diff --git a/.github/workflows/neural-experiment.yml b/.github/workflows/neural-experiment.yml
new file mode 100644
index 0000000..76f5be1
--- /dev/null
+++ b/.github/workflows/neural-experiment.yml
@@ -0,0 +1,36 @@
+name: Neural experiment
+on:
+  pull_request:
+  workflow_dispatch:
+permissions:
+  contents: read
+concurrency:
+  group: neural-experiment-${{ github.ref }}
+  cancel-in-progress: true
+jobs:
+  neural-checks:
+    strategy:
+      fail-fast: false
+      matrix:
+        os: [ubuntu-latest, macos-latest, windows-latest]
+    runs-on: ${{ matrix.os }}
+    steps:
+      - uses: actions/checkout@11d5960a326750d5838078e36cf38b85af677262
+      - uses: dtolnay/rust-toolchain@4716b85f2fac3e324e64fa2810f6b5c3905760a5
+        with:
+          components: rustfmt, clippy
+      - uses: Swatinem/rust-cache@6323deb102c322ba6fcbdcafc7e3dddab59af2b6
+        with:
+          workspaces: experiments/neural -> ../../target/neural
+      - run: cargo fmt --manifest-path experiments/neural/Cargo.toml -- --check
+      - run: cargo clippy --locked --manifest-path experiments/neural/Cargo.toml --target-dir target/neural --all-targets -- -D warnings
+      - run: cargo test --locked --manifest-path experiments/neural/Cargo.toml --target-dir target/neural
+  neural-audit:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@11d5960a326750d5838078e36cf38b85af677262
+      - uses: dtolnay/rust-toolchain@4716b85f2fac3e324e64fa2810f6b5c3905760a5
+      - uses: Swatinem/rust-cache@6323deb102c322ba6fcbdcafc7e3dddab59af2b6
+      - run: cargo install cargo-audit --locked --version 0.22.0
+      # Candle transitively uses this unmaintained macro crate; see the protocol.
+      - run: cargo audit --file experiments/neural/Cargo.lock --deny warnings --ignore RUSTSEC-2024-0436
diff --git a/README.md b/README.md
index 9c0633f..aee1546 100644
--- a/README.md
+++ b/README.md
@@ -170,6 +170,11 @@ remain. English alone is supported.
 
 The [AAC experiment protocol](docs/aac-experiment.md) and earlier
 [conversational results](docs/quality-results.md) document model-selection history.
+Switchify is a general-purpose typing app. The separate
+[neural experiment](docs/general-neural-protocol.md) tests messages, email,
+documents and search-style text equally, without changing the production model.
+Its [results](docs/general-neural-results.md) show the measured accuracy gain
+and CPU latency cost on synthetic fixtures.
 The `prepare` and `evaluate` commands retain the earlier comparison workflows;
 `prepare` alone does not prepare the promoted production corpus. Use the commands
 above for production. A new model-selection exercise needs a new protocol; these
diff --git a/docs/futo-investigation.md b/docs/futo-investigation.md
new file mode 100644
index 0000000..82cce67
--- /dev/null
+++ b/docs/futo-investigation.md
@@ -0,0 +1,38 @@
+# FUTO keyboard model investigation
+
+Inspected on 2026-10-03. FUTO is a relevant general-purpose keyboard reference.
+It has not been benchmarked or integrated in this experiment.
+
+The official source at commit
+`70a5d390c505a6bbcc4e14966e5628e43ca3f1fc` selects the English `ml4_q6_k` model.
+The resource is available from the official `keyboard-large-resources` submodule
+at commit `d87d9dbdf3966bbe18413be375dab2f6c7bbdfdd`, path
+`raw/ml4_q6_k.gguf`. GitLab's file metadata reports 30,662,880 bytes and content
+SHA-256 `6545c1c9ef2d76e9bfb87ad4fcf2061889513af84fcf30d907412be7fcdedb7b`.
+These are publisher metadata, not a locally verified download hash. Its name
+identifies the Q6_K model; no comparison with the F32 SmolLM2 timings is implied.
+
+The model is not an arbitrary chat GGUF. The Android loader requires KeyboardLM
+metadata and recognized feature flags. The native prediction path includes
+special word-boundary behavior, candidate search, capitalization filters and
+`<XBU>`, `<XBC>`, `<XEC>` / character tokens for supported correction modes.
+Loading weights with a generic GGUF runtime alone would not reproduce FUTO.
+
+A standalone Windows/macOS benchmark appears feasible by isolating the native
+prediction implementation and its matching runtime behind a Rust adapter.
+That is an engineering assessment from the source, not a tested build. A useful
+next step is to reproduce its next-word and partial-word outputs on fixed
+fixtures before comparing latency or accuracy. Its correction mode must not
+silently violate Switchify's exact-prefix completion contract.
+
+The repository identifies its code licence as FUTO Source First 1.1. Model terms
+also need checking before any redistribution; this investigation neither copies
+FUTO code into the MIT library nor distributes its model.
+
+## Sources
+
+- [Pinned model loader](https://github.com/futo-org/android-keyboard/blob/70a5d390c505a6bbcc4e14966e5628e43ca3f1fc/java/src/org/futo/inputmethod/latin/xlm/ModelPaths.kt)
+- [Pinned native prediction path](https://github.com/futo-org/android-keyboard/blob/70a5d390c505a6bbcc4e14966e5628e43ca3f1fc/native/jni/org_futo_inputmethod_latin_xlm_LanguageModel.cpp)
+- [Pinned resource submodule declarations](https://github.com/futo-org/android-keyboard/blob/70a5d390c505a6bbcc4e14966e5628e43ca3f1fc/.gitmodules)
+- [Official model resource](https://gitlab.futo.org/keyboard/keyboard-large-resources/-/blob/d87d9dbdf3966bbe18413be375dab2f6c7bbdfdd/raw/ml4_q6_k.gguf)
+- [Pinned code licence](https://github.com/futo-org/android-keyboard/blob/70a5d390c505a6bbcc4e14966e5628e43ca3f1fc/LICENSE.md)
diff --git a/docs/general-neural-protocol.md b/docs/general-neural-protocol.md
new file mode 100644
index 0000000..062a0a4
--- /dev/null
+++ b/docs/general-neural-protocol.md
@@ -0,0 +1,93 @@
+# General-purpose neural experiment
+
+Frozen 2026-10-03 before scoring. Switchify is a general-purpose typing app.
+Messages, email, documents and search-style text receive equal weight. AAC is
+not a selection objective. The existing released model is a control, including
+its historical training mixture; this experiment does not endorse that mixture.
+
+## Fixed comparison
+
+Compare the released predictor with a SmolLM2-135M base-model reranker using
+Candle 0.11.0, CPU F32, four Rayon threads and no GPU, personal learning or
+network access during inference. This initial implementation is unquantized.
+Weights, tokenizer and configuration are pinned by SHA-256 and upstream commit.
+The experiment is a separate, unpublished Rust crate. Production dependencies,
+APIs and model formats stay unchanged.
+
+Both modes request the existing engine's top eight prefix-matching words. The
+control returns its first five. The neural mode scores each entire candidate's
+token sequence, followed by the probability of a word boundary, and returns
+the five highest scores. It uses BOS plus the last 63 tokens of preceding text
+within the current sentence. It does not score only the first subword, normalize
+by word length, mix frequency scales or learn from targets. Ties retain baseline
+order. Each query starts with an empty KV cache; the context cache is shared only
+between candidate branches within that query. Prefixes filter candidates before
+neural scoring. This is a bounded reranker, not unrestricted neural generation;
+report top-eight candidate coverage as its accuracy ceiling.
