchPrefillEngine(copy.deepcopy(model),max_context=128,chunk=16,page_size=16)
+        self.jobs=[]
+    def submit(self,request,send_from=0,slot=None):
+        out=self.engine.prefill(request,send_from)
+        k,v=[(t if torch.is_tensor(t) else torch.cat(t)).to(torch.bfloat16) for t in (out.keys,out.values)]
+        slab=torch.stack((k,v)).view(torch.int16).numpy().view(np.uint16)
+        progress=_KVProgress(slab,send_from,len(request.input_ids));f=Future();f.kv_progress=progress
+        progress.publish(0,1,send_from,len(request.input_ids))
+        self.jobs.append((f,progress,replace(out,keys=k,values=v)))
+        return f
+    def finish(self,index):
+        f,p,out=self.jobs[index]
+        p.publish(1,out.keys.shape[0],p.coverage.start,p.coverage.end);p.coverage.finish();f.set_result(out)
+
+
+def setup(holder,*,cache=False,prefetch=False):
+    model=_llama();fake=FakeBlocks(model,batch=1,blocks=4 if cache else 1)
+    holder['model']=fake;worker=Producer(model)
+    return fake,worker,AtomBatcher(fake,prefix_cache=cache,prefill_worker=worker,local_prefill=False,remote_prefetch=prefetch)
+
+
+def test_partial_import_before_completion_does_not_admit_decode(fake_rebel):
+    fake,w,b=setup(fake_rebel);prompt=_prompts(1,20,35)[0]
+    f=b.submit(PDRequest('a',prompt,3));b.step()
+    torch.testing.assert_close(fake.kv[0][0,:,:len(prompt)],w.jobs[0][2].keys[0].float())
+    assert not fake.kv[2].any() and b.kv.length[0]==0 and not b.decoding and not f.done()
+    w.finish(0);b.run_until_idle();assert len(f.result())==3 and b.idle
+
+
+def test_prefetch_progress_never_overwrites_active_decode(fake_rebel):
+    fake,w,b=setup(fake_rebel,prefetch=True);p,q=_prompts(2,20,35)
+    first=b.submit(PDRequest('first',p,12));second=b.submit(PDRequest('second',q,3))
+    b.step();w.finish(0);b.step()
+    assert len(w.jobs)==2 and b._prefetched.slot==-1 and len(b.decoding)==1
+    block=b.blocks.lane_block[0];snapshot=[k[block,:,:len(p)].clone() for k in fake.kv]
+    b.step()
+    for k,old in zip(fake.kv,snapshot):torch.testing.assert_close(k[block,:,:len(p)],old)
+    w.finish(1);b.run_until_idle();assert len(first.result())==12 and len(second.result())==3
+    assert b.idle
+
+
+def test_failed_prefill_does_not_publish_prefix(fake_rebel):
+    fake,w,b=setup(fake_rebel,cache=True);p=_prompts(1,32,33)[0]
+    f=b.submit(PDRequest('bad',p,3));b.step()
+    w.jobs[0][0].set_exception(RuntimeError('producer failed'))
+    b.run_until_idle()
+    with pytest.raises(RuntimeError):f.result()
+    assert not b.blocks.index and not b.blocks.entries, 'incomplete prompt published as reusable prefix'
+    assert not b.blocks.drain_events()
+
+
+def test_layer_span_rejects_bad_geometry_before_any_write(fake_rebel):
+    fake,w,b=setup(fake_rebel);b.blocks.admit(0,(1,2))
+    k=torch.ones((1,99,2,4))
+    with pytest.raises(InvalidKVImport):b.blocks.import_layer_span(0,0,0,k,k)
+    assert not any(t.any() for t in fake.kv)
+
+
+def test_layer_span_only_touches_selected_layers(fake_rebel):
+    fake,w,b=setup(fake_rebel);b.blocks.admit(0,(1,2))
+    c=fake.config;k=torch.ones((1,c.num_key_value_heads,2,c.head_dim))
+    b.blocks.import_layer_span(0,0,1,k,k*2)
+    assert not fake.kv[0].any() and not fake.kv[1].any()
+    assert torch.all(fake.kv[2][0,:,:2]==1) and torch.all(fake.kv[3][0,:,:2]==2)
+
+
+def test_streamed_decode_matches_whole_import_of_identical_kv(fake_rebel):
+    from types import SimpleNamespace
+    fake,w,b=setup(fake_rebel);prompt=_prompts(1,20,35)[0]
+    f=b.submit(PDRequest('stream',prompt,8));b.step();w.finish(0);b.run_until_idle()
+    control=FakeBlocks(copy.deepcopy(fake.m),batch=1,blocks=1);fake_rebel['model']=control
+    def submit(*args,**kw):
+        result=Future();result.set_result(w.jobs[0][2]);return result
+    baseline=AtomBatcher(control,prefix_cache=False,prefill_worker=SimpleNamespace(submit=submit),local_prefill=False)
+    g=baseline.submit(PDRequest('whole',prompt,8));baseline.run_until_idle()
+    assert f.result()==g.result()
+
+
+def test_cancelled_partial_import_never_publishes_or_overwrites_reused_lane(fake_rebel):
+    from opt_prime.accel.pd.runtime import RequestCancelled
+    fake,w,b=setup(fake_rebel,cache=True);p,q=_prompts(2,32,33)
+    old=b.submit(PDRequest('old',p,3));b.step();assert b.cancel('old');b.step()
+    with pytest.raises(RequestCancelled):old.result()
+    assert not b.blocks.index
+    new=b.submit(PDRequest('new',q,3));b.step()
+    snapshot=[t.clone() for t in fake.kv]
+    w.finish(0);b.step()
+    for t,s in zip(fake.kv,snapshot):torch.testing.assert_close(t,s)
+    w.finish(1);b.run_until_idle();assert len(new.result())==3 and b.idle
+
+
+def test_partial_sdk_failure_poisons_owner(fake_rebel):
+    fake,w,b=setup(fake_rebel)
+    def fail(*args):raise RuntimeError('SDK failed during partial write')
+    b.blocks.io_rt.set_kv_cache_for_layer=fail
+    b.submit(PDRequest('fail',_prompts(1,20,35)[0],3))
+    with pytest.raises(RuntimeError,match='SDK failed'):b.step()
+    assert b.poisoned and not b.blocks.index
+
+
+def test_hybrid_layer_import_uses_lane_for_sliding_and_block_for_global(fake_rebel):
+    from types import SimpleNamespace
+    fake=FakeBlocks(_llama(),batch=2,blocks=4);fake.rbln_config.cache_impl='hybrid'
+    for i,m in enumerate(fake.rbln_config.kvcache_metas):
+        m['layer_type']='sliding_attention' if i<2 else 'full_attention'
+        m['shape']=([2,m['shape'][1],4,m['shape'][3]] if i<2 else m['shape'])
+    fake_rebel['model']=fake
+    b=AtomBatcher(fake,prefix_cache=False,prefill_worker=SimpleNamespace(),local_prefill=False)
+    # First free global block differs from lane1.
