From 8ad6f58ebfccfed00385dfaec6d07e679baa2867 Mon Sep 17 00:00:00 2001 From: Claude Date: Sat, 30 May 2026 17:31:08 +0000 Subject: [PATCH 1/7] feat(arm-discovery): transcode Aerial+ rule mining to Rust + ruff DTO/SPO/codegen synergy map MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit New standalone zero-dep crate `lance-graph-arm-discovery` (D-ARM-13) — the Rust transcode of Aerial+ neurosymbolic association-rule mining (Karabulut 2025, arXiv 2504.19354v1), the upstream runtime-data proposer leg of streaming-arm-nars-discovery-v1. Supersedes the plan §14 deferral ("autoencoder stays in Python") per the user's transcode directive. Faithful port: - encode: one-hot FeatureSpec + Dataset + support/confidence counting - aerial::autoencoder: under-complete denoising AE, per-feature softmax + cross-entropy, hand-written backprop, seeded SplitMix64 (reproducible) - aerial::extract: Algorithm 1 reconstruction-probe rule extraction (mark antecedent, uniform elsewhere, forward, tau_a/tau_c gates), then support/confidence confirmed on the data - translator::arm_to_nars: (support, confidence, n) -> NARS (f, c) verbatim per paper section 2/3.3; (f,c) == TruthValue::new == ruff_spo_triplet::Triple - ndjson: emits the exact {"s","p","o","f","c"} line the SPO store loader reads Verified standalone: 35/35 tests pass, clippy -D warnings clean. The nondeterministic AE is kept out of the deterministic lance-graph compile path (excluded crate, feature-gated, seeded) and emits CandidateRule *proposals* gated by the downstream ratification council. Synergy map (.claude/knowledge/aerial-arm-ruff-spo-codegen-synergies.md): the Aerial leg is the runtime-data frontend of a three-frontend / one-substrate / two-codegen bracket whose substrate (ruff_spo_triplet::Triple) and codegen legs (ruff_python_codegen || op_emitter.rs) already exist. ruff_python_dto_check is the static-AST sibling frontend. Key gap surfaced: ruff_spo_triplet::Predicate is a closed vocabulary with no implication/association predicate, so loading ARM rules through that ndjson path needs `Implies` added first (D-ARM-SYN-1, council-gated). D-ARM-SYN-2/3 wire the ModelGraph adapter + ArmDiscovered truth calibration. Board: STATUS_BOARD (D-ARM-13 + D-ARM-SYN-1/2/3), EPIPHANIES (E-DISCOVERY-CODEGEN-BRACKET-1 realised), LATEST_STATE (inventory), AGENT_LOG. https://claude.ai/code/session_012SorR8UbtEvYmbX8cXftj7 --- .claude/board/AGENT_LOG.md | 17 + .claude/board/EPIPHANIES.md | 18 + .claude/board/LATEST_STATE.md | 8 + .claude/board/STATUS_BOARD.md | 4 + .../aerial-arm-ruff-spo-codegen-synergies.md | 205 +++++++++++ Cargo.toml | 5 + crates/lance-graph-arm-discovery/Cargo.lock | 7 + crates/lance-graph-arm-discovery/Cargo.toml | 33 ++ crates/lance-graph-arm-discovery/README.md | 75 ++++ .../src/aerial/autoencoder.rs | 348 ++++++++++++++++++ .../src/aerial/extract.rs | 333 +++++++++++++++++ .../src/aerial/mod.rs | 214 +++++++++++ .../src/aerial/rng.rs | 125 +++++++ .../lance-graph-arm-discovery/src/encode.rs | 246 +++++++++++++ crates/lance-graph-arm-discovery/src/lib.rs | 71 ++++ .../lance-graph-arm-discovery/src/ndjson.rs | 140 +++++++ crates/lance-graph-arm-discovery/src/rule.rs | 130 +++++++ .../src/translator.rs | 219 +++++++++++ 18 files changed, 2198 insertions(+) create mode 100644 .claude/knowledge/aerial-arm-ruff-spo-codegen-synergies.md create mode 100644 crates/lance-graph-arm-discovery/Cargo.lock create mode 100644 crates/lance-graph-arm-discovery/Cargo.toml create mode 100644 crates/lance-graph-arm-discovery/README.md create mode 100644 crates/lance-graph-arm-discovery/src/aerial/autoencoder.rs create mode 100644 crates/lance-graph-arm-discovery/src/aerial/extract.rs create mode 100644 crates/lance-graph-arm-discovery/src/aerial/mod.rs create mode 100644 crates/lance-graph-arm-discovery/src/aerial/rng.rs create mode 100644 crates/lance-graph-arm-discovery/src/encode.rs create mode 100644 crates/lance-graph-arm-discovery/src/lib.rs create mode 100644 crates/lance-graph-arm-discovery/src/ndjson.rs create mode 100644 crates/lance-graph-arm-discovery/src/rule.rs create mode 100644 crates/lance-graph-arm-discovery/src/translator.rs diff --git a/.claude/board/AGENT_LOG.md b/.claude/board/AGENT_LOG.md index 98638cdf..79012f73 100644 --- a/.claude/board/AGENT_LOG.md +++ b/.claude/board/AGENT_LOG.md @@ -1,3 +1,20 @@ +## [Main thread / Opus] Aerial+ Rust transcode (D-ARM-13) + ruff DTO/SPO/codegen synergy map + +**Branch:** claude/jolly-cori-clnf9 | **Files:** +- `crates/lance-graph-arm-discovery/` (NEW standalone crate, ~1.2K LOC + tests) — `aerial::{rng, autoencoder, extract}`, `translator`, `ndjson`, `rule`, `encode`, `lib`, `README` +- `Cargo.toml` (root) — added crate to `exclude` (standalone pattern, like bgz17/deepnsm) +- `.claude/knowledge/aerial-arm-ruff-spo-codegen-synergies.md` (NEW) — the synergy map +- `.claude/board/STATUS_BOARD.md` — D-ARM-13 + D-ARM-SYN-1/2/3 rows +- `.claude/board/EPIPHANIES.md` — predicate-vocabulary-gap finding (prepend) + +**Cargo:** `cargo test --manifest-path crates/lance-graph-arm-discovery/Cargo.toml` → **35/35 pass**; `cargo clippy … -- -D warnings` → clean. Main-thread only (no agents spawned, per session-stability rule). Big workspace NOT built (crate is excluded/zero-dep, verified independently). + +**D-ids:** D-ARM-13 (**Shipped on branch**); D-ARM-SYN-1/2/3 (**Queued**, council-gated). + +**Outcome:** DONE. Transcoded **Aerial+** (Karabulut 2025, 2504.19354v1) to zero-dep Rust — the autoencoder leg the plan §14 had explicitly deferred to Python; the user's directive ("transcode aerial rule mining to rust") supersedes that deferral. Faithful port: one-hot encoding → under-complete **denoising autoencoder** (per-feature softmax + cross-entropy, hand-written backprop, seeded SplitMix64 for reproducibility) → **Algorithm 1** reconstruction-probe rule extraction (mark antecedent, uniform elsewhere, forward, τ_a antecedent test + τ_c consequent test) → support/confidence confirmed on data → `CandidateRule`. Tests prove the AE learns a cross-feature dependency and Algorithm 1 recovers a planted rule while rejecting an independent feature. Translator `arm_to_nars` maps `(support, confidence, n) → NARS (f, c)` verbatim per paper §2/§3.3; `ndjson` emits the exact `{"s","p","o","f","c"}` line shape the SPO store loader reads. **Synergy finding:** the Aerial leg is the *runtime-data* frontend of a three-frontend/one-substrate/two-codegen bracket whose substrate (`ruff_spo_triplet::Triple`) and codegen (`ruff_python_codegen` ∥ `op_emitter.rs`) legs already exist in the ruff fork; `ruff_python_dto_check` is the *static-AST* sibling frontend. Key gap surfaced: `ruff_spo_triplet::Predicate` is a closed vocabulary with **no implication/association predicate**, so loading ARM rules through that ndjson path needs `Implies` added there first (D-ARM-SYN-1, deliberate ontology change → council-gated). Determinism boundary preserved: the nondeterministic AE stays a seeded *fan-in proposer*, out of the deterministic compile path, output gated by Stage D. PR to follow. + +--- + ## [Main thread / Opus 4.7] streaming-arm-nars-discovery-v1 — integration plan + handover + #434 corrections (the upstream proposer leg) **Branch:** claude/activate-lance-graph-att-k2pHI (rebased onto origin/main post PR #434 merge) | **Files:** diff --git a/.claude/board/EPIPHANIES.md b/.claude/board/EPIPHANIES.md index c74b93c4..a1ef6493 100644 --- a/.claude/board/EPIPHANIES.md +++ b/.claude/board/EPIPHANIES.md @@ -1,3 +1,21 @@ +## 2026-05-30 — E-DISCOVERY-CODEGEN-BRACKET-1 (realised) — the Aerial+ transcode is the runtime-data frontend of a bracket whose substrate + codegen legs ALREADY EXIST in the ruff fork; the ruff SPO predicate vocabulary is the only missing seam + +**Status:** FINDING (type-level, grounded in source read 2026-05-30) + CONJECTURE (the three D-ARM-SYN wiring deliverables). Author-stated; the three wirings are council-gated. + +The two-paper bracket (`streaming-arm-nars-discovery-v1.md`: Aerial+ discovery upstream, Abreu M2M codegen downstream, SPO+NARS middle) is not a future aspiration — **its substrate and both codegen legs are already implemented in `adaworldapi/ruff`** and `lance-graph-ontology`. Transcoding Aerial+ to Rust (D-ARM-13) made this concrete: + +1. **One substrate, three frontends.** `ruff_spo_triplet::Triple { s, p, o, f, c }` *"mirrors `lance_graph::graph::spo::odoo_ontology::OntologyTriple` field-for-field"* and its ndjson is *"exactly what `parse_triples` reads."* Three proposer frontends converge on it: `ruff_python_dto_check` (static Python AST → `Extracted`/Authoritative), `ruff_ruby_spo` (Rails scaffold), and **`lance-graph-arm-discovery` (Aerial+, runtime data → `ArmDiscovered`)**. The first two are bounded by the literal artifact; only the ARM leg surfaces co-correlations that exist solely in runtime rows. + +2. **Two codegen legs, one thesis.** `op_emitter.rs` (ratified SoA → deterministic Rust dispatch) and `ruff_python_codegen::round_trip` (AST → deterministic Python) are the same externalise-interpretation thesis the Abreu paper validates, in two target languages. Both sit downstream of the ratification firewall. + +3. **The truth scale is already shared.** Aerial's `arm_to_nars` produces the exact `(f, c)` that `TruthValue::new` and `ruff_spo_triplet::Provenance::truth()` carry, and `NarsTruth::expectation()` reimplements `TruthValue::expectation` so one `TruthGate` covers mined and extracted facts alike. + +4. **The one missing seam (the actionable finding).** `ruff_spo_triplet::Predicate` is a **closed vocabulary** (`rdf:type, has_function, emitted_by, depends_on, reads_field, raises, traverses_relation`) and `from_ndjson` **hard-rejects** anything else — and **none of them is an implication/association relation.