+
+Boundary probability sums the next-token probabilities for EOS and tokens whose
+decoded text starts with whitespace, a number, or one of `. ! ? ; : , ( ) [ ] { }
+" - /`. Apostrophe continuations remain inside a word. This is an explicit
+approximation, not an exact tokenizer-independent word probability.
+
+## Workload and decision
+
+The checked-in fixtures contain 24 newly authored synthetic examples in each
+of four domains. They are diagnostic fixtures, not a representative user corpus
+or independent evidence of real-world accuracy. Normalize with the existing
+Unicode/sentence tokenizer. Reject exact full-sentence overlap with the pinned
+production training file. Unknown overlap with neural pretraining remains.
+
+For each domain and each prefix length 0 through 4 Unicode graphemes, hash-sort
+eligible target positions and take 64 distinct queries, excluding words already
+complete at that prefix. The fixed workload has 1,280 queries per mode. Duplicate
+preceding-text/prefix/target triples within a cell are removed. All words before
+the target are supplied identically; each predictor uses its supported context.
+Lowercasing and removing punctuation limit conclusions about realistic writing.
+
+Report top-one, top-five and candidate coverage per domain/prefix, cold load,
+warm median, p95, maximum, failures, model file size and process peak memory.
+Prime once without timing. Abort on inference errors instead of silently falling
+back. A smaller `--per-cell` run is calibration only, not a result used to tune
+the model, shortlist or ranking rule.
+
+A promising result requires higher mean top-five accuracy over prefixes 0,1,2
+and all four domains, no domain/prefix loss above one percentage point there,
+and warm p95 below 20 ms. Do not tune after seeing fixture scores or promote a
+model automatically. Describe tradeoffs even if the criterion fails. Windows
+is the local benchmark platform; macOS build/tests do not establish macOS speed.
+
+Investigate FUTO's official model integration separately. Do not equate generic
+GGUF loading with implementing its custom keyboard prompts and tokenizer rules.
+No FUTO accuracy claim without running its actual prediction path.
+
+## Reproduction
+
+Use Rust 1.97.1 and Python 3. Prepare the unchanged production partitions with
+`python scripts/aac_experiment.py --prepare-only` if they are not cached. Extract
+`english.sqlite` from the model bundle on the
+[v0.1.0 release](https://github.com/switchifyapp/switchify-prediction/releases/tag/v0.1.0).
+Then run from the repository root:
+
+```sh
+cargo build --release --locked --manifest-path experiments/neural/Cargo.toml --target-dir target/neural
+python scripts/general_neural_experiment.py --baseline /path/to/english.sqlite --output artifacts/general-neural
+```
+
+The Python command downloads and verifies the pinned model before launching the
+offline Rust executables. Use a fresh output directory for each full run.
+It records aggregate results and Windows process peak working set. Other
+platforms report memory as unavailable, not zero. No captured user text is used.
+
+Run formatting, Clippy and tests with the separate crate's manifest in addition
+to the repository checks. Its lockfile contains Candle's transitive `paste`
+1.0.15 dependency, which has the unmaintained-package advisory RUSTSEC-2024-0436.
+The production dependency audit remains unchanged. For this unpublished
+experiment only, audit the separate lockfile with that single advisory exception:
+
+```sh
+cargo audit --file experiments/neural/Cargo.lock --deny warnings --ignore RUSTSEC-2024-0436
+```
+
+This exception does not assert that the dependency is maintained. Reassess the
+dependency before considering production integration.
diff --git a/docs/general-neural-results.json b/docs/general-neural-results.json
new file mode 100644
index 0000000..76db8eb
--- /dev/null
+++ b/docs/general-neural-results.json
@@ -0,0 +1,325 @@
+{
+  "protocol": "general-neural-v1",
+  "hardware": "Windows-11-10.0.26200-SP0; AMD64; 24 logical CPUs",
+  "rayon_threads": 4,
+  "dtype": "F32",
+  "device": "CPU",
+  "executable_sha256": "67cc3ad32dd7abd22c699b8bd1973c6110cc084b2bf3cf16d4da3d393c8c72b2",
+  "fixture_sha256": "5e7fe93fc126862da5895d6c7304414c2a49a48303688648349afb3db956fe76",
+  "reports": {
+    "baseline": {
+      "mode": "Baseline",
+      "fixture_sha256": "5e7fe93fc126862da5895d6c7304414c2a49a48303688648349afb3db956fe76",
+      "baseline_sha256": "222253417d0a7a705823ffb7e599a3bcf5d5d3daf4a9d76161ac6b3e555aeaad",
+      "model_manifest_sha256": "5099892d4ea02608c0710c55358c4fd38e5890c9fd061fa3291b89b21cacf140",
+      "per_cell": 64,
+      "shortlist": 8,
+      "context_tokens": 64,
+      "cold_load_ms": 2543.1276000000003,
+      "query_count": 1280,
+      "warm_median_ms": 0.0792,
+      "warm_p95_ms": 6.0288,
+      "warm_max_ms": 8.2803,
+      "failed_queries": 0,
+      "model_file_bytes": 269060552,
+      "baseline_payload_bytes": 16821218,
+      "cells": {
+        "documents": {
+          "0": {
+            "queries": 64,
+            "top1_hits": 2,
+            "top5_hits": 6,
+            "shortlist_hits": 10
+          },
+          "1": {
+            "queries": 64,
+            "top1_hits": 7,
+            "top5_hits": 9,
+            "shortlist_hits": 10
+          },
+          "2": {
+            "queries": 64,
+            "top1_hits": 10,
+            "top5_hits": 19,
+            "shortlist_hits": 23
+          },
+          "3": {
+            "queries": 64,
+            "top1_hits": 12,
+            "top5_hits": 34,
+            "shortlist_hits": 41
+          },
+          "4": {
+            "queries": 64,
+            "top1_hits": 22,
+            "top5_hits": 46,
+            "shortlist_hits": 53
+          }
+        },
+        "email": {
+          "0": {
+            "queries": 64,
+            "top1_hits": 3,
+            "top5_hits": 12,
+            "shortlist_hits": 19
+          },
+          "1": {
+            "queries": 64,
+            "top1_hits": 16,
+            "top5_hits": 29,
+            "shortlist_hits": 31
+          },
+          "2": {
+            "queries": 64,
+            "top1_hits": 27,
+            "top5_hits": 42,
+            "shortlist_hits": 44
+          },
+          "3": {
+            "queries": 64,
+            "top1_hits": 26,
+            "top5_hits": 42,
+            "shortlist_hits": 47
+          },
+          "4": {
+            "queries": 64,
+            "top1_hits": 26,
+            "top5_hits": 47,
+            "shortlist_hits": 52
+          }
+        },
+        "messages": {
+          "0": {
+            "queries": 64,
+            "top1_hits": 10,
+            "top5_hits": 13,
+            "shortlist_hits": 19
+          },
+          "1": {
+            "queries": 64,
+            "top1_hits": 23,
+            "top5_hits": 34,
+            "shortlist_hits": 41
+          },
+          "2": {
+            "queries": 64,
+            "top1_hits": 32,
+            "top5_hits": 55,
+            "shortlist_hits": 57
+          },
+          "3": {
+            "queries": 64,
+            "top1_hits": 38,
+            "top5_hits": 59,
+            "shortlist_hits": 59
+          },
+          "4": {
+            "queries": 64,
+            "top1_hits": 42,
+            "top5_hits": 62,
+            "shortlist_hits": 63
+          }
+        },
+        "search": {
+          "0": {
+            "queries": 64,
+            "top1_hits": 2,
+            "top5_hits": 5,
+            "shortlist_hits": 7
+          },
+          "1": {
+            "queries": 64,
+            "top1_hits": 6,
+            "top5_hits": 10,
+            "shortlist_hits": 12
+          },
+          "2": {