+    b.blocks.admit(1,tuple(range(10)))
+    c=fake.config;k=torch.arange(10).reshape(1,1,10,1).float().expand(1,c.num_key_value_heads,10,c.head_dim)
+    writes=[]
+    b.blocks.io_rt.get_kv_cache_size_for_layer=lambda name: int(np.prod(next(m['shape'] for m in b.blocks.metas if m['name']==name)))*4
+    b.blocks.io_rt.set_kv_cache_for_layer=lambda name,block,offset,n,buf:writes.append((name,block,offset,n,buf[block,:,offset:offset+n].copy()))
+    b.blocks.import_layer_span(1,0,0,k,k)
+    b.blocks.import_layer_span(1,0,1,k,k)
+    assert [row[1:4] for row in writes]==[(1,0,4),(1,0,4),(0,0,10),(0,0,10)]
+    assert writes[0][4][0,:,0].tolist()==[6.,7.,8.,9.]
+    assert writes[2][4][0,:,0].tolist()==list(map(float,range(10)))
+
+
+def test_multilane_hybrid_defers_partial_cache_until_decode_admission(fake_rebel):
+    from test_atom_hybrid import HybridFake
+    fake=HybridFake();fake_rebel['model']=fake
+    for meta in fake.rbln_config.kvcache_metas[:2]:meta['layer_type']='sliding_attention'
+    w=Producer(fake.m);b=AtomBatcher(fake,prefill_worker=w,local_prefill=False)
+    f=b.submit(PDRequest('hybrid',_prompts(1,20,35)[0],3));b.step()
+    assert b.stats['progress_spans']==0, 'idle hybrid decode lanes can overwrite partial local KV'
+    w.finish(0);b.run_until_idle();assert len(f.result())==3 and b.stats['progress_spans']==2
+
+
+@pytest.mark.parametrize('outcome', ['error', 'cancel'])
+def test_prefetched_partial_failure_preserves_active_decode_and_source_ownership(fake_rebel, outcome):
+    from opt_prime.accel.pd.runtime import RequestCancelled
+    fake, w, b = setup(fake_rebel, prefetch=True)
+    p, q = _prompts(2, 20, 35)
+    first = b.submit(PDRequest('first', p, 12))
+    second = b.submit(PDRequest('second', q, 3))
+    b.step(); w.finish(0); b.step()
+    assert b.decoding and b._prefetched.slot == -1
+    pending, progress, _ = w.jobs[1]
+    assert progress.coverage.pending and not pending.done()
+    imported = b.stats['progress_spans']
+    if outcome == 'error':
+        pending.set_exception(RuntimeError('partial source failed'))
+    else:
+        assert b.cancel('second')
+    while not first.done():
+        b.step()
+        assert b.stats['progress_spans'] == imported
+    if outcome == 'cancel':
+        assert not pending.done() and not b.idle
+        w.finish(1)
+    b.run_until_idle()
+    with pytest.raises(RuntimeError if outcome == 'error' else RequestCancelled):
+        second.result()
+    assert b.idle and not b.poisoned and b.free == [0]
+    # Compare with the same BF16 source/cache path without the failed prefetch.
+    _, control_worker, control = setup(fake_rebel)
+    expected = control.submit(PDRequest('reference', p, 12))
+    control.step(); control_worker.finish(0); control.run_until_idle()
+    assert first.result() == expected.result()
diff --git a/opt_prime/tests/test_pd_dense_hybrid.py b/opt_prime/tests/test_pd_dense_hybrid.py
new file mode 100644
index 00000000..a9454acc
--- /dev/null
+++ b/opt_prime/tests/test_pd_dense_hybrid.py
@@ -0,0 +1,65 @@
+"""Dense subprocess KV must preserve model-layer order in grouped GPU pools (#258)."""
+import pytest
+import torch
+from opt_prime.accel.pd.paged import PagedKV
+from opt_prime.accel.pd.torch_models import KVGroup
+
+
+def pool():
+    groups = (KVGroup('sliding', 4, 1, 2, (0, 2)), KVGroup('full', None, 1, 2, (1,)))
+    cache = PagedKV(3, 1, 1, 2, torch.float32, 'cpu', pages=4, page_size=4,
+                    max_context=16, groups=groups)
+    assert cache.alloc(0, 12) == 0
+    return cache
+
+
+def test_dense_span_maps_model_layers_at_nonzero_start():
+    cache = pool()
+    keys = torch.arange(30).reshape(3, 1, 5, 2).float()
+    cache.write_span(0, 3, keys, keys + 100)
+    idx = cache.span_index(0, 3, 8)
+    for layer in range(3):
+        k, v = cache.layer_kv(layer)
+        torch.testing.assert_close(k[:, idx], keys[layer])
+        torch.testing.assert_close(v[:, idx], keys[layer] + 100)
+    assert all(not x[:, :, :3].any() for x in cache.gk)
+
+
+@pytest.mark.parametrize('bad', ['missing_group', 'bad_later_shape'])
+def test_invalid_grouped_span_rejected_before_any_write(bad):
+    cache = pool()
+    keys = [torch.ones(2, 1, 5, 2), torch.ones(1, 1, 5, 2)]
+    values = [x + 1 for x in keys]
+    if bad == 'missing_group':
+        keys, values = keys[:1], values[:1]
+    else:
+        values[1] = torch.ones(1, 1, 4, 2)
+    with pytest.raises(ValueError):
+        cache.write_span(0, 0, keys, values)
+    assert all(not x.any() for x in cache.gk + cache.gv)
+
+
+def test_dense_gemma_pd_matches_independent_hf_beyond_window():
+    from dataclasses import replace
+    from test_pd_hybrid import _gemma3, _greedy, _prompts
+    from opt_prime.accel.pd import PDRequest
+    from opt_prime.accel.pd.pd_batching import TorchPrefillEngine, PrefillWorker, PDBatcher
+    import copy
+
+    class DenseEngine(TorchPrefillEngine):
+        def prefill(self, request, send_from=0):
+            out = super().prefill(request, send_from)
+            def dense(groups):
+                by_layer = {layer: span[i] for g, span in zip(self.kv.groups, groups)
+                            for i, layer in enumerate(g.layers)}
+                return torch.stack([by_layer[i] for i in range(len(by_layer))])
+            return replace(out, keys=dense(out.keys), values=dense(out.values))
+
+    model = _gemma3()
+    engine = DenseEngine(copy.deepcopy(model), max_context=128, chunk=16, page_size=8)
+    batcher = PDBatcher(model, PrefillWorker(engine), slots=2, max_context=128, page_size=8,
+                        prefix_cache=False)
+    prompts = _prompts(3, 25, 40, seed=258)
+    futures = [batcher.submit(PDRequest(str(i), p, 20)) for i, p in enumerate(prompts)]
+    batcher.run_until_idle()
+    assert [f.result() for f in futures] == [_greedy(model, p, 20) for p in prompts]
diff --git a/opt_prime/tests/test_pd_layer_progress.py b/opt_prime/tests/test_pd_layer_progress.py
new file mode 100644
index 00000000..99f5277b
--- /dev/null
+++ b/opt_prime/tests/test_pd_layer_progress.py
@@ -0,0 +1,101 @@
+"""Partial KV must cover every layer/position without overlaps before publication."""