** An `X → Y` ARM rule therefore cannot flow through that loader until `Implies`/`CoOccursWith` is added (a *deliberate* ontology change per that crate's own doc). This is D-ARM-SYN-1; it gates SYN-2 (the `CandidateRule → ModelGraph` adapter) and SYN-3 (the `ArmDiscovered` truth calibration below the codegen gate). + +**Determinism boundary (unchanged, reaffirmed):** Aerial+ is the only nondeterministic node in the bracket. The transcode keeps it standalone, seeded (`aerial::Rng`), behind the `aerial` feature, and emitting a `CandidateRule` *proposal* — never a committed triple. Promotion is the council's job. Full map: `.claude/knowledge/aerial-arm-ruff-spo-codegen-synergies.md`. Code: `crates/lance-graph-arm-discovery/`. Cross-ref: `E-INTERPRET-NOT-STORE-1`, `E-SOA-IS-THE-ONLY`, `I-NOISE-FLOOR-JIRAK`, Karabulut 2025 §2/§3.3, Abreu 2025 §4. + +--- + ## 2026-05-29 — E-SOA-IS-THE-ONLY — there is ONE SoA, never transformed; mailbox SoA mutation IS the hot path; Libet −550 ms anchors the Rubicon kanban in surrealkv-on-lance; SPO-W witness is a *pointer* via the belief-state arc **Status:** CONJECTURE / design (user-stated 2026-05-29, post-PR-#433). Records five layered rulings; details in `bindspace-singleton-to-mailbox-soa-v1.md` §11.1–§11.5. Author-stated; not council-gated. diff --git a/.claude/board/LATEST_STATE.md b/.claude/board/LATEST_STATE.md index 2666f583..a93c9740 100644 --- a/.claude/board/LATEST_STATE.md +++ b/.claude/board/LATEST_STATE.md @@ -472,3 +472,11 @@ PR sequence: #360 → #361 → post-#360 substrate-sweep (this PR). | PR | Merged | Title | What it added | |---|---|---|---| | **#427** | 2026-05-28 | feat(mailbox-soa): bindspace→mailbox migration wave A1-A4 (thoughtspace columns + transitional routing + WitnessTable + plan §10) | First implementation pulse of `bindspace-singleton-to-mailbox-soa-v1` (PR #418 plan). **A1** (`1df12eca`, +103): 4 thoughtspace columns on `MailboxSoA` (`edges`/`qualia`/`meta`/`entity_type`) + 8 row accessors + zero-init in `new()` + reset in `reset_row()`. **A2** (`61b641d5`, +42): transitional `mailboxes: HashMap` + `with_mailbox()` builder + `mailbox()` accessor on `ShaderDriver` — sibling-shape, additive, singleton untouched. **A3** (`ef848a34`, +187): new `WitnessTable` + `WitnessEntry{ mailbox_ref, spo_fact_ref }` primitive in `lance-graph-contract::witness_table` (zero-dep, 3 unit tests, `const fn new`, `get`/`set` bounds-checked). **A4** (`0f448730`, +36): plan §10 "2026-05-28 architectural refinements" appended — 7 ratified findings (SoA-Lance ≠ cascade; cascade is not an index space; 64k-256k mailbox envelope ~360 MB - 1.4 GB RAM-resident; W-slot = per-cohort witness table not corpus pointer; cascade granularities = CPU/cache boundaries 64/256/4096/16384; `simd_soa.rs` introspects per-SoA shape; SoA invariant spawn → commit, two egress modes external/internal) + 2 surviving OQs (OQ-MBX-8 `persisted_row` vs Lance native versioning; OQ-MBX-15′ container scoping granularity). Codex P1 follow-on `f541b280`: widen `WitnessEntry.mailbox_ref` u16 → u32 + correct `Option` size doc. **457 contract+driver tests passing**, zero new behavioural code outside the columns/builder/primitive. Singleton `Arc` NOT removed (sibling pattern); cutover in a downstream slice (D-MBX-3/4). Merge commit `84296118`. Author session — this governance row is the post-merge close-out; per-deliverable AGENT_LOG entries D-MBX-A1..A4 already prepended at branch HEAD pre-merge. | + +## 2026-05-30 — Append: new standalone crate `lance-graph-arm-discovery` (Aerial+ transcode, D-ARM-13) on branch `claude/jolly-cori-clnf9` + +(Per APPEND-ONLY rule: dated annotation augmenting the "Current Contract Inventory" snapshot above. Branch work, not yet merged — recorded so a new session does not re-derive the crate.) + +### Current Contract Inventory — new entry + +- **`crates/lance-graph-arm-discovery`** (NEW, **excluded** standalone zero-dep crate; build via `cargo test --manifest-path crates/lance-graph-arm-discovery/Cargo.toml`). The **Aerial+** Rust transcode (Karabulut 2025, 2504.19354v1) — the upstream runtime-data proposer leg of `streaming-arm-nars-discovery-v1`. Public surface: `encode::{FeatureSpec, Dataset}`, `rule::{Item, CandidateRule, Proposer}`, `translator::{arm_to_nars, NarsTruth, CandidateTriple, FeedProjector, DebugProjector, NARS_PERSONALITY_K}`, `ndjson::to_ndjson`, and (feature `aerial`, default-on) `aerial::{Rng, AerialAutoencoder, AerialParams, AerialProposer, extract_rules, ExtractParams}`. 35/35 tests, clippy `-D warnings` clean. Emits the `{"s","p","o","f","c"}` ndjson the SPO store loader reads; `(f,c)` == `TruthValue::new(f,c)` == `ruff_spo_triplet::Triple{f,c}`. Determinism boundary: nondeterministic AE is seeded + feature-gated + emits *proposals* only. Synergy map: `.claude/knowledge/aerial-arm-ruff-spo-codegen-synergies.md`. Status board: D-ARM-13 (Shipped on branch) + D-ARM-SYN-1/2/3 (Queued). **Not** in `lance-graph-contract` yet — `rule`/`translator` are the local seam until D-ARM-1/2 land the shared carriers. diff --git a/.claude/board/STATUS_BOARD.md b/.claude/board/STATUS_BOARD.md index aeed9336..cf389d0a 100644 --- a/.claude/board/STATUS_BOARD.md +++ b/.claude/board/STATUS_BOARD.md @@ -633,6 +633,10 @@ The missing UPSTREAM discovery leg. Today's proposers (curated L-docs + AST-extr | D-ARM-10 | `op_emitter::bucket_corpus` ratification filter (`confidence ≥ Ratified`) + 2 tests | lance-graph-ontology::op_emitter | 30 | HIGH | **Queued** | depends on D-ARM-1 | | D-ARM-11 | `style_recipe.rs` rule 8 — ArmDiscovered backing adds `DAtom::Compute` weight 2 (provisional) | lance-graph-ontology::style_recipe | 80 | MED | **Queued** | depends on D-ARM-1 | | D-ARM-12 | End-to-end pipeline test + bench (synthetic Odoo feed → all 5 stages → council micro-batch) | lance-graph-arm-discovery::tests + benches | 400 | MED | **Queued** | depends on Waves 1-6; informs OQ-ARM-2 + OQ-ARM-7 | +| D-ARM-13 | **Aerial+ Rust transcode** — denoising autoencoder (softmax/feature + CE, paper §3.2) + Algorithm 1 reconstruction-probe rule extraction (§3.3) + seeded `Rng` + `AerialProposer` impl of `Proposer`. User-directed port that plan §14 had deferred ("autoencoder stays in Python"); supersedes that deferral. | lance-graph-arm-discovery::aerial | ~1.2K | HIGH | **Shipped (branch)** | branch `claude/jolly-cori-clnf9`; standalone zero-dep crate (excluded); 35/35 tests, clippy `-D warnings` clean. Keeps the nondeterministic AE OUT of the deterministic compile path; output is a *proposal* gated by Stage D. | +| D-ARM-SYN-1 | Add `Implies`/`CoOccursWith` to `ruff_spo_triplet::Predicate` closed vocabulary (+ `Provenance` tier) so ARM rules load through the same `parse_triples` ndjson path as the static extractor | ruff/ruff_spo_triplet | 40 | MED | **Queued** | council-gated (deliberate ontology change); blocks SYN-2; see `.claude/knowledge/aerial-arm-ruff-spo-codegen-synergies.md` §1 | +| D-ARM-SYN-2 | `CandidateRule → ruff_spo_triplet::ModelGraph` adapter so the Aerial runtime-data leg joins the `ruff_python_dto_check` static-AST leg in one graph before `expand()` | lance-graph-arm-discovery + ruff_spo_triplet | 120 | MED | **Queued** | depends on SYN-1; synergy doc §2 | +| D-ARM-SYN-3 | Calibrate `ProvenanceTier::ArmDiscovered` `(f,c)` below the `op_emitter` ratification gate + below static `Inferred (0.85,0.75)` so un-ratified ARM truth is council-visible but codegen-filtered | lance-graph-contract + lance-graph-ontology::op_emitter | 30 | MED | **Queued** | depends on D-ARM-1 + SYN-1; synergy doc §3/§4 | --- diff --git a/.claude/knowledge/aerial-arm-ruff-spo-codegen-synergies.md b/.claude/knowledge/aerial-arm-ruff-spo-codegen-synergies.md new file mode 100644 index 00000000..6aba3bb2 --- /dev/null +++ b/.claude/knowledge/aerial-arm-ruff-spo-codegen-synergies.md @@ -0,0 +1,205 @@ +# Aerial+ ARM-discovery ↔ ruff DTO / SPO / codegen — synergy map + +> **READ BY:** integration-lead, truth-architect, dto-soa-savant, prior-art-savant, +> any agent touching `lance-graph-arm-discovery`, `ruff_spo_triplet`, +> `ruff_python_dto_check`, `ruff_python_codegen`, or `op_emitter.rs`. +> +> **Status:** FINDING (type-level, grounded in source read 2026-05-30) + +> CONJECTURE (the three proposed wiring deliverables, council-gated). +> +> **Anchors:** `streaming-arm-nars-discovery-v1.md` (the plan this implements +> the Aerial leg of), `E-DISCOVERY-CODEGEN-BRACKET-1` (candidate epiphany), +> `E-SOA-IS-THE-ONLY`, `I-NOISE-FLOOR-JIRAK`. Papers: Karabulut 2025 +> (Aerial+, 2504.19354v1), Abreu 2025 (ontology M2M, 2511.13661v1). + +--- + +## 0. One sentence + +The Aerial+ Rust transcode (`crates/lance-graph-arm-discovery`) is the +**runtime-data proposer** of a three-frontend / one-substrate / two-codegen +bracket whose substrate and codegen legs **already exist in the `ruff` fork** — +so Aerial's job is to emit into contracts the ruff crates already define, not +to invent new ones. + +``` + PROPOSER FRONTENDS SUBSTRATE CODEGEN LEGS + ┌───────────────────────┐ ┌────────────────────┐ + │ ruff_python_dto_check │ static AST ┐ │ op_emitter.rs │ Rust + │ (Python source → IR) │ │ ruff_spo_triplet │ (ratified SoA → │ dispatch + ├───────────────────────┤ ├──► Triple{s,p,o,f,c} ─┤ RECIPE_* + Ops) │ + │ ruff_ruby_spo (scaffold)│ static AST │ = NARS truth ├────────────────────┤ + ├───────────────────────┤ │ = SPO ndjson │ ruff_python_codegen │ Python + │ lance-graph-arm- │ RUNTIME ┘ loader contract │ (AST → source, │ source + │ discovery (Aerial+) │ DATA ◄── THIS WORK │ round_trip) │ + └───────────────────────┘ └────────────────────┘ + ▲ determinism firewall = ratification gate ▲ + (Aerial is nondeterministic → stays UPSTREAM of the gate) +``` + +This is the literal realisation of the two-paper bracket: Aerial+ supplies the +upstream discovery leg, the Abreu M2M paper validates the downstream codegen +leg, and `ruff_spo_triplet`'s `Triple{s,p,o,f,c}` is the SPO+NARS invariant +middle both converge on. + +--- + +## 1. Synergy A — `ruff_spo_triplet` IS the truth/triple contract Aerial emits into + +**FINDING.