+            "queries": 64,
+            "top1_hits": 10,
+            "top5_hits": 19,
+            "shortlist_hits": 22
+          },
+          "3": {
+            "queries": 64,
+            "top1_hits": 15,
+            "top5_hits": 36,
+            "shortlist_hits": 42
+          },
+          "4": {
+            "queries": 64,
+            "top1_hits": 24,
+            "top5_hits": 49,
+            "shortlist_hits": 53
+          }
+        }
+      },
+      "peak_process_working_set_bytes": 389099520
+    },
+    "neural": {
+      "mode": "Neural",
+      "fixture_sha256": "5e7fe93fc126862da5895d6c7304414c2a49a48303688648349afb3db956fe76",
+      "baseline_sha256": "222253417d0a7a705823ffb7e599a3bcf5d5d3daf4a9d76161ac6b3e555aeaad",
+      "model_manifest_sha256": "5099892d4ea02608c0710c55358c4fd38e5890c9fd061fa3291b89b21cacf140",
+      "per_cell": 64,
+      "shortlist": 8,
+      "context_tokens": 64,
+      "cold_load_ms": 2818.6385,
+      "query_count": 1280,
+      "warm_median_ms": 165.7749,
+      "warm_p95_ms": 229.35410000000002,
+      "warm_max_ms": 396.5521,
+      "failed_queries": 0,
+      "model_file_bytes": 269060552,
+      "baseline_payload_bytes": 16821218,
+      "cells": {
+        "documents": {
+          "0": {
+            "queries": 64,
+            "top1_hits": 6,
+            "top5_hits": 10,
+            "shortlist_hits": 10
+          },
+          "1": {
+            "queries": 64,
+            "top1_hits": 9,
+            "top5_hits": 10,
+            "shortlist_hits": 10
+          },
+          "2": {
+            "queries": 64,
+            "top1_hits": 21,
+            "top5_hits": 23,
+            "shortlist_hits": 23
+          },
+          "3": {
+            "queries": 64,
+            "top1_hits": 38,
+            "top5_hits": 41,
+            "shortlist_hits": 41
+          },
+          "4": {
+            "queries": 64,
+            "top1_hits": 47,
+            "top5_hits": 53,
+            "shortlist_hits": 53
+          }
+        },
+        "email": {
+          "0": {
+            "queries": 64,
+            "top1_hits": 11,
+            "top5_hits": 18,
+            "shortlist_hits": 19
+          },
+          "1": {
+            "queries": 64,
+            "top1_hits": 24,
+            "top5_hits": 30,
+            "shortlist_hits": 31
+          },
+          "2": {
+            "queries": 64,
+            "top1_hits": 32,
+            "top5_hits": 44,
+            "shortlist_hits": 44
+          },
+          "3": {
+            "queries": 64,
+            "top1_hits": 37,
+            "top5_hits": 47,
+            "shortlist_hits": 47
+          },
+          "4": {
+            "queries": 64,
+            "top1_hits": 43,
+            "top5_hits": 52,
+            "shortlist_hits": 52
+          }
+        },
+        "messages": {
+          "0": {
+            "queries": 64,
+            "top1_hits": 13,
+            "top5_hits": 18,
+            "shortlist_hits": 19
+          },
+          "1": {
+            "queries": 64,
+            "top1_hits": 25,
+            "top5_hits": 38,
+            "shortlist_hits": 41
+          },
+          "2": {
+            "queries": 64,
+            "top1_hits": 42,
+            "top5_hits": 56,
+            "shortlist_hits": 57
+          },
+          "3": {
+            "queries": 64,
+            "top1_hits": 50,
+            "top5_hits": 59,
+            "shortlist_hits": 59
+          },
+          "4": {
+            "queries": 64,
+            "top1_hits": 49,
+            "top5_hits": 63,
+            "shortlist_hits": 63
+          }
+        },
+        "search": {
+          "0": {
+            "queries": 64,
+            "top1_hits": 5,
+            "top5_hits": 7,
+            "shortlist_hits": 7
+          },
+          "1": {
+            "queries": 64,
+            "top1_hits": 11,
+            "top5_hits": 12,
+            "shortlist_hits": 12
+          },
+          "2": {
+            "queries": 64,
+            "top1_hits": 18,
+            "top5_hits": 22,
+            "shortlist_hits": 22
+          },
+          "3": {
+            "queries": 64,
+            "top1_hits": 32,
+            "top5_hits": 41,
+            "shortlist_hits": 42
+          },
+          "4": {
+            "queries": 64,
+            "top1_hits": 35,
+            "top5_hits": 53,
+            "shortlist_hits": 53
+          }
+        }
+      },
+      "peak_process_working_set_bytes": 1087586304
+    }
+  },
+  "memory_method": "Windows peak working set sampled from OS high-water counter; unavailable on other platforms. Includes loading and scoring, not model payload alone.",
+  "decision": {
+    "mean_early_top5_gain": 0.045572916666666664,
+    "worst_early_cell_gain": 0.015625,
+    "accuracy_passed": true,
+    "latency_passed": false,
+    "promote": false
+  },
+  "cpu": "AMD Ryzen AI 9 HX 370 w/ Radeon 890M",
+  "date": "2026-10-03",
+  "experiment_source_commit": "f9f42dc26076e6d18173d8dc390d2729e29379c7",
+  "limitations": [
+    "Synthetic diagnostic fixtures, not representative user text or evidence of unseen-user accuracy.",
+    "A bounded top-eight reranker, not unrestricted neural prediction.",
+    "Unquantized CPU F32 without cross-query KV caching; no inference network access.",
+    "Windows measured; macOS performance not measured.",
+    "Peak process working set includes validation, loading and scoring, not just model payload."
+  ]
+}
diff --git a/docs/general-neural-results.md b/docs/general-neural-results.md
new file mode 100644
index 0000000..b348167
--- /dev/null
+++ b/docs/general-neural-results.md
@@ -0,0 +1,102 @@
+# General-purpose neural prediction results
+
+The SmolLM2 reranker improved early top-five accuracy by **4.56 percentage
+points** on these synthetic fixtures, but missed the 20 ms p95 target. It is
+worth optimizing and evaluating on independent everyday writing before any
+production decision. The shipping model and Switchify PC are unchanged.
+
+Measured 2026-10-03 on Windows 11, AMD Ryzen AI 9 HX 370, 24 logical CPUs.
+Candle 0.11.0 used CPU F32 with four Rayon threads. Each mode completed 1,280
+queries, 64 per domain/prefix cell, with zero inference failures.
+
+## Accuracy
+
+These are 96 newly authored diagnostic fixtures, 24 each for messages, email,
+documents and search-style text. They are not real-user observations. AAC was
+not included in the selection objective. The existing control retains its
+historical AAC-weighted training mixture; reranking does not fix its candidate
+vocabulary or recover words absent from its top-eight shortlist.
+
+Early accuracy averages prefixes 0, 1 and 2 with equal domain/cell weights.
+
+| Domain | Current top-one | Neural top-one | Current top-five | Neural top-five | Top-eight ceiling |
+|---|---:|---:|---:|---:|---:|
+| documents | 9.90% | 18.75% | 17.71% | 22.40% | 22.40% |
+| email | 23.96% | 34.90% | 43.23% | 47.92% | 48.96% |
+| messages | 33.85% | 41.67% | 53.12% | 58.33% | 60.94% |
+| search | 9.38% | 17.71% | 17.71% | 21.35% | 21.35% |
+
+| Typed graphemes | Current top-five | Neural top-five | Change |
+|---|---:|---:|---:|
+| 0 | 14.06% | 20.70% | +6.64 pp |
+| 1 | 32.03% | 35.16% | +3.12 pp |
+| 2 | 52.73% | 56.64% | +3.91 pp |
+| 3 | 66.80% | 73.44% | +6.64 pp |
+| 4 | 79.69% | 86.33% | +6.64 pp |
+
+The mean early top-five score rose from 32.94% to 37.50%. Every one of the
+twelve early domain/prefix cells improved, with the smallest gain one hit out
+of 64. The accuracy criterion passed. Prefix rows use independently hash-selected
+target positions, so they do not measure actual keystrokes saved for a fixed
+set of words. Per-domain/per-prefix raw hit counts and candidate coverage are
+available in the JSON report.