+import importlib.util
+from pathlib import Path
+import pytest
+
+path=Path(__file__).parents[1]/'opt_prime/accel/pd/layer_progress.py'
+
+def tracker(*args):
+    assert path.exists(), 'layer-progress validator is not implemented'
+    spec=importlib.util.spec_from_file_location('layer_progress',path)
+    m=importlib.util.module_from_spec(spec);spec.loader.exec_module(m)
+    return m.LayerProgress(*args)
+
+
+def test_partial_prefix_and_out_of_order_layers_require_complete_coverage():
+    p=tracker(3,5,9)
+    p.publish(1,3,5,9)
+    p.publish(0,1,7,9)
+    assert p.drain()==[(1,3,5,9),(0,1,7,9)]
+    with pytest.raises(ValueError,match='incomplete'):p.finish()
+    p.publish(0,1,5,7)
+    p.finish()
+    assert p.drain()==[(0,1,5,7)]
+    assert p.drain()==[]
+    with pytest.raises(ValueError,match='closed'):p.publish(0,1,5,7)
+
+
+def test_overlap_rejection_is_atomic_across_layers():
+    p=tracker(3,0,4)
+    p.publish(1,2,0,2)
+    with pytest.raises(ValueError,match='overlap'):p.publish(0,3,1,3)
+    # If the rejected span mutated layer0, this valid replacement would fail.
+    p.publish(0,1,0,4)
+    p.publish(1,2,2,4)
+    p.publish(2,3,0,4)
+    p.finish()
+    assert len(p.drain())==4
+
+
+@pytest.mark.parametrize('span',[(-1,1,0,4),(0,4,0,4),(0,1,-1,4),
+    (0,1,0,5),(0,0,0,4),(0,1,2,2),(True,2,0,4),(0,1,0.0,4)])
+def test_invalid_progress_never_enters_queue(span):
+    p=tracker(3,0,4)
+    with pytest.raises(ValueError):p.publish(*span)
+    assert p.drain()==[]
+
+
+def test_empty_uncached_suffix_needs_no_transfer():
+    p=tracker(3,8,8)
+    p.finish()
+    assert p.drain()==[]
+
+
+def test_partial_hybrid_write_preserves_other_layers_and_positions():
+    import torch
+    from test_pd_dense_hybrid import pool
+    cache=pool()
+    assert hasattr(cache,'write_layer_span'), 'partial receiver import is not implemented'
+    keys=torch.arange(20).reshape(2,1,5,2).float()+1
+    cache.write_layer_span(0,3,1,keys,keys+100)
+    idx=cache.span_index(0,3,8)
+    for layer in [1,2]:
+        k,v=cache.layer_kv(layer)
+        torch.testing.assert_close(k[:,idx],keys[layer-1])
+        torch.testing.assert_close(v[:,idx],keys[layer-1]+100)
+    assert not cache.layer_kv(0)[0].any()
+    assert cache.length[0]==0 and not cache.cached
+
+
+@pytest.mark.parametrize('slot,start,layer',[(0,10,0),(0,-1,0),(1,0,0),(0,0,2)])
+def test_partial_write_rejects_unreserved_positions_and_unknown_layers(slot,start,layer):
+    import torch
+    from test_pd_dense_hybrid import pool
+    cache=pool()
+    assert hasattr(cache,'write_layer_span'), 'partial receiver import is not implemented'
+    keys=torch.ones(2,1,5,2)
+    with pytest.raises(ValueError):cache.write_layer_span(slot,start,layer,keys,keys)
+    assert all(not t.any() for t in cache.gk+cache.gv)
+
+
+def test_partial_write_rejects_later_group_geometry_before_mutation():
+    import torch
+    from opt_prime.accel.pd.paged import PagedKV
+    from opt_prime.accel.pd.torch_models import KVGroup
+    cache=PagedKV(2,1,1,2,torch.float32,'cpu',pages=4,page_size=4,max_context=16,
+        groups=(KVGroup('a',None,1,2,(0,)),KVGroup('b',None,2,2,(1,))))
+    cache.alloc(0,12)
+    assert hasattr(cache,'write_layer_span'), 'partial receiver import is not implemented'
+    keys=torch.ones(2,1,5,2)
+    with pytest.raises(ValueError):cache.write_layer_span(0,0,0,keys,keys)
+    assert all(not t.any() for t in cache.gk+cache.gv)
+
+
+def test_partial_write_cannot_overwrite_an_admitted_prefix():
+    import torch
+    from test_pd_dense_hybrid import pool
+    cache=pool()
+    cache.length[0]=4
+    keys=torch.ones(1,1,5,2)
+    with pytest.raises(ValueError,match='prefix'):cache.write_layer_span(0,3,0,keys,keys)
+    assert all(not t.any() for t in cache.gk+cache.gv)
diff --git a/opt_prime/tests/test_pd_stream_admission.py b/opt_prime/tests/test_pd_stream_admission.py
new file mode 100644
index 00000000..73ebf1eb
--- /dev/null
+++ b/opt_prime/tests/test_pd_stream_admission.py
@@ -0,0 +1,98 @@
+"""Real model/cache, controlled producer completion: never publish partial state."""