** `ruff_spo_triplet::triple::Triple` is `{ s, p, o, f: f32, c: f32 }` +and its doc says it *"mirrors `lance_graph::graph::spo::odoo_ontology::OntologyTriple` +field-for-field so the ndjson this crate writes loads into that store with no +transform."* `ruff_spo_triplet::ndjson::to_ndjson` emits one +`{"s","p","o","f","c"}` object per line — *"exactly what +`lance_graph::graph::spo::odoo_ontology::parse_triples` reads."* + +Our transcode targets this contract directly: + +| Aerial (this crate) | ruff_spo_triplet | lance_graph SPO | +| --- | --- | --- | +| `translator::CandidateTriple{s,p,o,f,c}` | `triple::Triple{s,p,o,f,c}` | `odoo_ontology::OntologyTriple` | +| `ndjson::to_ndjson` → `{"s","p","o","f","c"}\n` | `ndjson::to_ndjson` (same shape) | `parse_triples` (the loader) | +| `arm_to_nars` → `(f, c)` | `Provenance::truth()` → `(f, c)` | `TruthValue::new(f, c)` | + +The truth scales line up exactly — `NarsTruth::expectation()` reimplements +`TruthValue::expectation` (`c·(f−0.5)+0.5`) so an Aerial-mined rule and a +ruff-extracted structural triple are gated by the **same** `TruthGate`. Test +`aerial::tests::mined_rules_serialise_to_spo_ndjson` proves a mined rule round- +trips into that line shape end-to-end. + +**FINDING — the gap (the actionable bit).** `ruff_spo_triplet::Predicate` is a +**closed vocabulary**: `{rdf:type, has_function, emitted_by, depends_on, +reads_field, raises, traverses_relation}`, and `ndjson::from_ndjson` +**hard-rejects** any predicate outside it. None of these is an *association / +implication* relation. So a mined `X → Y` rule cannot today flow through the +ruff loader: its natural predicate (`implies` / `co_occurs_with`) is not in the +set. This is by design — the doc says adding a predicate is *"a deliberate +ontology change."* The Aerial `DebugProjector` emits `"implies"` precisely to +surface this seam. + +→ **Deliverable D-ARM-SYN-1 (CONJECTURE, council-gated):** add `Implies` +(and/or `CoOccursWith`) to `ruff_spo_triplet::Predicate` with a new +`Provenance` tier (see §4) so ARM-discovered rules load through the *same* +`parse_triples` path as the static extractor. Touches: one enum variant + one +`as_str`/`from_str` arm + one `default_provenance` arm + the ndjson closed- +vocab test. Low LOC, deliberate ontology decision → must pass the council +(`dto-soa-savant` + `prior-art-savant`). + +--- + +## 2. Synergy B — `ruff_python_dto_check` and Aerial are the two ends of the proposer spectrum + +`ruff_python_dto_check` harvests DTO/route/handler/model facts from **static +Python source** via `ruff_python_parser` + `ruff_python_ast` into a JSON +`Bundle`, and (with `ruff_ruby_spo`) fills the shared `ruff_spo_triplet::ModelGraph` +IR. It is the Rust-native successor to the plan's `tools/odoo-blueprint-extractor` +AST walk — the **`Extracted` / "Authoritative"** proposer leg. + +**FINDING — complementary, not redundant.** The dto_check extractor is *bounded +by what the source literally states* (it reads `@api.depends`, compute bodies, +`raise`s — see `ir.rs` cheat-sheet). It structurally **cannot** surface a +co-correlation that only exists in runtime rows — e.g. "invoices route through +Fiscal Position A when partner-country = B AND product-category = C", implicit +in years of `account.move` but absent from `account_fiscal_position.py`. That +is exactly the gap Aerial fills: the **`ArmDiscovered` runtime-data leg**. The +two are the same shape (`ModelGraph` → `Triple`), different evidence source: + +| Leg | Source | Crate | Provenance tier | Truth | +| --- | --- | --- | --- | --- | +| Curated | L-docs prose | hand | `Curated` | high f, high c | +| Extracted (static AST) | Python/Ruby source| `ruff_python_dto_check` | `Structural`/`Authoritative`/`Inferred` | tiered fixed | +| **ArmDiscovered (runtime)** | **parquet rows** | **`lance-graph-arm-discovery`** | **`ArmDiscovered`** | **data-derived `(f,c)`** | + +→ **Deliverable D-ARM-SYN-2 (CONJECTURE):** a `CandidateRule → ModelGraph` +adapter (or a parallel `arm:`-namespaced `ModelGraph`) so Aerial output joins +the dto_check output in one graph before `expand()`. The IR is "intentionally +dumb owned data" (per `ir.rs`), so the adapter is pure mapping — the antecedent +items become a synthetic subject IRI, the consequent an object IRI, the rule a +`Implies` edge. Gated on D-ARM-SYN-1. + +--- + +## 3. Synergy C — `op_emitter.rs` and `ruff_python_codegen` are the two codegen legs of one bracket + +`op_emitter.rs` (lance-graph-ontology) turns ratified typed-SoA recipes into +**deterministic Rust** (`RECIPE_*` consts + per-kind `Op` structs + static +slices, `include!`-ed at build time). `ruff_python_codegen::round_trip` +turns a Python AST back into **deterministic Python source** (`Generator` + +`Stylist`). Same thesis, two target languages: + +**FINDING.** Both are *deterministic, externalised-interpretation* codegen — +the exact doctrine the Abreu M2M paper independently validates ("externalize +mapping knowledge into ontologies + rules, not code"). Both consume an already- +ratified symbolic artifact; neither does discovery. They sit **downstream of +the determinism firewall.** + +**FINDING — the firewall is load-bearing for Aerial specifically.** Aerial+'s +autoencoder is nondeterministic in general (random init, denoising mask, float +reduction order). The transcode makes it *seedable* (`aerial::Rng`, +`reproducible_from_seed` test) so it is auditable, but it is still a **fan-in +proposer, never the trunk** (`streaming-arm-nars-discovery-v1.md` §2.1: pair- +stats is the deterministic default trunk). Therefore: + +- Aerial output is `CandidateRule` (a *proposal*), never a committed triple. +- Promotion to a triple `op_emitter` will compile is the **council's** job + (Stage D), via the `op_emitter::bucket_corpus` ratification filter + (D-ARM-10: `confidence ≥ Ratified`). An ArmDiscovered candidate that has not + passed the council never reaches either codegen leg. + +→ **Deliverable D-ARM-SYN-3 (CONJECTURE):** when D-ARM-SYN-1's `ArmDiscovered` +provenance tier lands, calibrate its `(f, c)` so that **un-ratified** ARM truth +sits *below* `op_emitter`'s ratification gate and *below* the static +`Inferred` tier `(0.85, 0.75)` — i.e. ARM rules are visible to the council but +filtered out of codegen until ratified. This makes the firewall a truth-gate, +not a separate code path. + +--- + +## 4. The truth-tier alignment (the number that has to be right) + +`ruff_spo_triplet::Provenance::truth()` fixes three tiers: +`Structural (1.0, 1.0)`, `Authoritative (0.95, 0.90)`, `Inferred (0.85, 0.75)`. +Aerial's truth is **not** a fixed tier — it is computed per rule by +`arm_to_nars`: + +- `f = ARM confidence = P(Y|X)` (clamped `[0,1]`) +- `c = m / (m + k)`, `m = support × n` (evidential mass), `k` = NAL-9 personality (default 1.0) + +So a well-evidenced, high-confidence ARM rule can *reach* Authoritative-tier +expectation, while a thin-evidence rule lands far below Inferred. That is the +desired behaviour: ARM truth is **continuous and evidence-driven**, and the +`I-NOISE-FLOOR-JIRAK` floor (D-ARM-7, separate gate) keeps the thin tail from +ever being emitted. The proposed `ArmDiscovered` tier is therefore best modelled +not as a fixed `(f,c)` pair but as "the `arm_to_nars` output, Jirak-floored, +council-gated for codegen." + +--- + +## 5. Determinism boundary — who sits where + +| Component | Det? | Side of firewall | Crate | +| --- | --- | --- | --- | +| pair-stats proposer (D-ARM-3) | yes | upstream (trunk) | lance-graph-arm-discovery (future) | +| **Aerial+ proposer (this work)** | seeded| **upstream (fan-in)** | **lance-graph-arm-discovery** | +| `ruff_python_dto_check` extractor | yes | upstream | ruff | +| translator `arm_to_nars` | yes | upstream | lance-graph-arm-discovery | +| ratification council (Stage D) | n/d→d| **THE firewall** | session A2A | +| `op_emitter` Rust codegen | yes | downstream | lance-graph-ontology | +| `ruff_python_codegen` round_trip | yes | downstream | ruff | + +The single rule: **nothing nondeterministic crosses the council.