+
+## Speed and memory
+
+| Metric | Current engine | Neural reranker |
+|---|---:|---:|
+| Cold load ms | 2543.128 | 2818.639 |
+| Warm median ms | 0.079 | 165.775 |
+| Warm p95 ms | 6.029 | 229.354 |
+| Warm maximum ms | 8.280 | 396.552 |
+| Peak process working set MiB | 371.07 | 1037.20 |
+| Inference failures | 0 | 0 |
+
+The baseline database is 28.42 MiB. The neural weights add 256.60 MiB, plus
+about 2 MiB for the tokenizer and small configuration/notice files. F32 tensors
+and runtime allocations make memory use larger than the stored weights.
+
+Both modes load once and predict entirely in memory. The timed neural path
+includes baseline shortlist generation and whole-word scoring of up to eight
+candidates. Context computation is shared within a query, with no reuse across
+keystrokes. Each candidate has its own cloned context cache. Cold load starts
+after checksum verification and therefore does not represent an OS-cold disk.
+Peak working set includes validation, model loading and scoring. It is not
+comparable to a model-payload estimate.
+
+No local builds or other benchmark processes ran during the full scoring run.
+The 20-query calibration checked execution and timing before the full run; it
+did not change the shortlist, model, fixtures, selection rule or scoring method.
+
+## Decision and next work
+
+Do not promote this implementation. Its accuracy result is encouraging, but
+229 ms p95 is well above 20 ms. Quantization, batching and reuse of context across
+keystrokes are optimization candidates, not measured improvements here. Any
+optimized version must repeat accuracy checks and be tested on Windows and macOS.
+
+A stronger evaluation should use independent, appropriately licensed everyday
+writing with realistic capitalization and punctuation. The current fixtures
+were authored for this experiment, exact training-sentence overlap was rejected,
+and unknown overlap with neural pretraining cannot be excluded. Candidate
+shortlisting also limits how much longer-context reasoning can help.
+
+The [FUTO investigation](futo-investigation.md) located the official English
+model and its custom prediction path. It has not been run here. It remains a
+separate engineering candidate; these measurements do not rank FUTO against
+SmolLM2.
+
+## Validation and reproduction
+
+Production formatting, Clippy with warnings denied, 22 Rust tests and 12 Python
+tests passed locally. The separate Rust experiment passed formatting, Clippy
+and three fake/pure tests covering full-token scores, cache isolation, sentence
+boundaries, Unicode queries and inference errors. Automated tests inject no input.
+
+The production dependency audit passed. The separate experiment documents one
+unmaintained-package exception for Candle's transitive `paste` dependency,
+`RUSTSEC-2024-0436`. It is not a production dependency change.
+
+See the [frozen protocol and reproduction command](general-neural-protocol.md)
+and [aggregate machine-readable results](general-neural-results.json).
+No experimental weights or user text are committed or distributed.
diff --git a/experiments/neural/Cargo.lock b/experiments/neural/Cargo.lock
new file mode 100644
index 0000000..d9a9103
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+dependencies = [
+ "stable_deref_trait",
+ "yoke-derive",
+ "zerofrom",
+]
+
+[[package]]
+name = "yoke-derive"
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+ "proc-macro2",
+ "quote",
+ "syn 3.0.6",
+ "synstructure",
+]
+
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+ "zerocopy-derive",
+]
+
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+ "proc-macro2",
+ "quote",
+ "syn 2.0.119",
+]
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+ "zerofrom-derive",
+]
+
+[[package]]
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+dependencies = [
+ "proc-macro2",
+ "quote",
+ "syn 3.0.6",
+ "synstructure",
+]
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+dependencies = [
+ "crc32fast",
+ "indexmap",
+ "memchr",
+ "typed-path",
+]
+
+[[package]]
+name = "zmij"
+version = "1.0.23"
+source = "registry+https://github.com/rust-lang/crates.io-index"
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diff --git a/experiments/neural/Cargo.toml b/experiments/neural/Cargo.toml
new file mode 100644
index 0000000..b3e2399
--- /dev/null
+++ b/experiments/neural/Cargo.toml
@@ -0,0 +1,20 @@
+[package]
+name = "switchify-neural-experiment"
+version = "0.1.0"
+edition = "2024"
+publish = false
+
+[dependencies]
+anyhow = "1"
+candle-core = "=0.11.0"
+candle-nn = "=0.11.0"
+candle-transformers = "=0.11.0"
+clap = { version = "4", features = ["derive"] }
+serde = { version = "1", features = ["derive"] }
+serde_json = "1"
+sha2 = "0.10"
+switchify-prediction = { path = "../.." }
+tokenizers = { version = "0.22", default-features = false, features = ["fancy-regex"] }
+unicode-segmentation = "1"
+
+[workspace]
diff --git a/experiments/neural/fixtures.json b/experiments/neural/fixtures.json
new file mode 100644
index 0000000..fcf5924
--- /dev/null
+++ b/experiments/neural/fixtures.json
@@ -0,0 +1,106 @@
+{
+  "messages": [
+    "I'm running a little late because the train stopped outside the station.",
+    "Could you bring my blue jacket when you come over this evening?",
+    "We should book a table somewhere near the cinema before the weekend.",
+    "The delivery arrived this morning and I left your parcel beside the stairs.",
+    "Let me know when you finish work and we can arrange dinner.",
+    "I thought the meeting was tomorrow but the calendar says Thursday afternoon.",
+    "My phone battery is nearly empty so I'll call you after lunch.",
+    "Thanks for helping me move the furniture into the spare bedroom yesterday.",
+    "Do you remember which supermarket sells the coffee we bought last month?",
+    "I'll pick up some vegetables and bread on the way back home.",
+    "The weather looks better tomorrow so we could walk along the beach.",
+    "Your photographs from the holiday look amazing especially the mountain views.",
+    "Please send me the address again because I can't find the message.",
+    "We have enough chairs but somebody needs to bring another folding table.",
+    "I haven't watched the final episode yet so please don't spoil anything.",
+    "The tickets are cheaper if we travel before the morning rush hour.",
+    "Can you check whether the front door is locked before you leave?",
+    "I found your headphones underneath the sofa while I was cleaning today.",
+    "Let's choose another restaurant because the place we wanted is fully booked.",
+    "The children are staying with their grandparents until late on Sunday evening.",
+    "I've ordered a replacement charger and it should arrive early next week.",
+    "Would you prefer to meet outside the library or beside the station?",
+    "There is some leftover pasta in the fridge if you want lunch.",
+    "We enjoyed the concert although finding somewhere to park was quite difficult."