+import copy
+from concurrent.futures import Future
+from dataclasses import replace
+import numpy as np
+import pytest
+import torch
+from opt_prime.accel.pd import PDRequest
+from opt_prime.accel.pd.pd_batching import PDBatcher, PrefillWorker, TorchPrefillEngine, _KVProgress
+from opt_prime.accel.pd.runtime import RequestCancelled
+from test_pd_hybrid import _gemma3,_prompts
+
+
+class ControlledProducer:
+    def __init__(self,model):
+        self.engine=TorchPrefillEngine(copy.deepcopy(model),max_context=128,chunk=16,page_size=8)
+        self.jobs=[]
+
+    def submit(self,request,send_from=0,slot=None):
+        out=self.engine.prefill(request,send_from)
+        def dense(groups):
+            mapping={layer:t[i] for g,t in zip(self.engine.kv.groups,groups) for i,layer in enumerate(g.layers)}
+            return torch.stack([mapping[i] for i in range(len(mapping))])
+        keys,values=dense(out.keys),dense(out.values)
+        slab=torch.stack((keys,values)).view(torch.int16).numpy().view(np.uint16)
+        progress=_KVProgress(slab,send_from,len(request.input_ids))
+        f=Future();f.kv_progress=progress
+        progress.coverage.publish(0,1,send_from,len(request.input_ids))
+        self.jobs.append((f,progress,replace(out,keys=keys,values=values)))
+        return f
+
+    def finish(self,index=0,complete=True):
+        f,progress,out=self.jobs[index]
+        progress.coverage.publish(1,out.keys.shape[0],progress.coverage.start,progress.coverage.end)
+        if complete:progress.coverage.finish()
+        f.set_result(out)
+
+
+def setup(device="cpu"):
+    # CUDA flex-attention dot products require a KV block dimension of at least16.
+    if device == "cuda" and not torch.cuda.is_available(): pytest.skip("CUDA unavailable")
+    model=_gemma3().to(torch.bfloat16)
+    worker=ControlledProducer(model)
+    batch=PDBatcher(copy.deepcopy(model).to(device),worker,slots=2,max_context=128,page_size=(16 if device == "cuda" else 8),prefix_cache=False)
+    return model,worker,batch
+
+
+def baseline(model,prompt,device="cpu"):
+    worker=PrefillWorker(TorchPrefillEngine(copy.deepcopy(model),max_context=128,chunk=16,page_size=8))
+    try:
+        b=PDBatcher(copy.deepcopy(model).to(device),worker,slots=1,max_context=128,page_size=(16 if device == "cuda" else 8),prefix_cache=False)
+        f=b.submit(PDRequest('whole',prompt,8));b.run_until_idle();return f.result()
+    finally:worker.close()
+
+
+@pytest.mark.parametrize("device", ["cpu", "cuda"])
+def test_hybrid_partial_import_happens_before_completion_without_admission(device):
+    model,worker,b=setup(device);prompt=_prompts(1,25,26,265)[0]
+    result=b.submit(PDRequest('stream',prompt,8));b.step()
+    request=b.by_id['stream'];idx=b.kv.span_index(request.slot,0,len(prompt))
+    keys,_=b.kv.layer_kv(0)
+    torch.testing.assert_close(keys[:,idx].cpu(),worker.jobs[0][2].keys[0])
+    assert not b.kv.layer_kv(1)[0].any()
+    assert b.kv.length[request.slot]==0 and not b.decoding and not result.done()
+    worker.finish();b.run_until_idle()
+    assert result.result()==baseline(model,prompt,device)
+
+
+@pytest.mark.parametrize("device", ["cpu", "cuda"])
+def test_cancellation_and_late_old_completion_do_not_corrupt_reused_cache(device):
+    model,worker,b=setup(device);p,q=_prompts(2,25,26,267)
+    old=b.submit(PDRequest('old',p,8));b.step();assert b.cancel('old');b.step()
+    with pytest.raises(RequestCancelled):old.result()
+    new=b.submit(PDRequest('new',q,8));b.step()
+    worker.finish(0);worker.finish(1);b.run_until_idle()
+    assert new.result()==baseline(model,q,device)
+    assert b.idle and not b.by_id and sorted(b.free)==[0,1]
+
+
+@pytest.mark.parametrize("device", ["cpu", "cuda"])
+def test_partial_producer_failure_preserves_other_active_decode(device):
+    model,worker,b=setup(device);p,q=_prompts(2,25,26,269)
+    kept=b.submit(PDRequest('kept',p,8));b.step();worker.finish(0);b.step()
+    bad=b.submit(PDRequest('bad',q,8));b.step()
+    worker.jobs[1][0].set_exception(RuntimeError('producer failed'))
+    b.run_until_idle()
+    with pytest.raises(RuntimeError,match='producer failed'):bad.result()
+    assert kept.result()==baseline(model,p,device)
+    assert b.idle and not b.by_id
+
+
+@pytest.mark.parametrize("device", ["cpu", "cuda"])
+def test_unvalidated_stream_completion_cannot_publish_cache(device):
+    _,worker,b=setup(device);prompt=_prompts(1,25,26,271)[0]
+    result=b.submit(PDRequest('incomplete',prompt,8));b.step();worker.finish(complete=False)
+    b.run_until_idle()
+    with pytest.raises(RuntimeError,match='coverage'):result.result()
+    assert b.idle and not b.by_id and not b.kv.cached
diff --git a/opt_prime/tests/test_prefill_factory.py b/opt_prime/tests/test_prefill_factory.py
new file mode 100644
index 00000000..ba6af700
--- /dev/null
+++ b/opt_prime/tests/test_prefill_factory.py
@@ -0,0 +1,87 @@
+"""Explicit local factories preserve checkpoint inputs and validate slab geometry."""