** Aerial is +the only nondeterministic node in the whole bracket, which is exactly why the +transcode keeps it standalone, seeded, and behind the `aerial` feature. + +--- + +## 6. Cross-refs + +- Code: `crates/lance-graph-arm-discovery/` (this work) — `aerial::{autoencoder, extract}`, `translator`, `ndjson`, `rule`, `encode`. +- `ruff/crates/ruff_spo_triplet/{triple.rs, ir.rs, ndjson.rs}` — the contract. +- `ruff/crates/ruff_python_dto_check/` — the static-AST sibling proposer. +- `ruff/crates/ruff_python_codegen/lib.rs` — the Python codegen sibling. +- `crates/lance-graph-ontology/src/odoo_blueprint/op_emitter.rs` — the Rust codegen leg + the ratification-filter touchpoint (D-ARM-10). +- `crates/lance-graph/src/graph/spo/truth.rs` — `TruthValue` (the truth the (f,c) feeds). +- Plan: `.claude/plans/streaming-arm-nars-discovery-v1.md` (D-ARM-9 Aerial leg; this is its transcode). +- Papers: Karabulut 2025 §2/§3.3 (truth + Algorithm 1); Abreu 2025 §4 (externalise-interpretation). diff --git a/Cargo.toml b/Cargo.toml index d0cd8a57..0a30d429 100644 --- a/Cargo.toml +++ b/Cargo.toml @@ -39,6 +39,11 @@ exclude = [ "crates/learning", "crates/jc", "crates/sigker", + # Aerial+ ARM-discovery transcode — standalone zero-dep proposer crate, + # verified via `cargo test --manifest-path crates/lance-graph-arm-discovery/Cargo.toml`. + # Kept out of the workspace so the nondeterministic autoencoder never + # enters the deterministic lance-graph compile path (determinism boundary). + "crates/lance-graph-arm-discovery", ] resolver = "2" diff --git a/crates/lance-graph-arm-discovery/Cargo.lock b/crates/lance-graph-arm-discovery/Cargo.lock new file mode 100644 index 00000000..a6274758 --- /dev/null +++ b/crates/lance-graph-arm-discovery/Cargo.lock @@ -0,0 +1,7 @@ +# This file is automatically @generated by Cargo. +# It is not intended for manual editing. +version = 4 + +[[package]] +name = "lance-graph-arm-discovery" +version = "0.1.0" diff --git a/crates/lance-graph-arm-discovery/Cargo.toml b/crates/lance-graph-arm-discovery/Cargo.toml new file mode 100644 index 00000000..ad1c15a6 --- /dev/null +++ b/crates/lance-graph-arm-discovery/Cargo.toml @@ -0,0 +1,33 @@ +# SPDX-License-Identifier: Apache-2.0 +# SPDX-FileCopyrightText: Copyright The Lance Authors + +[package] +name = "lance-graph-arm-discovery" +version = "0.1.0" +edition = "2021" +description = "Streaming association-rule discovery (Aerial+ transcode) → NARS-truth SPO candidates" +license = "Apache-2.0" + +# Standalone, zero-dependency crate — EXCLUDED from the workspace (see root +# Cargo.toml `exclude`), exactly like bgz17 / deepnsm / bgz-tensor. It is +# verified independently with: +# +# cargo test --manifest-path crates/lance-graph-arm-discovery/Cargo.toml +# +# This keeps the nondeterministic Aerial+ autoencoder OUT of the deterministic +# lance-graph compile path (the determinism-boundary doctrine of +# `streaming-arm-nars-discovery-v1.md`). When D-ARM-1/D-ARM-2 land the shared +# `lance-graph-contract` carriers, this crate adopts them by path dep; the +# local `rule`/`translator` types are the seam until then. + +[dependencies] +# none — std only (deterministic, offline-buildable, no neural framework). + +[features] +default = ["aerial"] +# The Aerial+ neurosymbolic proposer. Gated so a deployment can compile the +# carriers (`rule`, `translator`, `ndjson`) without the autoencoder. +aerial = [] + +[lib] +doctest = true diff --git a/crates/lance-graph-arm-discovery/README.md b/crates/lance-graph-arm-discovery/README.md new file mode 100644 index 00000000..cffbc101 --- /dev/null +++ b/crates/lance-graph-arm-discovery/README.md @@ -0,0 +1,75 @@ + + +# lance-graph-arm-discovery + +Rust transcode of **Aerial+** — neurosymbolic association-rule mining +(Karabulut, Groth, Degeler, *Neurosymbolic Association Rule Mining from +Tabular Data*, arXiv 2504.19354v1). The **upstream proposer leg** of +`streaming-arm-nars-discovery-v1.md`: it mines `(X → Y)` rules from tabular +runtime data and lifts each into a NARS-truth SPO candidate that the +`lance_graph` SPO store consumes through the **same ndjson contract** as the +static `ruff_spo_triplet` extractor. + +```text + rows ─► one-hot ─► denoising autoencoder ─► Algorithm 1 probe ─► CandidateRule + (softmax/feature, CE) (reconstruction) │ + ▼ + arm_to_nars: (support, confidence, n) → NARS (f, c) + ▼ + {"s","p","o","f","c"} ndjson ─► SPO store loader +``` + +## Why standalone & zero-dep + +This crate is **excluded from the workspace** (built via `--manifest-path`, +like `bgz17` / `deepnsm`) and depends on `std` only. Two reasons: + +1. **Determinism boundary.** The autoencoder is nondeterministic in general; + keeping it out of the `lance-graph` compile path enforces the plan's + firewall — Aerial+ is a *fan-in* proposer, never the deterministic trunk, + and its output is a *proposal* that the downstream ratification council + must vet before any codegen leg consumes it. +2. **Reproducibility.** Every random source draws from one seeded SplitMix64 + stream (`aerial::Rng`), so the same seed + data + hyper-parameters give + bit-identical weights and identical mined rules — auditable despite being + a neural net. + +## Layout + +| Module | Stage | Role | +| --- | --- | --- | +| `encode` | — | one-hot `FeatureSpec` + `Dataset` + support/confidence counting | +| `aerial::rng` | A | seeded SplitMix64 PRNG | +| `aerial::autoencoder` | A | under-complete denoising AE (softmax-per-feature, CE, hand backprop) | +| `aerial::extract` | A | Algorithm 1 — reconstruction-probe rule extraction | +| `aerial` (`AerialProposer`) | A | `Proposer` impl: `fit` (train) + `next_batch` (mine) | +| `translator` | B | `arm_to_nars` + `CandidateTriple{s,p,o,f,c}` + `FeedProjector` | +| `ndjson` | emit | `{"s","p","o","f","c"}` lines = the SPO-loader contract | +| `rule` | — | `Item`, `CandidateRule`, `Proposer` trait | + +## Build & test + +```bash +cargo test --manifest-path crates/lance-graph-arm-discovery/Cargo.toml +cargo clippy --manifest-path crates/lance-graph-arm-discovery/Cargo.toml --all-targets -- -D warnings +``` + +## Truth mapping (verbatim from the paper) + +- ARM **confidence** = `P(Y|X)` → NARS **frequency** `f` +- ARM **support × n** (evidential mass `m`) → NARS **confidence** `c = m/(m+k)` + +The resulting `(f, c)` is exactly the pair `TruthValue::new(f, c)` and +`ruff_spo_triplet::Triple{f, c}` carry — see +`.claude/knowledge/aerial-arm-ruff-spo-codegen-synergies.md` for the full +synergy map with the `ruff` DTO / SPO / codegen crates. + +## Status + +Implements the Aerial+ leg (D-ARM-9) of the plan. The pair-stats deterministic +trunk (D-ARM-3), the `lance-graph-contract` carriers (D-ARM-1/2), and the +hypothesis-test / queue stages (D-ARM-5/6) remain to land; the local `rule` / +`translator` types are the seam until the contract carriers exist. diff --git a/crates/lance-graph-arm-discovery/src/aerial/autoencoder.rs b/crates/lance-graph-arm-discovery/src/aerial/autoencoder.rs new file mode 100644 index 00000000..f7b6298e --- /dev/null +++ b/crates/lance-graph-arm-discovery/src/aerial/autoencoder.rs @@ -0,0 +1,348 @@ +// SPDX-License-Identifier: Apache-2.0 +// SPDX-FileCopyrightText: Copyright The Lance Authors + +//! The Aerial+ under-complete **denoising autoencoder** (paper §3.2). +//! +//! Architecture (one hidden layer, the paper's minimal form): +//! +//! ```text +//! x (D one-hot slots) ──noise──► x̃ +//! │ encoder h = tanh(We·x̃ + be) (H < D, under-complete) +//! ▼ +//! h (H) +//! │ decoder z = Wd·h + bd +//! ▼ softmax PER FEATURE BLOCK +//! p (D, each block a probability distribution over its categories) +//! ``` +//! +//! Loss is **categorical cross-entropy per block** summed over features — +//! the multi-class form of the paper's BCE-per-feature objective. The +//! softmax+CE pairing gives the clean logit gradient `dz = p − target`, +//! which keeps the hand-written backprop short and correct. +//! +//! Denoising (random input masking) is **load-bearing, not decorative**: it +//! is what forces the bottleneck to predict a feature from the *other* +//! features, which is exactly what the reconstruction probe in +//! [`crate::aerial::extract`] reads back out as an association rule. + +// Dense GEMV / backprop kernels below are clearest as explicit index loops +// over the row-major weight matrices (multiple parallel arrays indexed by the +// same `i`/`j`). Clippy's `enumerate().take().skip()` rewrite would obscure +// the linear-algebra structure, so the lint is allowed module-wide here. +#![allow(clippy::needless_range_loop)] + +use crate::aerial::rng::Rng; +use crate::encode::{Dataset, FeatureSpec}; + +/// A trained (or freshly-initialised) Aerial+ autoencoder. +#[derive(Debug, Clone)] +pub struct AerialAutoencoder { + dim: usize, + hidden: usize, + /// Per-feature `[start, end)` blocks for the softmax. + blocks: Vec<(usize, usize)>, + /// Encoder weights, row-major `[hidden][dim]`. + we: Vec, + /// Encoder bias `[hidden]`. + be: Vec, + /// Decoder weights, row-major `[dim][hidden]`. + wd: Vec, + /// Decoder bias `[dim]`. + bd: Vec, +} + +impl AerialAutoencoder { + /// Initialise an autoencoder for `spec` with `hidden` latent units. + /// Weights are Xavier-scaled draws from the seeded `rng`; biases zero. + #[must_use] + pub fn new(spec: &FeatureSpec, hidden: usize, rng: &mut Rng) -> Self { + let dim = spec.dim(); + assert!(hidden > 0, "hidden dim must be ≥ 1"); + let blocks: Vec<(usize, usize)> = + (0..spec.num_features()).map(|f| spec.block(f)).collect(); + + let enc_scale = (1.0 / dim as f32).sqrt(); + let dec_scale = (1.0 / hidden as f32).sqrt(); + let we = (0..hidden * dim).map(|_| rng.normal() * enc_scale).collect(); + let wd = (0..dim * hidden).map(|_| rng.normal() * dec_scale).collect(); + + Self { + dim, + hidden, + blocks, + we, + be: vec![0.0; hidden], + wd, + bd: vec![0.0; dim], + } + } + + /// Input dimension `D`. + #[must_use] + pub fn dim(&self) -> usize { + self.dim + } + + /// Hidden dimension `H`. + #[must_use] + pub fn hidden(&self) -> usize { + self.hidden + } + + /// Forward pass on an arbitrary input vector (one-hot row OR a probe + /// vector with uniform blocks). Returns `(hidden, probs)` where `probs` + /// is per-block softmax over the decoder logits. + #[must_use] + pub fn forward(&self, input: &[f32]) -> (Vec, Vec) { + debug_assert_eq!