+  ],
+  "email": [
+    "Thank you for sending the revised proposal and the updated delivery schedule.",
+    "Please find the requested documents attached for your review before our meeting.",
+    "Could you confirm whether the replacement parts will arrive by Friday afternoon?",
+    "I would appreciate a breakdown of the additional charges on this invoice.",
+    "Following our discussion yesterday we have adjusted the project milestones accordingly.",
+    "The finance team needs your approval before processing the outstanding supplier payment.",
+    "Unfortunately I am unavailable on Tuesday but Wednesday morning would suit me.",
+    "Please include the purchase reference when replying so we can locate your order.",
+    "We are reviewing the applications and expect to contact shortlisted candidates shortly.",
+    "Your subscription renewal has been processed and the receipt is attached below.",
+    "I noticed that the contact details in the shared document are outdated.",
+    "Would it be possible to extend the deadline until the following Monday?",
+    "The customer reported that the replacement device still displays the same error.",
+    "We have reserved the conference room for the training session next week.",
+    "Please let us know if you require any further information about registration.",
+    "I am writing to request a quotation for the equipment listed below.",
+    "The updated contract reflects the changes agreed during our last telephone conversation.",
+    "Thank you for your patience while our technical team investigates this issue.",
+    "We will send the final agenda once all speakers have confirmed attendance.",
+    "Please ensure that each expense claim includes a copy of the original receipt.",
+    "I have forwarded your enquiry to the colleague responsible for international deliveries.",
+    "The maintenance work is scheduled outside normal office hours to minimise disruption.",
+    "Could everyone review the attached spreadsheet and correct any missing information?",
+    "We look forward to welcoming your team to our offices next month."
+  ],
+  "documents": [
+    "The experiment compared three methods under identical conditions to reduce measurement bias.",
+    "Participants completed a short questionnaire before beginning the practical assessment session.",
+    "The proposed timetable allows additional preparation time between the morning presentations.",
+    "A reliable backup process should preserve recent changes without interrupting normal work.",
+    "The building includes a shared kitchen and several meeting rooms on each floor.",
+    "This section explains how to replace the filter and clean the surrounding housing.",
+    "The committee recommended further consultation before making a decision about the redevelopment.",
+    "Changes in customer demand affected both production planning and warehouse storage requirements.",
+    "The first chapter describes the historical background and introduces the main research questions.",
+    "Employees should report damaged equipment to their supervisor before attempting any repairs.",
+    "The new route connects the residential neighbourhood with the hospital and central station.",
+    "Each observation was recorded separately and checked against the original laboratory notes.",
+    "The installation process creates a shortcut and copies the required files automatically.",
+    "A small increase in temperature caused the material to expand along its length.",
+    "The report identifies several opportunities to improve communication between regional offices.",
+    "Successful applicants will receive written confirmation together with instructions for the next stage.",
+    "The museum collection includes paintings and photographs donated by local residents.",
+    "Regular maintenance helps prevent unexpected failures and extends the useful life of equipment.",
+    "The survey results suggest that visitors value clear information and convenient opening hours.",
+    "A detailed inventory should include the location and condition of every stored item.",
+    "The software displays a warning when the available storage falls below the recommended level.",
+    "The final design includes wider entrances and improved lighting throughout the public areas.",
+    "Researchers compared the original measurements with results collected during the second trial.",
+    "This guide provides practical examples of common problems and explains possible solutions."
+  ],
+  "search": [
+    "best lightweight waterproof jackets for walking during rainy weather",
+    "how to replace a damaged charging cable on a laptop",
+    "restaurants serving vegetarian meals near the central railway station",
+    "compare electricity tariffs for apartments with electric heating systems",
+    "train timetables between the airport and the city centre",
+    "instructions for cleaning reusable coffee filters without harsh chemicals",
+    "comfortable office chairs with adjustable armrests and lumbar support",
+    "weekend activities for families visiting the natural history museum",
+    "troubleshooting wireless headphones that disconnect during telephone conversations",
+    "simple recipes using roasted vegetables and leftover cooked chicken",
+    "opening hours for the public swimming pool during school holidays",
+    "how to recover an accidentally deleted folder from a backup",
+    "affordable accommodation within walking distance of the conference centre",
+    "cycling routes suitable for beginners around the coastal villages",
+    "portable monitors compatible with laptops using standard display connections",
+    "average rainfall during autumn in popular European holiday destinations",
+    "where to recycle broken household appliances and electronic equipment",
+    "beginner photography courses covering lighting composition and camera settings",
+    "quiet washing machines with efficient programmes for delicate clothing",
+    "requirements for renewing an expired passport before travelling abroad",
+    "comparison of cloud storage services offering automatic photograph backups",
+    "how to transfer calendar appointments between different email accounts",
+    "replacement cushions for outdoor furniture exposed to winter weather",
+    "local evening classes teaching conversational Spanish to complete beginners"
+  ]
+}
diff --git a/experiments/neural/src/main.rs b/experiments/neural/src/main.rs
new file mode 100644
index 0000000..ac2f5af
--- /dev/null
+++ b/experiments/neural/src/main.rs
@@ -0,0 +1,476 @@
+use anyhow::{Context, Result, bail, ensure};
+use candle_core::{DType, Device, Tensor};
+use candle_nn::VarBuilder;
+use candle_transformers::models::llama::{Cache, Llama, LlamaConfig};
+use clap::{Parser, ValueEnum};
+use serde::{Deserialize, Serialize};
+use sha2::{Digest, Sha256};
+use std::{
+    collections::{BTreeMap, BTreeSet},
+    fs,
+    io::Read,
+    path::{Path, PathBuf},
+    time::Instant,
+};
+use switchify_prediction::{Options, Predictor, sentences};
+use tokenizers::Tokenizer;
+use unicode_segmentation::UnicodeSegmentation;
+
+const BASELINE_SHA: &str = "222253417d0a7a705823ffb7e599a3bcf5d5d3daf4a9d76161ac6b3e555aeaad";
+const TRAIN_SHA: &str = "ff724de5ab3b6e609ed77b699c51287fc82f79d026fdecfa4892939abfbb2834";
+const SHORTLIST: usize = 8;
+const CONTEXT_TOKENS: usize = 64;
+
+#[derive(Clone, Copy, Debug, ValueEnum, Serialize)]
+enum Mode {
+    Baseline,
+    Neural,
+}
+
+#[derive(Parser)]
+struct Args {
+    #[arg(long)]
+    baseline: PathBuf,
+    #[arg(long)]
+    model: PathBuf,
+    #[arg(long)]
+    manifest: PathBuf,
+    #[arg(long)]
+    fixtures: PathBuf,
+    #[arg(long)]
+    training: PathBuf,
+    #[arg(long)]
+    output: PathBuf,
+    #[arg(long, value_enum)]
+    mode: Mode,
+    /// Use 64 for the frozen experiment; smaller values are calibration only.