+import json,sys
+from types import SimpleNamespace,ModuleType
+import pytest
+import torch
+from safetensors.torch import save_file
+from opt_prime.accel.pd import prefill_server as server
+
+
+def setup(tmp_path,monkeypatch):
+    config=dict(num_hidden_layers=2,num_key_value_heads=1,num_attention_heads=2,hidden_size=8)
+    (tmp_path/'config.json').write_text(json.dumps(config))
+    save_file({'w':torch.ones(2,dtype=torch.bfloat16)},tmp_path/'model.safetensors')
+    module=ModuleType('test_local_prefill')
+    calls=[]
+    def factory(c,w,**kw):
+        calls.append((c,w,kw));return SimpleNamespace(prefill=lambda tokens:None)
+    module.make=factory;monkeypatch.setitem(sys.modules,module.__name__,module)
+    args=SimpleNamespace(model=str(tmp_path),engine_factory='test_local_prefill:make',width='32',chip=0,
+                         slots=1,layers=2,kv_heads=1,head_dim=4,max_context=64)
+    return args,calls
+
+
+def test_factory_receives_same_fp32_weights_and_config(tmp_path,monkeypatch):
+    args,calls=setup(tmp_path,monkeypatch)
+    engine=server.factory_engine(args)
+    assert callable(engine.prefill)
+    assert len(calls)==1
+    c,w,kw=calls[0]
+    assert c['hidden_size']==8 and w['w'].dtype==torch.float32
+    assert w['w'].tolist()==[1.,1.] and kw==dict(width=32,chip=0)
+
+
+@pytest.mark.parametrize('field,value',[('head_dim',8),('layers',3),('width','0'),('width','128'),('engine_factory','bad')])
+def test_factory_rejects_bad_setup_before_construction(tmp_path,monkeypatch,field,value):
+    args,calls=setup(tmp_path,monkeypatch);setattr(args,field,value)
+    with pytest.raises(ValueError):server.factory_engine(args)
+    assert not calls
+
+
+@pytest.mark.parametrize('stream',[False,True])
+def test_production_server_loads_factory_over_real_pipes(children,stream):
+    import time
+    from opt_prime.accel.pd import PDRequest
+    make,_,_,gate=children
+    root=gate.parent
+    (root/'config.json').write_text(json.dumps(dict(num_hidden_layers=1,num_key_value_heads=1,head_dim=2)))
+    save_file({'w':torch.ones(1)},root/'model.safetensors')
+    (root/'child').write_text(f'#!{sys.executable}\n'+'''
+import os,sys,time
+from pathlib import Path
+from types import ModuleType
+sys.path.insert(0,os.getcwd())
+import torch
+from opt_prime.accel.pd import prefill_server as server
+root=Path(os.environ['PREFILL_TEST_GATE']).parent
+module=ModuleType('local_fixture')
+class Engine:
+    def __init__(self,config,weights,**kw):
+        assert weights['w'].tolist()==[1.] and kw['width']==8
+    def prefill(self,tokens):
+        assert isinstance(tokens,list), "factory expects token list"
+        k=torch.tensor([1.,2.],dtype=torch.bfloat16).reshape(1,1,2,1).expand(1,1,2,2)
+        return k,k,torch.tensor([0.,1.])
+    def prefill_stream(self,tokens,emit):
+        k,v,logits=self.prefill(tokens)
+        emit(0,0,k[:,:,:1],v[:,:,:1])
+        while not (root/'gate').exists():time.sleep(.005)
+        emit(0,1,k[:,:,1:],v[:,:,1:])
+        return logits
+module.make=Engine;sys.modules[module.__name__]=module
+sys.argv=[sys.argv[0]]+sys.argv[3:]+['--engine','factory','--engine-factory','local_fixture:make','--model',str(root),'--width','8']
+server.main()
+''')
+    worker=make(stream_layers=stream)
+    future=worker.submit(PDRequest('factory',(1,2),1),slot=0)
+    if stream:
+        deadline=time.monotonic()+5;rows=[]
+        while not rows and time.monotonic()<deadline:
+            rows=future.kv_progress.drain();time.sleep(.005)
+        assert rows and not future.done()
+        gate.touch()
+    result=future.result(timeout=5)
+    assert result.token==1 and result.keys[0,0,:,0].tolist()==[1.,2.]
+
+
+from test_subprocess_prefill_failure import children
diff --git a/opt_prime/tests/test_prefill_source_progress.py b/opt_prime/tests/test_prefill_source_progress.py
new file mode 100644
index 00000000..f2f64a6b
--- /dev/null
+++ b/opt_prime/tests/test_prefill_source_progress.py
@@ -0,0 +1,140 @@
+"""Producer emits immutable CPU KV only after copying, before final logits."""
+import numpy as np
+import pytest
+import torch
+from opt_prime.accel.pd import prefill_server as server
+
+
+def test_source_suffix_written_before_notification_and_complete_before_return():
+    slab=np.zeros((1,2,2,1,8,2),dtype=np.uint16)
+    messages=[]
+    class Engine:
+        def prefill_stream(self,tokens,emit):
+            for layer in range(2):
+                k=torch.full((1,1,len(tokens),2),layer+1,dtype=torch.bfloat16)
+                emit(layer,0,k,k+2)
+                assert len(messages)==layer+1
+            return torch.tensor([1.,2.])
+    def publish(msg):
+        layer=msg['kv_span'][0]
+        got=torch.from_numpy(slab[0,0,layer,:,:2].view(np.int16)).view(torch.bfloat16)
+        assert torch.all(got==layer+1)
+        messages.append(msg)
+    logits=server.stream_result(Engine(),dict(id=7,tokens=[1,2,3,4],send_from=2,slot=0),slab,publish)
+    assert logits.tolist()==[1.,2.]
+    assert messages==[dict(id=7,kv_span=[i,i+1,2,4]) for i in range(2)]
+    assert not slab[:,:,:,:,2:].any()
+
+
+@pytest.mark.parametrize('mode',['overlap','hole','geometry'])
+def test_invalid_source_never_completes(mode):
+    slab=np.zeros((1,2,2,1,8,2),dtype=np.uint16);messages=[]
+    class Engine:
+        def prefill_stream(self,tokens,emit):
+            k=torch.ones((1,1,2,2),dtype=torch.bfloat16)
+            emit(0,0,k,k)
+            if mode=='overlap':emit(0,0,k*3,k*3)
+            if mode=='geometry':emit(1,0,k,torch.ones((1,2,2,2)))
+            return torch.zeros(2)
+    with pytest.raises(ValueError):
+        server.stream_result(Engine(),dict(id=0,tokens=[1,2],send_from=0,slot=0),slab,messages.append)
+    assert len(messages)==1
+    assert not slab[:,:,1].any()
+
+
+def test_rngd_full_emits_each_group_before_next_advance():
+    emitted=[]
+    class Stage:
+        Ks=[];Vs=[];x=torch.tensor([1.])