(input.len(), self.dim); + // Encoder: h = tanh(We·x + be) + let mut h = vec![0.0f32; self.hidden]; + for j in 0..self.hidden { + let mut acc = self.be[j]; + let base = j * self.dim; + for i in 0..self.dim { + acc += self.we[base + i] * input[i]; + } + h[j] = acc.tanh(); + } + // Decoder logits: z = Wd·h + bd + let mut z = vec![0.0f32; self.dim]; + for i in 0..self.dim { + let mut acc = self.bd[i]; + let base = i * self.hidden; + for j in 0..self.hidden { + acc += self.wd[base + j] * h[j]; + } + z[i] = acc; + } + // Per-block softmax. + let mut p = z; + for &(s, e) in &self.blocks { + softmax_in_place(&mut p[s..e]); + } + (h, p) + } + + /// Reconstruct per-block probabilities for an input (forward, probs only). + #[must_use] + pub fn reconstruct(&self, input: &[f32]) -> Vec { + self.forward(input).1 + } + + /// Mean cross-entropy loss over a dataset's clean one-hot encodings. + /// Used as a training-progress signal in tests and diagnostics. + #[must_use] + pub fn mean_loss(&self, data: &Dataset) -> f32 { + if data.is_empty() { + return 0.0; + } + let mut total = 0.0f32; + for row in &data.rows { + let x = data.spec.encode(row); + let p = self.reconstruct(&x); + total += cross_entropy(&x, &p, &self.blocks); + } + total / data.len() as f32 + } + + /// Train in place with denoising SGD. Returns the final epoch's mean loss. + /// + /// One pass per epoch over a shuffled index set; per-sample weight update. + /// Each epoch corrupts inputs afresh (mask each slot with probability + /// `noise`) while the reconstruction target stays the clean one-hot row. + pub fn train( + &mut self, + data: &Dataset, + epochs: usize, + learning_rate: f32, + noise: f32, + rng: &mut Rng, + ) -> f32 { + if data.is_empty() { + return 0.0; + } + // Pre-encode clean targets once. + let targets: Vec> = data.rows.iter().map(|r| data.spec.encode(r)).collect(); + let mut order: Vec = (0..targets.len()).collect(); + let mut last = 0.0f32; + + for _epoch in 0..epochs { + rng.shuffle(&mut order); + let mut epoch_loss = 0.0f32; + for &idx in &order { + let target = &targets[idx]; + // Corrupt: copy then mask slots to 0 with probability `noise`. + let mut input = target.clone(); + if noise > 0.0 { + for v in &mut input { + if rng.bernoulli(noise) { + *v = 0.0; + } + } + } + epoch_loss += self.train_step(&input, target, learning_rate); + } + last = epoch_loss / targets.len() as f32; + } + last + } + + /// One forward+backward+update on a single (corrupted input, clean + /// target) pair. Returns the cross-entropy loss before the update. + fn train_step(&mut self, input: &[f32], target: &[f32], lr: f32) -> f32 { + // Forward (recompute hidden so we can backprop through it). + let (h, p) = self.forward(input); + let loss = cross_entropy(target, &p, &self.blocks); + + // Output gradient: dz = p − target (softmax + CE). + let mut dz = vec![0.0f32; self.dim]; + for i in 0..self.dim { + dz[i] = p[i] - target[i]; + } + + // Backprop into hidden: dh[j] = Σ_i Wd[i][j]·dz[i] + let mut dh = vec![0.0f32; self.hidden]; + for i in 0..self.dim { + let base = i * self.hidden; + let dzi = dz[i]; + for j in 0..self.hidden { + dh[j] += self.wd[base + j] * dzi; + } + } + + // Decoder update: Wd[i][j] -= lr·dz[i]·h[j]; bd[i] -= lr·dz[i] + for i in 0..self.dim { + let base = i * self.hidden; + let dzi = dz[i]; + for j in 0..self.hidden { + self.wd[base + j] -= lr * dzi * h[j]; + } + self.bd[i] -= lr * dzi; + } + + // Through tanh: da[j] = dh[j]·(1 − h[j]²) + // Encoder update: We[j][i] -= lr·da[j]·input[i]; be[j] -= lr·da[j] + for j in 0..self.hidden { + let da = dh[j] * (1.0 - h[j] * h[j]); + let base = j * self.dim; + for i in 0..self.dim { + self.we[base + i] -= lr * da * input[i]; + } + self.be[j] -= lr * da; + } + + loss + } +} + +/// Numerically-stable softmax in place over a slice (one feature block). +fn softmax_in_place(block: &mut [f32]) { + if block.is_empty() { + return; + } + let max = block.iter().copied().fold(f32::NEG_INFINITY, f32::max); + let mut sum = 0.0f32; + for v in block.iter_mut() { + *v = (*v - max).exp(); + sum += *v; + } + if sum > 0.0 { + for v in block.iter_mut() { + *v /= sum; + } + } +} + +/// Cross-entropy `−Σ target·ln(p)` summed over all blocks. `target` is the +/// clean one-hot vector; `p` is the per-block softmax output. +fn cross_entropy(target: &[f32], p: &[f32], blocks: &[(usize, usize)]) -> f32 { + let mut loss = 0.0f32; + for &(s, e) in blocks { + for i in s..e { + if target[i] > 0.0 { + loss -= target[i] * (p[i].max(1e-9)).ln(); + } + } + } + loss +} + +#[cfg(test)] +mod tests { + use super::*; + + fn corr_dataset(n: usize, seed: u64) -> Dataset { + // feature1 == feature0 (deterministic dependency); feature2 random. + let spec = FeatureSpec::new(vec![2, 2, 2]); + let mut rng = Rng::new(seed); + let rows = (0..n) + .map(|_| { + let a = (rng.next_u64() % 2) as u32; + let c = (rng.next_u64() % 2) as u32; + vec![a, a, c] + }) + .collect(); + Dataset::new(spec, rows) + } + + #[test] + fn forward_blocks_are_probability_distributions() { + let spec = FeatureSpec::new(vec![2, 3]); + let mut rng = Rng::new(1); + let ae = AerialAutoencoder::new(&spec, 4, &mut rng); + let (_, p) = ae.forward(&spec.encode(&[0, 2])); + // each block sums to ~1 + assert!((p[0..2].iter().sum::() - 1.0).abs() < 1e-5); + assert!((p[2..5].iter().sum::() - 1.0).abs() < 1e-5); + } + + #[test] + fn training_reduces_loss() { + let data = corr_dataset(300, 7); + let mut rng = Rng::new(42); + let mut ae = AerialAutoencoder::new(&data.spec, 4, &mut rng); + let before = ae.mean_loss(&data); + ae.train(&data, 400, 0.1, 0.3, &mut rng); + let after = ae.mean_loss(&data); + assert!( + after < before * 0.7, + "loss should drop substantially: before={before}, after={after}" + ); + } + + #[test] + fn denoising_ae_learns_cross_feature_dependency() { + // After training, probing "feature0 = 0" (one-hot) with the rest of + // the input left at uniform should reconstruct feature1 = 0 with + // high probability — that IS the learned A→B dependency. + let data = corr_dataset(400, 11); + let mut rng = Rng::new(42); + let mut ae = AerialAutoencoder::new(&data.spec, 4, &mut rng); + ae.train(&data, 600, 0.1, 0.3, &mut rng); + + // Probe vector: feature0 block = one-hot(0), features 1 and 2 uniform. + let mut probe = vec![0.5f32; data.spec.dim()]; // all uniform (cards=2) + probe[0] = 1.0; // feature0 = cat0 + probe[1] = 0.0; + let p = ae.reconstruct(&probe); + // feature1 block is slots [2,4): expect cat0 (slot 2) dominant. + assert!( + p[2] > 0.6, + "feature1=0 should be reconstructed from feature0=0; p={:?}", + &p[2..4] + ); + assert!(p[2] > p[3], "the correlated category must win"); + } + + #[test] + fn training_is_reproducible_from_seed() { + let data = corr_dataset(200, 5); + let mut ae1 = AerialAutoencoder::new(&data.spec, 4, &mut Rng::new(42)); + let mut ae2 = AerialAutoencoder::new(&data.spec, 4, &mut Rng::new(42)); + let l1 = ae1.train(&data, 100, 0.1, 0.2, &mut Rng::new(123)); + let l2 = ae2.train(&data, 100, 0.1, 0.2, &mut Rng::new(123)); + assert_eq!(l1, l2, "same seeds ⇒ identical training"); + assert_eq!(ae1.we, ae2.we); + assert_eq!(ae1.wd, ae2.wd); + } +} diff --git a/crates/lance-graph-arm-discovery/src/aerial/extract.rs b/crates/lance-graph-arm-discovery/src/aerial/extract.rs new file mode 100644 index 00000000..ebf824c2 --- /dev/null +++ b/crates/lance-graph-arm-discovery/src/aerial/extract.rs @@ -0,0 +1,333 @@ +// SPDX-License-Identifier: Apache-2.0 +// SPDX-FileCopyrightText: Copyright The Lance Authors + +//! Aerial+ **Algorithm 1** — rule extraction by reconstruction probing +//! (paper §3.3). +//! +//! The idea, verbatim: to test antecedent `X`, build an input where `X`'s +//! categories are marked at probability 1 and **every other feature is left +//! uniform** ("unknown"); forward-pass the trained autoencoder; then +//! +//! 1. **antecedent test** — every marked category must still come back with +//! probability `≥ τ_a` (`ant_similarity`); a self-inconsistent antecedent +//! the network cannot hold is rejected, and +//! 2. **consequent test** — for every *other* feature, if its top category +//! `Y` reconstructs with probability `≥ τ_c` (`cons_similarity`), the rule +//! `X → Y` is proposed. +//! +//! Each proposed rule's `support`/`confidence` are then measured **on the +//! actual data** (`encode::Dataset`) and gated by the classical ARM floors. +//! This is the double gate that makes Aerial+ robust: the network *proposes* +//! a rule from its learned conditional structure; the data *confirms* it. +//! An independent feature whose marginal happens to clear `τ_c` is dropped +//! at the confidence floor because `P(Y|X) ≈ P(Y)` for independent `Y`. + +use crate::aerial::autoencoder::AerialAutoencoder; +use crate::encode::Dataset; +use crate::rule::{CandidateRule, Item}; + +/// Thresholds + bounds for [`extract_rules`]. +#[derive(Debug, Clone, Copy)] +pub struct ExtractParams { + /// `τ_a` — minimum reconstructed probability of each marked antecedent + /// category for the antecedent to be considered coherent. + pub ant_similarity: f32, + /// `τ_c` — minimum reconstructed probability of a consequent category. + pub cons_similarity: f32, + /// Maximum antecedent size `|X|` (paper default 2; pair-stats trunk caps + /// here, the Aerial fan-in can go higher). + pub max_antecedent: usize, + /// Classical minimum support floor, measured on the data. + pub min_support: f32, + /// Classical minimum confidence floor, measured on the data. + pub min_confidence: f32, +} + +impl Default for ExtractParams { + fn default() -> Self { + Self { + ant_similarity: 0.5, + cons_similarity: 0.6, + max_antecedent: 2, + min_support: 0.01, + min_confidence: 0.5, + } + } +} + +/// Run Algorithm 1 against a trained autoencoder and the data it was trained +/// on. Returns deterministically-sorted candidate rules. +#[must_use] +pub fn extract_rules( + ae: &AerialAutoencoder, + data: &Dataset, + params: &ExtractParams, +) -> Vec { + let spec = &data.spec; + let n = data.len() as u32; + if n == 0 { + return Vec::new(); + } + + // Frequent single items (support floor) — the apriori-style prune that + // bounds the antecedent search, grouped by feature. + let mut frequent_by_feature: Vec> = vec![Vec::new(); spec.num_features()]; + for (f, bucket) in frequent_by_feature.iter_mut().enumerate() { + for cat in 0..spec.cardinality(f) { + let item = Item::new(f as u32, cat); + if data.support(&[item]) >= params.min_support { + bucket.push(item); + } + } + } + let candidate_features: Vec = (0..spec.num_features()) + .filter(|&f| !frequent_by_feature[f].is_empty()) + .collect(); + + // The neutral probe: every block uniform (= "unknown"). + let neutral = neutral_probe(spec); + + let mut rules: Vec = Vec::new(); + + let max_ant = params.max_antecedent.max(1); + for size in 1..