+    #[arg(long, default_value_t = 64)]
+    per_cell: usize,
+}
+
+fn sha(path: &Path) -> Result<String> {
+    let mut file = fs::File::open(path)?;
+    let mut h = Sha256::new();
+    let mut buffer = [0; 65536];
+    loop {
+        let n = file.read(&mut buffer)?;
+        if n == 0 {
+            break;
+        }
+        h.update(&buffer[..n]);
+    }
+    Ok(format!("{:x}", h.finalize()))
+}
+
+#[derive(Deserialize)]
+struct Source {
+    sha256: String,
+}
+#[derive(Deserialize)]
+struct Manifest {
+    files: BTreeMap<String, Source>,
+}
+
+fn verify_model(model: &Path, manifest: &Path) -> Result<()> {
+    let data: Manifest = serde_json::from_slice(&fs::read(manifest)?)?;
+    for name in ["config.json", "tokenizer.json", "model.safetensors"] {
+        let expected = data.files.get(name).context("Missing model checksum")?;
+        ensure!(
+            sha(&model.join(name))? == expected.sha256,
+            "Model checksum mismatch: {name}"
+        );
+    }
+    Ok(())
+}
+
+fn boundary(text: &str) -> bool {
+    text.chars()
+        .next()
+        .is_some_and(|c| c.is_whitespace() || c.is_numeric() || ".!?;:,()[]{}\"-/".contains(c))
+}
+
+fn log_prob(logits: &[f32], token: usize) -> f64 {
+    let maximum = logits.iter().copied().fold(f32::NEG_INFINITY, f32::max) as f64;
+    logits[token] as f64
+        - maximum
+        - logits
+            .iter()
+            .map(|x| (*x as f64 - maximum).exp())
+            .sum::<f64>()
+            .ln()
+}
+
+fn log_boundary(logits: &[f32], ids: &[usize]) -> f64 {
+    let maximum = logits.iter().copied().fold(f32::NEG_INFINITY, f32::max) as f64;
+    let all: f64 = logits.iter().map(|x| (*x as f64 - maximum).exp()).sum();
+    let end: f64 = ids
+        .iter()
+        .map(|&i| (logits[i] as f64 - maximum).exp())
+        .sum();
+    (end / all).ln()
+}
+
+fn word_score<S: Clone>(
+    tokens: &[u32],
+    first_logits: &[f32],
+    cache: &S,
+    position: usize,
+    boundary_ids: &[usize],
+    mut forward: impl FnMut(u32, usize, &mut S) -> Result<Vec<f32>>,
+) -> Result<f64> {
+    ensure!(!tokens.is_empty(), "Empty candidate tokenization");
+    let mut branch = cache.clone();
+    let mut logits = first_logits.to_vec();
+    let mut score = 0.;
+    for (index, &token) in tokens.iter().enumerate() {
+        score += log_prob(&logits, token as usize);
+        logits = forward(token, position + index, &mut branch)?;
+    }
+    score += log_boundary(&logits, boundary_ids);
+    ensure!(score.is_finite(), "Non-finite neural score");
+    Ok(score)
+}
+
+struct Neural {
+    model: Llama,
+    tokenizer: Tokenizer,
+    empty_cache: Cache,
+    boundary_ids: Vec<usize>,
+}
+
+impl Neural {
+    fn load(path: &Path) -> Result<Self> {
+        let tokenizer =
+            Tokenizer::from_file(path.join("tokenizer.json")).map_err(anyhow::Error::msg)?;
+        let cfg: LlamaConfig = serde_json::from_slice(&fs::read(path.join("config.json"))?)?;
+        let config = cfg.into_config(false);
+        ensure!(
+            config.bos_token_id == Some(0),
+            "Expected pinned SmolLM2 BOS token"
+        );
+        let empty_cache = Cache::new(true, DType::F32, &config, &Device::Cpu)?;
+        // Safe loader owns its bytes; no mmap lifetime or externally mutable mapping.
+        let weights = fs::read(path.join("model.safetensors"))?;
+        let vb = VarBuilder::from_buffered_safetensors(weights, DType::F32, &Device::Cpu)?;
+        let model = Llama::load(vb, &config)?;
+        let mut boundary_ids = vec![0]; // Pinned EOS.
+        for id in 1..config.vocab_size {
+            let decoded = tokenizer
+                .decode(&[id as u32], false)
+                .map_err(anyhow::Error::msg)?;
+            if boundary(&decoded) {
+                boundary_ids.push(id);
+            }
+        }
+        Ok(Self {
+            model,
+            tokenizer,
+            empty_cache,
+            boundary_ids,
+        })
+    }
+
+    fn forward(&self, ids: &[u32], position: usize, cache: &mut Cache) -> Result<Vec<f32>> {
+        let input = Tensor::new(ids, &Device::Cpu)?.unsqueeze(0)?;
+        Ok(self
+            .model
+            .forward(&input, position, cache)?
+            .squeeze(0)?
+            .to_vec1()?)
+    }
+
+    fn rank(&self, before: &str, candidates: &[String]) -> Result<Vec<String>> {
+        if candidates.is_empty() {
+            return Ok(Vec::new());
+        }
+        let encoded = self
+            .tokenizer
+            .encode(before, false)
+            .map_err(anyhow::Error::msg)?;
+        let ids = encoded.get_ids();
+        let mut context = vec![0];
+        context.extend_from_slice(&ids[ids.len().saturating_sub(CONTEXT_TOKENS - 1)..]);
+        let mut cache = self.empty_cache.clone();
+        let first_logits = self.forward(&context, 0, &mut cache)?;
+        let mut scored = Vec::new();
+        for (rank, word) in candidates.iter().enumerate() {
+            let text = if before.is_empty() {
+                word.clone()
+            } else {
+                format!(" {word}")
+            };
+            let encoded = self
+                .tokenizer
+                .encode(text, false)
+                .map_err(anyhow::Error::msg)?;
+            let tokens = encoded.get_ids();
+            let score = word_score(
+                tokens,
+                &first_logits,
+                &cache,
+                context.len(),
+                &self.boundary_ids,
+                |token, position, branch| self.forward(&[token], position, branch),
+            )?;
+            scored.push((score, rank, word.clone()));
+        }
+        scored.sort_by(|a, b| b.0.total_cmp(&a.0).then(a.1.cmp(&b.1)));
+        Ok(scored.into_iter().take(5).map(|x| x.2).collect())
+    }
+}
+
+#[derive(Clone, Debug)]
+struct Query {
+    domain: String,
+    prefix_chars: usize,
+    before: String,
+    prefix: String,
+    target: String,
+    key: String,
+}
+
+fn queries(
+    fixtures: &BTreeMap<String, Vec<String>>,
+    training: &BTreeSet<String>,
+    per_cell: usize,
+) -> Result<Vec<Query>> {
+    ensure!(per_cell > 0, "per-cell must be positive");
+    let mut result = Vec::new();
+    for (domain, texts) in fixtures {
+        let unique: BTreeSet<String> = texts
+            .iter()
+            .flat_map(|t| sentences(t))
+            .map(|s| s.join(" "))
+            .collect();
+        ensure!(
+            unique.is_disjoint(training),
+            "Fixture/training overlap in {domain}"
+        );
+        for prefix_chars in 0..=4 {
+            let mut cell = Vec::new();
+            let mut seen = BTreeSet::new();
+            for sentence in &unique {
+                let words: Vec<_> = sentence.split_whitespace().collect();
+                for (index, target) in words.iter().enumerate() {
+                    let graphemes: Vec<_> = target.graphemes(true).collect();
+                    if graphemes.len() <= prefix_chars {
+                        continue;
+                    }
+                    let before = words[..index].join(" ");
+                    let prefix = graphemes[..prefix_chars].concat();
+                    if !seen.insert((before.clone(), prefix.clone(), target.to_string())) {
+                        continue;
+                    }
+                    let key = switchify_prediction::digest(
+                        format!("{domain}\n{sentence}\n{index}\n{prefix_chars}").as_bytes(),
+                    );
+                    cell.push(Query {
+                        domain: domain.clone(),
+                        prefix_chars,
+                        before,
+                        prefix,
+                        target: target.to_string(),
+                        key,
+                    });
+                }
+            }
+            cell.sort_by(|a, b| a.key.cmp(&b.key));
+            ensure!(
+                cell.len() >= per_cell,
+                "Not enough queries in {domain}/{prefix_chars}: {}",
+                cell.len()
+            );
+            result.extend(cell.into_iter().take(per_cell));
+        }
+    }
+    Ok(result)
+}
+
+#[derive(Default, Serialize)]
+struct Cell {
+    queries: usize,
+    top1_hits: usize,
+    top5_hits: usize,
+    shortlist_hits: usize,
+}
+#[derive(Serialize)]
+struct Report {
+    mode: Mode,
+    fixture_sha256: String,
+    baseline_sha256: String,
+    model_manifest_sha256: String,
+    per_cell: usize,
+    shortlist: usize,
+    context_tokens: usize,
+    cold_load_ms: f64,
+    query_count: usize,
+    warm_median_ms: f64,
+    warm_p95_ms: f64,
+    warm_max_ms: f64,
+    failed_queries: usize,
+    model_file_bytes: u64,
+    baseline_payload_bytes: usize,
+    cells: BTreeMap<String, BTreeMap<usize, Cell>>,
+}
+
+fn percentile(times: &[f64], percent: usize) -> f64 {
+    times[(times.len() * percent).div_ceil(100).saturating_sub(1)]
+}
+
+fn main() -> Result<()> {
+    let args = Args::parse();
+    ensure!(!args.output.exists(), "Output already exists");
+    ensure!(
+        sha(&args.baseline)? == BASELINE_SHA,
+        "Baseline checksum mismatch"
+    );
+    ensure!(
+        sha(&args.training)? == TRAIN_SHA,
+        "Training checksum mismatch"
+    );
+    let fixtures: BTreeMap<String, Vec<String>> =
+        serde_json::from_slice(&fs::read(&args.fixtures)?)?;
+    let training = fs::read_to_string(&args.training)?