+        def advance(self,count):
+            assert count==1
+            assert len(emitted)==len(self.Ks)
+            self.Ks.append(torch.ones((1,1,2,4))*len(self.Ks))
+            self.Vs.append(torch.ones((1,1,4,2))*len(self.Vs))
+            return len(self.Ks)==2
+    class Engine:
+        def begin_device_prefill(self,tokens):return Stage()
+        def _logits(self,x,n):return np.array([1.,2.])
+    wrapper=server._RngdFull.__new__(server._RngdFull)
+    wrapper.engine=Engine();wrapper.widths=[4]
+    def emit(layer,start,k,v):
+        assert (layer,start)==(len(emitted),0)
+        assert k.shape==v.shape==(1,1,3,2)
+        assert k.device.type==v.device.type=='cpu'
+        emitted.append(layer)
+    assert wrapper.prefill_stream([1,2,3],emit).tolist()==[1.,2.]
+    assert emitted==[0,1]
+
+
+def test_real_server_and_receiver_stream_before_completion(children):
+    import sys,time
+    from opt_prime.accel.pd import PDRequest
+    make,_,_,gate=children
+    child=gate.parent/'child'
+    child.write_text(f'#!{sys.executable}\n'+'''
+import sys,os,time
+sys.path.insert(0,os.getcwd())
+from pathlib import Path
+import torch
+from opt_prime.accel.pd import prefill_server as server
+class Engine:
+    def __init__(self,args):pass
+    def prefill_stream(self,tokens,emit):
+        k=torch.ones((1,1,1,2),dtype=torch.bfloat16)
+        emit(0,0,k,k)
+        gate=Path(os.environ['PREFILL_TEST_GATE'])
+        while not gate.exists():time.sleep(.005)
+        emit(0,1,k*2,k*2)
+        return torch.tensor([0.,1.])
+server._RngdFull=Engine
+sys.argv=[sys.argv[0]]+sys.argv[3:]+['--engine','rngd-full','--model',str(Path.cwd())]
+server.main()
+''')
+    w=make(stream_layers=True)
+    f=w.submit(PDRequest('source',(1,2),1),slot=0)
+    deadline=time.monotonic()+5
+    rows=[]
+    while not rows and time.monotonic()<deadline:
+        rows=f.kv_progress.drain();time.sleep(.005)
+    assert rows and not f.done()
+    torch.testing.assert_close(rows[0][2],torch.ones((1,1,1,2),dtype=torch.bfloat16))
+    gate.touch();out=f.result(timeout=5)
+    assert out.token==1
+    assert out.keys[0,0,:,0].tolist()==[1.,2.]
+    assert f.kv_progress.coverage.finished
+
+
+from test_subprocess_prefill_failure import children
+
+
+def test_fully_cached_suffix_publishes_no_spans():
+    slab=np.zeros((1,2,2,1,8,2),dtype=np.uint16);messages=[]
+    class Engine:
+        def prefill_stream(self,tokens,emit):
+            k=torch.ones((2,1,2,2),dtype=torch.bfloat16)
+            emit(0,0,k,k)
+            return torch.ones(2)
+    server.stream_result(Engine(),dict(id=0,tokens=[1,2],send_from=2,slot=0),slab,messages.append)
+    assert not messages and not slab.any()
+
+
+@pytest.mark.parametrize('field,value',[('slot',True),('slot',-1),('send_from',True),('send_from',3),('id',True)])
+def test_bad_reservation_rejected_before_engine(field,value):
+    class Engine:
+        def prefill_stream(self,*args):pytest.fail('invalid reservation reached engine')
+    req=dict(id=0,tokens=[1,2],send_from=0,slot=0);req[field]=value
+    with pytest.raises(ValueError):
+        server.stream_result(Engine(),req,np.zeros((1,2,2,1,8,2),dtype=np.uint16),lambda _:None)
+
+
+def test_rngd_full_legacy_head_exports_logits_without_argmax_loss():
+    from types import SimpleNamespace
+    calls=[]
+    def head(*args):
+        calls.append(args)
+        return (torch.tensor([[1.,3.,2.,99.]],dtype=torch.bfloat16),)
+    wrapper=server._RngdFull.__new__(server._RngdFull)
+    wrapper.engine=SimpleNamespace(lmh=head,lm_norm_d=1,lm_eps_d=2,lm_nh_d=3,wlm_d=4,V=3)
+    result=wrapper._last_logits(torch.tensor([[5.],[6.]]),2)
+    assert result.tolist()==[1.,3.,2.] and result.dtype==torch.float32
+    assert len(calls)==1 and calls[0][0].tolist()==[6.]
diff --git a/opt_prime/tests/test_serving_prefill_cache.py b/opt_prime/tests/test_serving_prefill_cache.py
index 8534fec2..23434f33 100644
--- a/opt_prime/tests/test_serving_prefill_cache.py
+++ b/opt_prime/tests/test_serving_prefill_cache.py
@@ -88,3 +88,40 @@ def unexpected_load(*args, **kwargs):
     monkeypatch.setattr(factory.AutoModelForCausalLM, 'from_pretrained', unexpected_load)
     with pytest.raises(ValueError, match='CUDA'):
         factory.prefill_worker(args, 1, None)
+
+
+@pytest.mark.parametrize('backend,python,source', [('rngd','python',None),('torch',None,None),('torch','python','cpu')])
+def test_layer_stream_rejects_unsupported_owner(backend,python,source):
+    spec=importlib.util.spec_from_file_location('stream_factory',Path(__file__).parents[1]/'examples/pd/serve_batched.py')
+    factory=importlib.util.module_from_spec(spec);spec.loader.exec_module(factory)
+    args=SimpleNamespace(pd_prefill_stream_layers=True,backend=backend,pd_prefill_python=python,pd_prefill_device=source)
+    with pytest.raises(ValueError,match='layer streaming'):
+        factory.prefill_worker(args,1,None)
+
+
+@pytest.mark.parametrize('backend',['torch','atom'])
+def test_layer_stream_reaches_subprocess_receiver(monkeypatch,backend):
+    import opt_prime.accel.pd.pd_batching as pd
+    spec=importlib.util.spec_from_file_location('stream_factory',Path(__file__).parents[1]/'examples/pd/serve_batched.py')
+    factory=importlib.util.module_from_spec(spec);spec.loader.exec_module(factory)
+    received={}
+    def worker(*args,**kw):received.update(kw);return object()
+    monkeypatch.setattr(pd,'SubprocessPrefillWorker',worker)
+    args=SimpleNamespace(pd_prefill_stream_layers=True,backend=backend,pd_prefill_python='python',pd_prefill_device=None,
+                         pd_prefill_pythonpath=None,pd_prefill_args='--engine rngd-full',model='local',max_context=16)
+    config=LlamaConfig(hidden_size=32,num_attention_heads=4,num_key_value_heads=2,num_hidden_layers=2)
+    factory.prefill_worker(args,1,config)
+    assert received['stream_layers'] is True
+@pytest.mark.parametrize('backend,slots,remote,worker', [('torch',1,True,True),('atom',2,True,True),
+                                                        ('atom',1,False,True),('atom',1,True,False)])
+def test_next_prefill_rejects_unsupported_route_before_load(monkeypatch,backend,slots,remote,worker):
+    path=Path(__file__).parents[1]/'examples/pd/serve_batched.py'
+    spec=importlib.util.spec_from_file_location('serve_prefetch_test',path)
+    factory=importlib.util.module_from_spec(spec);spec.loader.exec_module(factory)
+    args=SimpleNamespace(model='must-not-load',backend=backend,slots=slots,pd_prefetch_next=True,
+                         pd_remote_only=remote,pd_prefill_python='python' if worker else None,pd_prefill_device=None)
+    def unexpected_load(*args,**kwargs):
+        pytest.fail('unsupported prefetch route reached model loading')
+    monkeypatch.setattr(factory.AutoTokenizer,'from_pretrained',unexpected_load)
+    with pytest.raises(ValueError,match='single-lane'):
+        factory.build(args)
diff --git a/opt_prime/tests/test_streaming_factories.py b/opt_prime/tests/test_streaming_factories.py
new file mode 100644
index 00000000..e0878840
--- /dev/null
+++ b/opt_prime/tests/test_streaming_factories.py
@@ -0,0 +1,76 @@
+"""Adapter ordering/layout checks without compiling an accelerator graph."""