=max_ant { + for feature_combo in feature_combinations(&candidate_features, size) { + // Cartesian product of the frequent items of each chosen feature. + for antecedent in item_product(&feature_combo, &frequent_by_feature) { + probe_antecedent(ae, spec, &neutral, &antecedent, &feature_combo, data, params, &mut rules); + } + } + } + + rules.sort_by(|a, b| { + a.antecedent + .cmp(&b.antecedent) + .then_with(|| a.consequent.cmp(&b.consequent)) + }); + rules.dedup_by(|a, b| a.antecedent == b.antecedent && a.consequent == b.consequent); + rules +} + +/// Probe one antecedent and append every confirmed rule it yields. +#[allow(clippy::too_many_arguments)] +fn probe_antecedent( + ae: &AerialAutoencoder, + spec: &crate::encode::FeatureSpec, + neutral: &[f32], + antecedent: &[Item], + antecedent_features: &[usize], + data: &Dataset, + params: &ExtractParams, + out: &mut Vec, +) { + // Build the probe: neutral, then mark the antecedent blocks one-hot. + let mut probe = neutral.to_vec(); + for &it in antecedent { + let (s, e) = spec.block(it.feature as usize); + probe[s..e].fill(0.0); + probe[spec.slot(it)] = 1.0; + } + + let p = ae.reconstruct(&probe); + + // Antecedent test: every marked category must survive at ≥ τ_a. + for &it in antecedent { + if p[spec.slot(it)] < params.ant_similarity { + return; + } + } + + // Consequent test: for each feature NOT in the antecedent, take its top + // category; if it clears τ_c, propose X → (g, w) and confirm on data. + let n = data.len() as u32; + for g in 0..spec.num_features() { + if antecedent_features.contains(&g) { + continue; + } + let (s, e) = spec.block(g); + let (mut best_cat, mut best_p) = (0usize, f32::NEG_INFINITY); + for (cat, &prob) in p[s..e].iter().enumerate() { + if prob > best_p { + best_p = prob; + best_cat = cat; + } + } + if best_p < params.cons_similarity { + continue; + } + let consequent = vec![Item::new(g as u32, best_cat as u32)]; + let support = { + let mut both = antecedent.to_vec(); + both.extend_from_slice(&consequent); + data.support(&both) + }; + let confidence = data.confidence(antecedent, &consequent); + let rule = CandidateRule { + antecedent: antecedent.to_vec(), + consequent, + support, + confidence, + n, + }; + if rule.passes(params.min_support, params.min_confidence) { + out.push(rule); + } + } +} + +/// The neutral probe vector: every feature block set to its uniform +/// distribution `1/cardinality`. +fn neutral_probe(spec: &crate::encode::FeatureSpec) -> Vec { + let mut v = vec![0.0f32; spec.dim()]; + for f in 0..spec.num_features() { + let (s, e) = spec.block(f); + let u = 1.0 / (e - s) as f32; + v[s..e].fill(u); + } + v +} + +/// All size-`k` combinations of the given feature indices (order preserved, +/// ascending), produced deterministically. +fn feature_combinations(features: &[usize], k: usize) -> Vec> { + let mut out = Vec::new(); + let mut combo = Vec::with_capacity(k); + fn recurse( + features: &[usize], + start: usize, + k: usize, + combo: &mut Vec, + out: &mut Vec>, + ) { + if combo.len() == k { + out.push(combo.clone()); + return; + } + for i in start..features.len() { + combo.push(features[i]); + recurse(features, i + 1, k, combo, out); + combo.pop(); + } + } + recurse(features, 0, k, &mut combo, &mut out); + out +} + +/// Cartesian product of the frequent items of each feature in `combo`, +/// yielding one antecedent (sorted by feature) per element. +fn item_product(combo: &[usize], frequent_by_feature: &[Vec]) -> Vec> { + let mut result: Vec> = vec![Vec::new()]; + for &f in combo { + let items = &frequent_by_feature[f]; + let mut next = Vec::with_capacity(result.len() * items.len()); + for prefix in &result { + for &item in items { + let mut ext = prefix.clone(); + ext.push(item); + next.push(ext); + } + } + result = next; + } + result +} + +#[cfg(test)] +mod tests { + use super::*; + use crate::aerial::rng::Rng; + use crate::encode::FeatureSpec; + + /// Build a dataset with a planted rule (f0=0 ⇒ f1=0, f0=1 ⇒ f1=1) plus an + /// independent feature f2, with a little label noise on f1. + fn planted(n: usize, seed: u64, noise: f32) -> Dataset { + let spec = FeatureSpec::new(vec![2, 2, 2]); + let mut rng = Rng::new(seed); + let rows = (0..n) + .map(|_| { + let a = (rng.next_u64() % 2) as u32; + let b = if rng.bernoulli(noise) { 1 - a } else { a }; + let c = (rng.next_u64() % 2) as u32; + vec![a, b, c] + }) + .collect(); + Dataset::new(spec, rows) + } + + #[test] + fn feature_combinations_are_correct() { + assert_eq!( + feature_combinations(&[0, 1, 2], 2), + vec![vec![0, 1], vec![0, 2], vec![1, 2]] + ); + assert_eq!(feature_combinations(&[0, 1, 2], 1), vec![vec![0], vec![1], vec![2]]); + } + + #[test] + fn item_product_enumerates_category_assignments() { + let freq = vec![ + vec![Item::new(0, 0), Item::new(0, 1)], + vec![Item::new(1, 0)], + ]; + let prod = item_product(&[0, 1], &freq); + assert_eq!(prod.len(), 2); + assert!(prod.contains(&vec![Item::new(0, 0), Item::new(1, 0)])); + assert!(prod.contains(&vec![Item::new(0, 1), Item::new(1, 0)])); + } + + #[test] + fn neutral_probe_is_uniform_per_block() { + let spec = FeatureSpec::new(vec![2, 4]); + let v = neutral_probe(&spec); + assert!((v[0] - 0.5).abs() < 1e-6); + assert!((v[2] - 0.25).abs() < 1e-6); + assert!((v[0..2].iter().sum::() - 1.0).abs() < 1e-6); + assert!((v[2..6].iter().sum::() - 1.0).abs() < 1e-6); + } + + #[test] + fn recovers_planted_rule_and_rejects_independent_feature() { + let data = planted(500, 11, 0.05); // 5% label noise on f1 + let mut rng = Rng::new(42); + let mut ae = AerialAutoencoder::new(&data.spec, 4, &mut rng); + ae.train(&data, 600, 0.1, 0.3, &mut rng); + + let params = ExtractParams { + ant_similarity: 0.5, + cons_similarity: 0.6, + max_antecedent: 1, + min_support: 0.05, + min_confidence: 0.7, + }; + let rules = extract_rules(&ae, &data, ¶ms); + + // The planted dependency f0 → f1 must surface (both directions of the + // category mapping are valid rules). + let has_f0_implies_f1 = rules.iter().any(|r| { + r.antecedent == vec![Item::new(0, 0)] && r.consequent == vec![Item::new(1, 0)] + }); + assert!(has_f0_implies_f1, "planted f0=0 ⇒ f1=0 not recovered: {rules:#?}"); + + // No rule should have feature 2 (independent) as a consequent of f0. + let spurious = rules.iter().any(|r| { + r.antecedent == vec![Item::new(0, 0)] && r.consequent[0].feature == 2 + }); + assert!(!spurious, "independent feature 2 leaked as a consequent: {rules:#?}"); + + // Recovered rule confidence reflects the data (≈0.95 with 5% noise). + let rule = rules + .iter() + .find(|r| r.antecedent == vec![Item::new(0, 0)] && r.consequent == vec![Item::new(1, 0)]) + .unwrap(); + assert!(rule.confidence >= 0.7, "confidence {} below floor", rule.confidence); + } + + #[test] + fn empty_dataset_yields_no_rules() { + let spec = FeatureSpec::new(vec![2, 2]); + let data = Dataset::new(spec, Vec::new()); + let mut rng = Rng::new(1); + let ae = AerialAutoencoder::new(&data.spec, 3, &mut rng); + assert!(extract_rules(&ae, &data, &ExtractParams::default()).is_empty()); + } +} diff --git a/crates/lance-graph-arm-discovery/src/aerial/mod.rs b/crates/lance-graph-arm-discovery/src/aerial/mod.rs new file mode 100644 index 00000000..ed1476d4 --- /dev/null +++ b/crates/lance-graph-arm-discovery/src/aerial/mod.rs @@ -0,0 +1,214 @@ +// SPDX-License-Identifier: Apache-2.0 +// SPDX-FileCopyrightText: Copyright The Lance Authors + +//! Aerial+ — the neurosymbolic association-rule proposer (Stage A fan-in). +//! +//! Transcode of Karabulut, Groth, Degeler, *Neurosymbolic Association Rule +//! Mining from Tabular Data* (arXiv 2504.19354v1). Two pieces: +//! +//! - [`autoencoder::AerialAutoencoder`] — the under-complete denoising AE +//! (softmax-per-feature, cross-entropy), §3.2. +//! - [`extract::extract_rules`] — Algorithm 1, the reconstruction probe, §3.3. +//! +//! [`AerialProposer`] wires them into the [`crate::rule::Proposer`] contract: +//! `fit` trains the AE on a window; `next_batch` runs Algorithm 1 once and +//! returns the confirmed rules. +//! +//! # Why this is a *fan-in*, not the trunk +//! +//! Per `streaming-arm-nars-discovery-v1.md` the deterministic pair-stats +//! proposer is the default trunk; Aerial+ earns its keep only on +//! high-dimensional sparse data where pair/triple counters blow up. It is +//! nondeterministic in the general case, so it is seeded here for +//! reproducibility and its output is gated by the downstream ratification +//! council before any triple it proposes can reach the codegen path. + +pub mod autoencoder; +pub mod extract; +pub mod rng; + +pub use autoencoder::AerialAutoencoder; +pub use extract::{extract_rules, ExtractParams}; +pub use rng::Rng; + +use crate::encode::Dataset; +use crate::rule::{CandidateRule, Proposer}; + +/// Full configuration for an [`AerialProposer`]: the autoencoder +/// hyper-parameters, the extraction thresholds, and the seed that makes the +/// whole run reproducible. +#[derive(Debug, Clone, Copy)] +pub struct AerialParams { + /// Latent dimension `H` (under-complete: keep `H < D`). + pub hidden_dim: usize, + /// Training epochs. + pub epochs: usize, + /// SGD learning rate. + pub learning_rate: f32, + /// Denoising mask probability (load-bearing — see autoencoder docs). + pub noise: f32, + /// Algorithm 1 extraction thresholds + ARM floors. + pub extract: ExtractParams, + /// PRNG seed — fixes weight init, denoising mask, and epoch shuffle. + pub seed: u64, +} + +impl Default for AerialParams { + fn default() -> Self { + Self { + hidden_dim: 8, + epochs: 500, + learning_rate: 0.1, + noise: 0.3, + extract: ExtractParams::default(), + seed: 0x5EED, + } + } +} + +/// A trained Aerial+ proposer over one data window. +#[derive(Debug)] +pub struct AerialProposer { + data: Dataset, + params: AerialParams, + ae: AerialAutoencoder, + /// Set once `next_batch` has run, so a second call returns `[]` + /// (the window is exhausted until `fit` is called on a fresh window). + drained: bool, +} + +impl AerialProposer { + /// Train an autoencoder on the window and return a ready proposer. + #[must_use] + pub fn fit(data: Dataset, params: AerialParams) -> Self { + let mut rng = Rng::new(params.seed); + let mut ae = AerialAutoencoder::new(&data.spec, params.hidden_dim, &mut rng); + ae.train(&data, params.epochs, params.learning_rate, params.noise, &mut rng); + Self { + data, + params, + ae, + drained: false, + } + } + + /// Borrow the trained autoencoder (for probes / diagnostics). + #[must_use] + pub fn autoencoder(&self) -> &AerialAutoencoder { + &self.ae + } + + /// Run Algorithm 1 once, regardless of the drained flag. + #[must_use] + pub fn mine(&self) -> Vec { + extract_rules(&self.ae, &self.data, &self.params.extract) + } +} + +impl Proposer for AerialProposer { + fn next_batch(&mut self) -> Vec { + if self.drained { + return Vec::new(); + } + self.drained = true; + self.mine() + } +} + +#[cfg(test)] +mod tests { + use super::*; + use crate::encode::FeatureSpec; + use crate::ndjson::to_ndjson; + use crate::rule::Item; + use crate::translator::{CandidateTriple, DebugProjector, NARS_PERSONALITY_K}; + + fn planted(n: usize, seed: u64) -> Dataset { + // f0 → f1 deterministic; f2 independent. + let spec = FeatureSpec::new(vec![2, 2, 2]); + let mut rng = Rng::new(seed); + let rows = (0..n) + .map(|_| { + let a = (rng.next_u64() % 2) as u32; + let c = (rng.next_u64() % 2) as u32; + vec![a, a, c] + }) + .collect(); + Dataset::new(spec, rows) + } + + #[test] + fn proposer_end_to_end_yields_then_drains() { + let data = planted(400, 9); + let mut params = AerialParams { + hidden_dim: 4, + epochs: 600, + ..AerialParams::default() + }; + params.extract.max_antecedent = 1; + params.extract.min_support = 0.05; + params.extract.min_confidence = 0.7; + + let mut proposer = AerialProposer::fit(data, params); + let batch = proposer.next_batch(); + assert!(!batch.is_empty(), "should mine the planted rule"); + // Drains: a second call is empty until re-fit on a new window. + assert!(proposer.next_batch().is_empty()); + } + + #[test] + fn mined_rules_serialise_to_spo_ndjson() { + // End-to-end synergy check: mine → translate → ndjson in the exact + // {s,p,o,f,c} shape the SPO store loader reads. + let data = planted(400, 13); + let mut params = AerialParams { + hidden_dim: 4, + epochs: 600, + ..AerialParams::default() + }; + params.extract.max_antecedent = 1; + params.extract.min_support = 0.05; + params.extract.min_confidence = 0.7; + + let proposer = AerialProposer::fit(data, params); + let rules = proposer.mine(); + assert!(!rules.is_empty()); + + let projector = DebugProjector::default(); + let triples: Vec = rules + .iter() + .map(|r| CandidateTriple::from_rule(r, &projector, NARS_PERSONALITY_K)) + .collect(); + let ndjson = to_ndjson(&triples); + + // Every line is a {s,p,o,f,c} object terminated by newline. + for line in ndjson.lines() { + assert!(line.starts_with("{\"s\":\""), "line: {line}"); + assert!(line.contains("\"p\":\"implies\""), "line: {line}"); + assert!(line.contains("\"f\":"), "line: {line}"); + assert!(line.contains("\"c\":"), "line: {line}"); + } + assert!(ndjson.ends_with('\n')); + } + + #[test] + fn reproducible_from_seed() { + let p = AerialParams { + hidden_dim: 4, + epochs: 200, + seed: 777, + ..AerialParams::default() + }; + let r1 = AerialProposer::fit(planted(200, 4), p).mine(); + let r2 = AerialProposer::fit(planted(200, 4), p).mine(); + assert_eq!(r1, r2, "same seed + data ⇒ identical rules"); + } + + #[test] + fn default_params_are_under_complete_on_small_specs() { + // sanity: default hidden 8 ≥ a 6-slot toy spec, so toy tests pass a + // smaller hidden explicitly; this asserts the default is documented. + let _ = Item::new(0, 0); + assert_eq!(AerialParams::default().hidden_dim, 8); + } +} diff --git a/crates/lance-graph-arm-discovery/src/aerial/rng.rs b/crates/lance-graph-arm-discovery/src/aerial/rng.rs new file mode 100644 index 00000000..4dd68d17 --- /dev/null +++ b/crates/lance-graph-arm-discovery/src/aerial/rng.rs @@ -0,0 +1,125 @@ +// SPDX-License-Identifier: Apache-2.0 +// SPDX-FileCopyrightText: Copyright The Lance Authors + +//! A tiny deterministic PRNG (SplitMix64) — zero-dep, seedable. +//! +//! The whole reason Aerial+ can be a *reproducible* fan-in proposer despite +//! being a neural net is that every source of randomness (weight init, +//! denoising mask, epoch shuffle) draws from one seeded stream. Same seed ⇒ +//! identical weights ⇒ identical mined rules. + +/// SplitMix64 — fast, well-distributed, fully deterministic from a `u64` seed. +#[derive(Debug, Clone)] +pub struct Rng { + state: u64, +} + +impl Rng { + /// Seed the generator. Any `u64` is a valid seed. + #[must_use] + pub fn new(seed: u64) -> Self { + Self { state: seed } + } + + /// Next raw `u64`. + pub fn next_u64(&mut self) -> u64 { + // SplitMix64 (Steele, Lea, Flood 2014). + self.state = self.state.wrapping_add(0x9E37_79B9_7F4A_7C15); + let mut z = self.state; + z = (z ^ (z >> 30)).wrapping_mul(0xBF58_476D_1CE4_E5B9); + z = (z ^ (z >> 27)).wrapping_mul(0x94D0_49BB_1331_11EB); + z ^ (z >> 31) + } + + /// Uniform `f32` in `[0, 1)`. + pub fn next_f32(&mut self) -> f32 { + // Top 24 bits → mantissa precision of f32. + let bits = (self.next_u64() >> 40) as u32; // 24 bits + bits as f32 / (1u32 << 24) as f32 + } + + /// Uniform `f32` in `[-range, range)`. + pub fn uniform(&mut self, range: f32) -> f32 { + (self.next_f32() * 2.0 - 1.0) * range + } + + /// Standard-normal `f32` via Box–Muller. + pub fn normal(&mut self) -> f32 { + // Guard u1 away from 0 so ln is finite. + let u1 = (self.next_f32()).max(1e-7); + let u2 = self.next_f32(); + let r = (-2.0 * u1.ln()).sqrt(); + r * (std::f32::consts::TAU * u2).cos() + } + + /// Fisher–Yates shuffle of an index slice in place. + pub fn shuffle(&mut self, idx: &mut [usize]) { + let len = idx.len(); + for i in (1..len).rev() { + let j = (self.next_u64() % (i as u64 + 1)) as usize; + idx.swap(i, j); + } + } + + /// Bernoulli draw: `true` with probability `p`. + pub fn bernoulli(&mut self, p: f32) -> bool { + self.next_f32() < p + } +} + +#[cfg(test)] +mod tests { + use super::*; + + #[test] + fn deterministic_from_seed() { + let mut a = Rng::new(42); + let mut b = Rng::new(42); + for _ in 0..100 { + assert_eq!(a.next_u64(), b.next_u64()); + } + } + + #[test] + fn different_seeds_diverge() { + let mut a = Rng::new(1); + let mut b = Rng::new(2); + assert_ne!(a.next_u64(), b.next_u64()); + } + + #[test] + fn uniform_in_range() { + let mut r = Rng::new(7); + for _ in 0..1000 { + let x = r.next_f32(); + assert!((0.0..1.0).contains(&x)); + } + } + + #[test] + fn shuffle_is_a_permutation() { + let mut r = Rng::new(99); + let mut idx: Vec = (0..50).collect(); + r.shuffle(&mut idx); + let mut sorted = idx.clone(); + sorted.sort_unstable(); + assert_eq!(sorted, (0..50).collect::>()); + } + + #[test] + fn normal_has_reasonable_spread() { + let mut r = Rng::new(3); + let n = 5000; + let mut sum = 0.0f64; + let mut sumsq = 0.0f64; + for _ in 0..n { + let x = r.normal() as f64; + sum += x; + sumsq += x * x; + } + let mean = sum / n as f64; + let var = sumsq / n as f64 - mean * mean; + assert!(mean.abs() < 0.1, "mean ~0, got {mean}"); + assert!((var - 1.0).abs() < 0.2, "var ~1, got {var}"); + } +} diff --git a/crates/lance-graph-arm-discovery/src/encode.rs b/crates/lance-graph-arm-discovery/src/encode.rs new file mode 100644 index 00000000..cb785f10 --- /dev/null +++ b/crates/lance-graph-arm-discovery/src/encode.rs @@ -0,0 +1,246 @@ +// SPDX-License-Identifier: Apache-2.0 +// SPDX-FileCopyrightText: Copyright The Lance Authors + +//! One-hot categorical encoding — the input layer of the Aerial+ transcode. +//! +//! Aerial+ (paper §3.1) one-hot encodes each row into a concatenation of +//! per-feature blocks: feature `i` with `k_i` categories occupies a block of +//! `k_i` slots, exactly one of which is `1.0` (the observed category). The +//! full input vector has dimension `D = Σ k_i`. +//! +//! Numerical columns must be discretised into bins *before* they reach this +//! layer (the paper bins numerics; [`FeatureSpec::bin`] is the helper). + +use crate::rule::Item; + +/// The schema for a one-hot encoding: the ordered list of features and how +/// many categories each has. +/// +/// Block offsets are derived once and cached so encode/decode is a pointer +/// add, not a scan. +#[derive(Debug, Clone, PartialEq, Eq)] +pub struct FeatureSpec { + /// Number of categories per feature, in feature order. + cardinalities: Vec, + /// Prefix-sum offsets: `offsets[i]` is the first input slot of feature `i`. + /// Length is `cardinalities.len() + 1`; the last entry is the total dim. + offsets: Vec, +} + +impl FeatureSpec { + /// Build a spec from per-feature cardinalities. + /// + /// # Panics + /// If any cardinality is zero (a feature with no categories cannot be + /// one-hot encoded). + #[must_use] + pub fn new(cardinalities: Vec) -> Self { + assert!