+        .lines()
+        .map(str::to_string)
+        .collect();
+    let cases = queries(&fixtures, &training, args.per_cell)?;
+    if cases.is_empty() {
+        bail!("Empty workload");
+    }
+    verify_model(&args.model, &args.manifest)?;
+    let start = Instant::now();
+    let baseline = Predictor::open(&args.baseline, None)?;
+    let neural = match args.mode {
+        Mode::Baseline => None,
+        Mode::Neural => Some(Neural::load(&args.model)?),
+    };
+    let cold_load_ms = start.elapsed().as_secs_f64() * 1000.;
+    let predict = |q: &Query| -> Result<(Vec<String>, Vec<String>)> {
+        let candidates: Vec<_> = baseline
+            .predict(
+                &q.before,
+                &q.prefix,
+                Options {
+                    limit: SHORTLIST,
+                    min_chars: 0,
+                    ..Options::default()
+                },
+            )
+            .into_iter()
+            .map(|s| s.word)
+            .collect();
+        let words = match &neural {
+            Some(n) => n.rank(&q.before, &candidates)?,
+            None => candidates.iter().take(5).cloned().collect(),
+        };
+        ensure!(
+            words.iter().all(|w| w.starts_with(&q.prefix)),
+            "Invalid prefix completion"
+        );
+        Ok((words, candidates))
+    };
+    // Untimed prime. Each measured request starts with a fresh neural context cache.
+    predict(&cases[0])?;
+    let mut times = Vec::new();
+    let mut cells: BTreeMap<String, BTreeMap<usize, Cell>> = BTreeMap::new();
+    for (index, q) in cases.iter().enumerate() {
+        let start = Instant::now();
+        let (words, candidates) = predict(q)?;
+        times.push(start.elapsed().as_secs_f64() * 1000.);
+        let cell = cells
+            .entry(q.domain.clone())
+            .or_default()
+            .entry(q.prefix_chars)
+            .or_default();
+        cell.queries += 1;
+        cell.top1_hits += usize::from(words.first() == Some(&q.target));
+        cell.top5_hits += usize::from(words.contains(&q.target));
+        cell.shortlist_hits += usize::from(candidates.contains(&q.target));
+        if index % 50 == 0 {
+            eprintln!("Scored {}/{}", index + 1, cases.len());
+        }
+    }
+    times.sort_by(f64::total_cmp);
+    let report = Report {
+        mode: args.mode,
+        fixture_sha256: sha(&args.fixtures)?,
+        baseline_sha256: sha(&args.baseline)?,
+        model_manifest_sha256: sha(&args.manifest)?,
+        per_cell: args.per_cell,
+        shortlist: SHORTLIST,
+        context_tokens: CONTEXT_TOKENS,
+        cold_load_ms,
+        query_count: times.len(),
+        warm_median_ms: percentile(&times, 50),
+        warm_p95_ms: percentile(&times, 95),
+        warm_max_ms: *times.last().unwrap(),
+        failed_queries: 0,
+        model_file_bytes: fs::metadata(args.model.join("model.safetensors"))?.len(),
+        baseline_payload_bytes: baseline.model_payload_bytes(),
+        cells,
+    };
+    fs::write(&args.output, serde_json::to_vec_pretty(&report)?)?;
+    Ok(())
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+    #[test]
+    fn whole_word_scoring_uses_every_token_and_isolates_branches() {
+        let context = vec![99];
+        let forward = |token: u32, position: usize, branch: &mut Vec<u32>| {
+            assert_eq!(position, branch.len());
+            branch.push(token);
+            Ok(vec![0., 0., 0., 0.])
+        };
+        let one = word_score(&[1], &[0.; 4], &context, 1, &[0, 3], forward).unwrap();
+        let two = word_score(&[1, 2], &[0.; 4], &context, 1, &[0, 3], forward).unwrap();
+        assert!((one.exp() - 0.125).abs() < 1e-9);
+        assert!((two.exp() - 0.03125).abs() < 1e-9);
+        assert_eq!(context, vec![99]);
+        assert!(word_score(&[], &[0.; 4], &context, 1, &[0], forward).is_err());
+        assert!(
+            word_score(&[1], &[0.; 4], &context, 1, &[0], |_, _, _| bail!(
+                "fake inference failure"
+            ))
+            .is_err()
+        );
+    }
+    #[test]
+    fn unicode_boundaries_and_sentence_context() {
+        let fixtures = BTreeMap::from([(
+            "test".into(),
+            vec![
+                "Earlier sentence. Café’s résumé includes naïveté and multilingual communication."
+                    .into(),
+            ],
+        )]);
+        let cases = queries(&fixtures, &BTreeSet::new(), 1).unwrap();
+        assert_eq!(cases.len(), 5);
+        assert!(cases.iter().all(|q| !q.before.contains("sentence café")));
+        assert_eq!(
+            queries(&fixtures, &BTreeSet::new(), 1)
+                .unwrap()
+                .iter()
+                .map(|q| &q.key)
+                .collect::<Vec<_>>(),
+            cases.iter().map(|q| &q.key).collect::<Vec<_>>()
+        );
+        assert!(queries(&fixtures, &BTreeSet::from(["earlier sentence".into()]), 1).is_err());
+        assert!(queries(&fixtures, &BTreeSet::new(), 0).is_err());
+        assert!(boundary(" next"));
+        assert!(boundary("."));
+        assert!(!boundary("ing"));
+        assert!(!boundary("'s"));
+        assert!(!boundary(""));
+    }
+    #[test]
+    fn probabilities_and_percentiles() {
+        let logits = [0., 0., 0., 0.];
+        assert!((log_prob(&logits, 1).exp() - 0.25).abs() < 1e-9);
+        assert!((log_boundary(&logits, &[0, 2]).exp() - 0.5).abs() < 1e-9);
+        assert_eq!(percentile(&[1., 2., 3., 4.], 95), 4.);
+    }
+}
diff --git a/general-neural-source-manifest.json b/general-neural-source-manifest.json
new file mode 100644
index 0000000..c6c54d6
--- /dev/null
+++ b/general-neural-source-manifest.json
@@ -0,0 +1,26 @@
+{
+  "model": "HuggingFaceTB/SmolLM2-135M",
+  "revision": "93efa2f097d58c2a74874c7e644dbc9b0cee75a2",
+  "files": {
+    "config.json": {
+      "url": "https://huggingface.co/HuggingFaceTB/SmolLM2-135M/resolve/93efa2f097d58c2a74874c7e644dbc9b0cee75a2/config.json",
+      "sha256": "1d556eab73b69c7f11f64c557a2f9c6f440bd4c6b89bb2584a6b498c92603843",
+      "bytes": 704
+    },
+    "tokenizer.json": {
+      "url": "https://huggingface.co/HuggingFaceTB/SmolLM2-135M/resolve/93efa2f097d58c2a74874c7e644dbc9b0cee75a2/tokenizer.json",
+      "sha256": "9ca9acddb6525a194ec8ac7a87f24fbba7232a9a15ffa1af0c1224fcd888e47c",
+      "bytes": 2104556
+    },
+    "model.safetensors": {
+      "url": "https://huggingface.co/HuggingFaceTB/SmolLM2-135M/resolve/93efa2f097d58c2a74874c7e644dbc9b0cee75a2/model.safetensors",
+      "sha256": "80521b40281d6ce74e35c9282c22539e75aa0ac8578892b2a59955ef78d55da1",
+      "bytes": 269060552
+    },
+    "README.md": {
+      "url": "https://huggingface.co/HuggingFaceTB/SmolLM2-135M/resolve/93efa2f097d58c2a74874c7e644dbc9b0cee75a2/README.md",
+      "sha256": "d1ba68cae64a89b6b434b11526e6e2271ee5ffd2c914ec35ed515f9d84c6085c",
+      "bytes": 6340
+    }
+  }
+}
diff --git a/scripts/general_neural_experiment.py b/scripts/general_neural_experiment.py
new file mode 100644
index 0000000..d665c16
--- /dev/null
+++ b/scripts/general_neural_experiment.py
@@ -0,0 +1,114 @@
+#!/usr/bin/env python3
+"""Fetch pinned model files, then benchmark two offline Rust processes."""