+import importlib.util,sys
+from pathlib import Path
+from types import SimpleNamespace,ModuleType
+import pytest
+import torch
+
+
+def adapter():
+    spec=importlib.util.spec_from_file_location('streaming_factories',Path(__file__).parents[1]/'examples/pd/streaming_factories.py')
+    module=importlib.util.module_from_spec(spec);spec.loader.exec_module(module);return module
+
+
+def test_qwen_stream_exports_model_layer_order_before_next_call():
+    m=adapter();spans=[]
+    e=SimpleNamespace(width=4,embedding=torch.arange(20).reshape(10,2).float(),device='cpu',
+                      layers=[(0,),(1,)],fixed=[],rotation=0,mask=0,chip=0,s={'rms_norm_eps':1e-6},
+                      norm=torch.ones(2),head=torch.eye(2),sync=lambda _:None)
+    def layer(x,i,*unused):
+        assert len(spans)==i
+        kv=torch.arange(16).reshape(2,1,2,4).to(torch.bfloat16)+i*16
+        return x+1,kv
+    e.module=layer
+    def emit(i,start,k,v):
+        assert k.shape==v.shape==(1,1,2,2)
+        assert (i,start)==(len(spans),0)
+        spans.append((k.clone(),v.clone()))
+    logits=m.qwen_stream(e,[1,2],emit)
+    z=e.embedding[2]+2
+    torch.testing.assert_close(logits,z*torch.rsqrt(z.square().mean()+1e-6))
+    assert spans[0][0][0,0].tolist()==[[0.,4.],[1.,5.]]
+    assert spans[1][1][0,0].tolist()==[[24.,28.],[25.,29.]]
+
+
+def test_koni_stream_uses_partitioned_stage_and_host_head():
+    m=adapter();spans=[]
+    class Stage:
+        def __init__(self):self.Ks=[];self.Vs=[];self.x=torch.ones((4,2))
+        def advance(self):
+            assert len(spans)==len(self.Ks)
+            self.Ks.append(torch.ones((1,1,2,4))*len(self.Ks));self.Vs.append(torch.ones((1,1,4,2))*len(self.Vs))
+            return len(self.Ks)==2
+    e=SimpleNamespace(width=4,config={'vocab_size':10,'rms_norm_eps':1e-6},engine=SimpleNamespace(begin_device_prefill=lambda _:Stage()),
+                      chip=0,sync=lambda _:None,norm=torch.ones(2),head=torch.eye(2))
+    def emit(i,start,k,v):
+        assert k.shape==v.shape==(1,1,2,2)
+        assert (i,start)==(len(spans),0);spans.append(i)
+    torch.testing.assert_close(m.koni_stream(e,[1,2],emit),torch.ones(2)*torch.rsqrt(torch.tensor(1.+1e-6)))
+    assert spans==[0,1]
+
+
+def test_gemma_stream_keeps_padding_and_local_global_inputs(monkeypatch):
+    m=adapter();spans=[];seen=[]
+    helpers=ModuleType('gemma_parts')
+    helpers.initial_hidden=lambda embedding,ids:embedding[ids]
+    helpers.final_logits=lambda hidden,norm,head,eps:hidden.float() @ head.float().T
+    monkeypatch.setitem(sys.modules,'gemma_parts',helpers)
+    e=SimpleNamespace(width=4,config={'vocab_size':10,'rms_norm_eps':1e-6,'pad_token_id':0},device='cpu',
+        weights={'model.embed_tokens.weight':torch.arange(20).reshape(10,2).float(),'model.norm.weight':torch.ones(2),'lm_head.weight':torch.eye(2)},
+        params=[(0,),(1,)],positions={True:(11,),False:(22,)},local=[True,False],chip=0,sync=lambda _:None)
+    def layer(x,i,position):
+        assert len(spans)==i;seen.append(position)
+        assert x[-1].tolist()==[float(i),float(i+1)]
+        return x+1,torch.ones((4,1,2))*i,torch.ones((4,1,2))*(i+3)
+    e.compiled=layer
+    def emit(i,start,k,v):
+        assert (i,start)==(len(spans),0) and k.shape==v.shape==(1,1,2,2)
+        spans.append(i)
+    assert m.gemma_stream(e,[1,2],emit).tolist()==[6.,7.]
+    assert seen==[11,22]
+
+
+@pytest.mark.parametrize('kind',['qwen','gemma','koni'])
+def test_bad_prompt_rejected_before_computation(kind):
+    m=adapter();e=SimpleNamespace(width=4)
+    with pytest.raises(ValueError):getattr(m,kind+'_stream')(e,[],lambda *a:None)
diff --git a/opt_prime/tests/test_subprocess_layer_progress.py b/opt_prime/tests/test_subprocess_layer_progress.py
new file mode 100644
index 00000000..2c8dee5d
--- /dev/null
+++ b/opt_prime/tests/test_subprocess_layer_progress.py
@@ -0,0 +1,132 @@
+"""Exercise real pipes/SHM: completed spans are observable before final logits."""
+import sys
+import time
+import pytest
+import torch
+from opt_prime.accel.pd import PDRequest
+from test_subprocess_prefill_failure import children
+
+CHILD = '''
+import json,os,sys,time
+from pathlib import Path
+import numpy as np
+from multiprocessing import shared_memory,resource_tracker
+mode=os.environ['PREFILL_TEST_MODE']
+gate=Path(os.environ['PREFILL_TEST_GATE'])
+def arg(name):return sys.argv[sys.argv.index(name)+1]
+shm=shared_memory.SharedMemory(name=arg('--shm'))
+resource_tracker.unregister(shm._name,'shared_memory')
+shape=tuple(int(arg(x)) for x in ['--slots'])+(2,)+tuple(int(arg(x)) for x in ['--layers','--kv-heads','--max-context','--head-dim'])
+slab=np.ndarray(shape,dtype=np.uint16,buffer=shm.buf)
+print(json.dumps({'ready':True,'kv_progress':1}),flush=True)
+for line in sys.stdin:
+ msg=json.loads(line)
+ assert msg['stream_layers'] is True
+ start=msg['send_from'];n=len(msg['tokens'])-start;slot=msg['slot']
+ slab[slot,:,:,:,:1]=0x3f80
+ span=[0,1,start,start+1]
+ if mode=='bad_id' and msg['id']==0:
+  while not gate.exists():time.sleep(.005)
+ print(json.dumps({'id':True if mode=='bad_id' and msg['id']==0 else msg['id'],'kv_span':span}),flush=True)
+ if msg['id']==0:
+  gate.with_suffix('.seen').touch()
+  while not gate.exists():time.sleep(.005)
+ if mode=='exit':sys.exit(8)
+ if mode=='overlap':print(json.dumps({'id':msg['id'],'kv_span':span}),flush=True)
+ if mode!='hole' and n>1:
+  slab[slot,:,:,:,1:n]=0x4000
+  print(json.dumps({'id':msg['id'],'kv_span':[0,1,start+1,start+n]}),flush=True)
+ print(json.dumps({'id':msg['id'],'n':1 if mode=='short' else n,'token':17}),flush=True)
+'''
+
+
+def make_stream(children, mode='ok'):
+    make,_,_,gate=children
+    path=gate.parent/'child'
+    path.write_text(f'#!{sys.executable}\n'+CHILD)
+    return make(mode,stream_layers=True),gate
+
+
+def first_span(future):
+    deadline=time.monotonic()+3
+    while time.monotonic()<deadline:
+        rows=future.kv_progress.drain()
+        if rows:return rows
+        time.sleep(.005)
+    pytest.fail('no partial KV notification before completion')
+
+
+def test_progress_reads_actual_shm_before_final_completion(children):
+    w,gate=make_stream(children)
+    future=w.submit(PDRequest('stream',(1,2),1),slot=0)
+    rows=first_span(future)
+    assert not future.done()
+    layer,start,keys,values=rows[0]
+    assert (layer,start,tuple(keys.shape))==(0,0,(1,1,1,2))
+    assert torch.equal(keys,torch.ones_like(keys)) and torch.equal(values,keys)
+    gate.touch()
+    out=future.result(timeout=3)
+    assert out.token==17
+    assert torch.equal(out.keys[0,0,:,0],torch.tensor([1,2],dtype=torch.bfloat16))
+    assert len(future.kv_progress.drain())==1
+
+
+@pytest.mark.parametrize('mode',['overlap','hole','short','exit'])
+def test_bad_stream_never_completes_successfully(children,mode):
+    w,gate=make_stream(children,mode)
+    f=w.submit(PDRequest('bad',(1,2),1),slot=0)
+    assert first_span(f)
+    gate.touch()
+    with pytest.raises(RuntimeError):f.result(timeout=3)
+    assert not w.ready
+    with pytest.raises(RuntimeError):w.submit(PDRequest('later',(1,2),1),slot=0).result(timeout=3)
+
+
+def test_streaming_opt_in_requires_capable_producer(children):
+    make,procs,_,_=children
+    with pytest.raises(RuntimeError,match='progress'):
+        make(stream_layers=True,startup_timeout=1)
+    assert procs[-1].poll() is not None
+
+
+def test_cancel_then_same_slot_reuse_keeps_new_stream_owned(children):
+    w,gate=make_stream(children)
+    first=w.submit(PDRequest('cancel',(1,2),1),slot=0)
+    assert first_span(first)
+    assert first.cancel()
+    second=w.submit(PDRequest('reuse',(1,2),1),slot=0)
+    gate.touch()
+    out=second.result(timeout=3)
+    assert first.cancelled() and out.token==17
+    assert len(second.kv_progress.drain())==2
+    assert torch.equal(out.values[0,0,:,0],torch.tensor([1,2],dtype=torch.bfloat16))
+
+
+def test_progress_positions_are_absolute_for_cached_prefix(children):
+    w,gate=make_stream(children)
+    f=w.submit(PDRequest('suffix',(1,2),1),send_from=1,slot=0)
+    rows=first_span(f)
+    assert rows[0][1]==1 and rows[0][2].shape[2]==1
+    gate.touch()
+    assert f.result(timeout=3).start==1
+
+
+def test_invalid_stream_capacity_fails_only_its_future(children):
+    w,gate=make_stream(children)
+    bad=w.submit(PDRequest('too-long',tuple(range(9)),1),slot=0)
+    with pytest.raises(ValueError,match='suffix'):bad.result(timeout=1)
+    assert w.ready
+    good=w.submit(PDRequest('valid',(1,2),1),slot=0)
+    assert first_span(good)
+    gate.touch()
+    assert good.result(timeout=3).token==17
+
+
+def test_boolean_response_id_cannot_alias_another_inflight_request(children):
+    w,gate=make_stream(children,'bad_id')
+    first=w.submit(PDRequest('first',(1,2),1),slot=0)
+    second=w.submit(PDRequest('second',(1,2),1),slot=1)
+    gate.touch()
+    for f in [first,second]:
+        with pytest.raises(RuntimeError):f.result(timeout=3)
+    assert second.kv_progress.drain()==[], 'boolean ID was routed to integer request1'