+import argparse
+import ctypes
+import json
+import os
+from pathlib import Path
+import platform
+import subprocess
+import tempfile
+import time
+
+from aac_experiment import sha
+
+ROOT = Path(__file__).resolve().parent.parent
+MANIFEST = ROOT / 'general-neural-source-manifest.json'
+
+
+def fetch(directory):
+    directory.mkdir(parents=True, exist_ok=True)
+    sources = json.loads(MANIFEST.read_text(encoding='utf-8'))
+    for name, source in sources['files'].items():
+        path = directory / name
+        if not path.exists():
+            with tempfile.TemporaryDirectory(dir=directory) as temporary:
+                downloaded = Path(temporary) / 'source'
+                subprocess.run(['curl', '-fsSL', '--retry', '3', '--max-time', '600',
+                                '-o', str(downloaded), source['url']], check=True)
+                if sha(downloaded) != source['sha256']:
+                    raise ValueError(f'Corrupt download: {name}')
+                downloaded.replace(path)
+        if sha(path) != source['sha256']:
+            raise ValueError(f'Corrupt cached source: {name}')
+
+
+def windows_peak(process):
+    class Counters(ctypes.Structure):
+        _fields_ = [('cb', ctypes.c_ulong), ('faults', ctypes.c_ulong)] + [
+            (name, ctypes.c_size_t) for name in ('peak_working_set', 'working_set',
+             'peak_paged', 'paged', 'peak_nonpaged', 'nonpaged', 'pagefile', 'peak_pagefile')]
+    counters = Counters()
+    counters.cb = ctypes.sizeof(counters)
+    function = ctypes.WinDLL('psapi', use_last_error=True).GetProcessMemoryInfo
+    function.argtypes = [ctypes.c_void_p, ctypes.POINTER(Counters), ctypes.c_ulong]
+    function.restype = ctypes.c_int
+    if not function(int(process._handle), ctypes.byref(counters), counters.cb):
+        return None
+    return counters.peak_working_set
+
+
+def run(command, env):
+    peak = None
+    process = subprocess.Popen(command, env=env, cwd=ROOT)
+    while process.poll() is None:
+        if os.name == 'nt':
+            current = windows_peak(process)
+            if current is not None:
+                peak = max(peak or 0, current)
+        time.sleep(0.05)
+    if process.returncode:
+        raise subprocess.CalledProcessError(process.returncode, command)
+    return peak
+
+
+def decision(reports):
+    gains = []
+    for domain, cells in reports['baseline']['cells'].items():
+        for prefix in ('0', '1', '2'):
+            base, neural = cells[prefix], reports['neural']['cells'][domain][prefix]
+            if base['queries'] != neural['queries']:
+                raise ValueError('Mismatched query counts')
+            gains.append((neural['top5_hits'] - base['top5_hits']) / base['queries'])
+    mean = sum(gains) / len(gains)
+    return {'mean_early_top5_gain': mean, 'worst_early_cell_gain': min(gains),
+            'accuracy_passed': mean > 0 and min(gains) >= -0.01,
+            'latency_passed': reports['neural']['warm_p95_ms'] < 20,
+            'promote': False}
+
+
+def main():
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument('--baseline', required=True, type=Path)
+    parser.add_argument('--output', type=Path, default=ROOT / 'artifacts/general-neural')
+    parser.add_argument('--fetch-only', action='store_true')
+    args = parser.parse_args()
+    out = args.output.resolve()
+    if any((out / f'{name}.json').exists() for name in ('baseline', 'neural', 'results')):
+        raise ValueError('Previous results exist; use a fresh output directory')
+    fetch(out / 'model')
+    if args.fetch_only:
+        return
+    exe = ROOT / 'target/neural/release' / ('switchify-neural-experiment.exe' if os.name == 'nt' else 'switchify-neural-experiment')
+    env = dict(os.environ, RAYON_NUM_THREADS='4')
+    report = {'protocol': 'general-neural-v1', 'hardware': f'{platform.platform()}; {platform.machine()}; {os.cpu_count()} logical CPUs',
+              'rayon_threads': 4, 'dtype': 'F32', 'device': 'CPU', 'executable_sha256': sha(exe),
+              'fixture_sha256': sha(ROOT / 'experiments/neural/fixtures.json'),
+              'reports': {}, 'memory_method': 'Windows peak working set sampled from OS high-water counter; unavailable on other platforms. Includes loading and scoring, not model payload alone.'}
+    for mode in ('baseline', 'neural'):
+        dest = out / f'{mode}.json'
+        command = [str(exe), '--baseline', str(args.baseline.resolve()), '--model', str(out / 'model'),
+                   '--manifest', str(MANIFEST), '--fixtures', str(ROOT / 'experiments/neural/fixtures.json'),
+                   '--training', str(ROOT / 'data/aac-oanc/prepared/candidate.txt'),
+                   '--mode', mode, '--output', str(dest)]
+        peak = run(command, env)
+        result = json.loads(dest.read_text(encoding='utf-8'))
+        result['peak_process_working_set_bytes'] = peak
+        report['reports'][mode] = result
+    report['decision'] = decision(report['reports'])
+    (out / 'results.json').write_bytes((json.dumps(report, indent=2) + '\n').encode())
+    print(json.dumps(report['decision'], indent=2))
+
+
+if __name__ == '__main__':
+    main()
diff --git a/scripts/test_general_neural_experiment.py b/scripts/test_general_neural_experiment.py
new file mode 100644
index 0000000..a265bab
--- /dev/null
+++ b/scripts/test_general_neural_experiment.py
@@ -0,0 +1,28 @@
+import copy
+import unittest
+
+from general_neural_experiment import decision
+
+
+class GeneralNeuralTests(unittest.TestCase):
+    def test_general_domain_gates(self):
+        base = {'cells': {d: {str(p): {'queries': 100, 'top5_hits': 50} for p in range(5)}
+                          for d in ['messages', 'email', 'documents', 'search']}, 'warm_p95_ms': 5}
+        neural = copy.deepcopy(base)
+        for cells in neural['cells'].values():
+            for cell in cells.values():
+                cell['top5_hits'] = 52
+        reports = {'baseline': base, 'neural': neural}
+        self.assertTrue(decision(reports)['accuracy_passed'])
+        neural['cells']['email']['2']['top5_hits'] = 48
+        self.assertFalse(decision(reports)['accuracy_passed'])
+        neural['warm_p95_ms'] = 20
+        self.assertFalse(decision(reports)['latency_passed'])
+        self.assertFalse(decision(reports)['promote'])
+        neural['cells']['email']['2']['queries'] = 99
+        with self.assertRaises(ValueError):
+            decision(reports)
+
+
+if __name__ == '__main__':
+    unittest.main()