( + cardinalities.iter().all(|&k| k > 0), + "every feature needs ≥1 category" + ); + let mut offsets = Vec::with_capacity(cardinalities.len() + 1); + let mut acc = 0usize; + offsets.push(0); + for &k in &cardinalities { + acc += k as usize; + offsets.push(acc); + } + Self { + cardinalities, + offsets, + } + } + + /// Total input dimension `D = Σ k_i`. + #[must_use] + pub fn dim(&self) -> usize { + *self.offsets.last().expect("offsets always has ≥1 entry") + } + + /// Number of features (columns). + #[must_use] + pub fn num_features(&self) -> usize { + self.cardinalities.len() + } + + /// Category count for a feature. + #[must_use] + pub fn cardinality(&self, feature: usize) -> u32 { + self.cardinalities[feature] + } + + /// The `[start, end)` input-slot range owned by a feature's block. + #[must_use] + pub fn block(&self, feature: usize) -> (usize, usize) { + (self.offsets[feature], self.offsets[feature + 1]) + } + + /// The absolute input slot for an `(feature, category)` item. + #[must_use] + pub fn slot(&self, item: Item) -> usize { + self.offsets[item.feature as usize] + item.category as usize + } + + /// One-hot encode a row given as one category index per feature. + /// + /// `row[i]` is the observed category of feature `i`. Returns a `D`-length + /// vector with one `1.0` per block. + #[must_use] + pub fn encode(&self, row: &[u32]) -> Vec { + assert_eq!(row.len(), self.num_features(), "row arity mismatch"); + let mut v = vec![0.0f32; self.dim()]; + for (feature, &cat) in row.iter().enumerate() { + debug_assert!(cat < self.cardinalities[feature], "category out of range"); + v[self.offsets[feature] + cat as usize] = 1.0; + } + v + } + + /// Discretise a numeric value into `[0, bins)` over `[lo, hi]` (uniform + /// width). Values at or below `lo` land in bin 0; at or above `hi` in the + /// last bin. This is the pre-encoding step for numerical columns. + #[must_use] + pub fn bin(value: f32, lo: f32, hi: f32, bins: u32) -> u32 { + debug_assert!(bins > 0 && hi > lo, "bad binning parameters"); + let t = ((value - lo) / (hi - lo)).clamp(0.0, 1.0); + let idx = (t * bins as f32) as u32; + idx.min(bins - 1) + } +} + +/// A window of rows in category-index form, sharing one [`FeatureSpec`]. +/// +/// This is the unit Aerial+ trains on and the unit support/confidence are +/// counted over. `rows.len()` is the `n` that the NARS confidence mapping +/// uses as evidential mass. +#[derive(Debug, Clone)] +pub struct Dataset { + /// The shared schema. + pub spec: FeatureSpec, + /// One `Vec` per row; each is one category index per feature. + pub rows: Vec>, +} + +impl Dataset { + /// Wrap rows in a dataset, validating arity against the spec. + /// + /// # Panics + /// If any row's length differs from the spec's feature count. + #[must_use] + pub fn new(spec: FeatureSpec, rows: Vec>) -> Self { + for r in &rows { + assert_eq!(r.len(), spec.num_features(), "row arity mismatch"); + } + Self { spec, rows } + } + + /// Window size `n`. + #[must_use] + pub fn len(&self) -> usize { + self.rows.len() + } + + /// Whether the window is empty. + #[must_use] + pub fn is_empty(&self) -> bool { + self.rows.is_empty() + } + + /// Count of rows containing every item in `items` (an AND over items). + #[must_use] + pub fn count_matching(&self, items: &[Item]) -> u32 { + self.rows + .iter() + .filter(|row| { + items + .iter() + .all(|it| row[it.feature as usize] == it.category) + }) + .count() as u32 + } + + /// Classical support of an itemset: `count_matching / n`. + #[must_use] + pub fn support(&self, items: &[Item]) -> f32 { + if self.rows.is_empty() { + return 0.0; + } + self.count_matching(items) as f32 / self.rows.len() as f32 + } + + /// Classical confidence of `antecedent → consequent`: + /// `count(antecedent ∪ consequent) / count(antecedent)`. + /// Returns `0.0` when the antecedent never occurs. + #[must_use] + pub fn confidence(&self, antecedent: &[Item], consequent: &[Item]) -> f32 { + let ant = self.count_matching(antecedent); + if ant == 0 { + return 0.0; + } + let mut both: Vec = antecedent.to_vec(); + both.extend_from_slice(consequent); + self.count_matching(&both) as f32 / ant as f32 + } +} + +#[cfg(test)] +mod tests { + use super::*; + + #[test] + fn offsets_and_dim() { + let spec = FeatureSpec::new(vec![2, 3, 1]); + assert_eq!(spec.dim(), 6); + assert_eq!(spec.num_features(), 3); + assert_eq!(spec.block(0), (0, 2)); + assert_eq!(spec.block(1), (2, 5)); + assert_eq!(spec.block(2), (5, 6)); + } + + #[test] + fn one_hot_sets_one_per_block() { + let spec = FeatureSpec::new(vec![2, 3]); + let v = spec.encode(&[1, 2]); + assert_eq!(v, vec![0.0, 1.0, 0.0, 0.0, 1.0]); + // exactly one 1.0 per block + assert_eq!(v[0..2].iter().sum::(), 1.0); + assert_eq!(v[2..5].iter().sum::(), 1.0); + } + + #[test] + fn slot_addressing() { + let spec = FeatureSpec::new(vec![2, 3]); + assert_eq!(spec.slot(Item::new(0, 1)), 1); + assert_eq!(spec.slot(Item::new(1, 2)), 4); + } + + #[test] + fn binning_clamps_edges() { + assert_eq!(FeatureSpec::bin(-5.0, 0.0, 10.0, 5), 0); + assert_eq!(FeatureSpec::bin(0.0, 0.0, 10.0, 5), 0); + assert_eq!(FeatureSpec::bin(9.99, 0.0, 10.0, 5), 4); + assert_eq!(FeatureSpec::bin(100.0, 0.0, 10.0, 5), 4); + assert_eq!(FeatureSpec::bin(5.0, 0.0, 10.0, 5), 2); + } + + #[test] + fn support_and_confidence_counts() { + let spec = FeatureSpec::new(vec![2, 2]); + // rows: (0,0),(0,0),(0,1),(1,1) + let data = Dataset::new( + spec, + vec![vec![0, 0], vec![0, 0], vec![0, 1], vec![1, 1]], + ); + // support of feature0=0 : 3/4 + assert!((data.support(&[Item::new(0, 0)]) - 0.75).abs() < 1e-6); + // confidence (f0=0 -> f1=0): count(0,0)=2 / count(f0=0)=3 + let c = data.confidence(&[Item::new(0, 0)], &[Item::new(1, 0)]); + assert!((c - 2.0 / 3.0).abs() < 1e-6); + // confidence on never-seen antecedent is 0 + assert_eq!( + data.confidence(&[Item::new(0, 5)], &[Item::new(1, 0)]), + 0.0 + ); + } +} diff --git a/crates/lance-graph-arm-discovery/src/lib.rs b/crates/lance-graph-arm-discovery/src/lib.rs new file mode 100644 index 00000000..a154589c --- /dev/null +++ b/crates/lance-graph-arm-discovery/src/lib.rs @@ -0,0 +1,71 @@ +// SPDX-License-Identifier: Apache-2.0 +// SPDX-FileCopyrightText: Copyright The Lance Authors + +//! `lance-graph-arm-discovery` — the **upstream proposer** leg of the +//! streaming association-rule discovery → NARS revision → ratified SPO → +//! deterministic codegen pipeline (`streaming-arm-nars-discovery-v1.md`). +//! +//! This crate is the Rust transcoding of **Aerial+** (Karabulut, Groth, +//! Degeler — *Neurosymbolic Association Rule Mining from Tabular Data*, +//! arXiv 2504.19354v1, Apr 2025), the neurosymbolic association-rule miner. +//! It mines `(X → Y)` rules from tabular runtime data and lifts each rule +//! into a NARS-truth-carrying SPO candidate that the existing +//! `lance_graph::graph::spo` store consumes through the **same ndjson +//! contract** as the static `ruff_spo_triplet` extractor. +//! +//! # Pipeline position +//! +//! ```text +//! tabular rows ─┐ +//! ▼ Stage A — Aerial+ proposer (THIS CRATE, `aerial`) +//! one-hot encode → denoising autoencoder (softmax-per-feature, BCE) +//! → Algorithm 1 reconstruction probe → CandidateRule +//! ▼ Stage B — translator (THIS CRATE, `translator`) +//! arm_to_nars: (support, confidence, n) → NARS (frequency, confidence) +//! ▼ emit (THIS CRATE, `ndjson`) +//! {"s","p","o","f","c"} ndjson ─► lance_graph SPO store loader +//! ▼ Stage C/D/E — hypothesis test → council → op_emitter +//! (downstream, other crates) +//! ``` +//! +//! # The determinism boundary (read before extending) +//! +//! Aerial+'s autoencoder is **nondeterministic** in the general case (random +//! init, denoising noise, float reduction order). Per the plan's determinism +//! firewall it is a **fan-in proposer**, NEVER the deterministic trunk and +//! NEVER allowed to cross the ratification gate (Stage D) un-vetted. Two +//! consequences are baked into this crate: +//! +//! 1. The autoencoder is **seedable** ([`aerial::Rng`]). Same seed, same data, +//! and same hyper-parameters give bit-identical weights and identical +//! rules. This makes the proposer reproducible for tests and audits even +//! though it is not the canonical deterministic path. +//! 2. The output is a plain [`rule::CandidateRule`] — a *proposal*, not a +//! committed triple. Promotion to the SPO store is the downstream +//! hypothesis-test + council's job, not this crate's. +//! +//! # Truth mapping (verbatim from the paper, §2 / §3.3) +//! +//! - ARM **confidence** = P(Y|X) → NARS **frequency** `f` +//! - ARM **support × n** (evidential mass) → NARS **confidence** `c = m/(m+k)` +//! +//! See [`translator::arm_to_nars`]. The resulting `(f, c)` is exactly the pair +//! `lance_graph::graph::spo::TruthValue::new(f, c)` and +//! `ruff_spo_triplet::Triple { f, c }` carry. + +#![forbid(unsafe_code)] + +pub mod encode; +pub mod ndjson; +pub mod rule; +pub mod translator; + +#[cfg(feature = "aerial")] +pub mod aerial; + +pub use encode::{Dataset, FeatureSpec}; +pub use rule::{CandidateRule, Item, Proposer}; +pub use translator::{arm_to_nars, CandidateTriple, FeedProjector, NarsTruth}; + +#[cfg(feature = "aerial")] +pub use aerial::{AerialAutoencoder, AerialParams, AerialProposer, Rng}; diff --git a/crates/lance-graph-arm-discovery/src/ndjson.rs b/crates/lance-graph-arm-discovery/src/ndjson.rs new file mode 100644 index 00000000..4026b6aa --- /dev/null +++ b/crates/lance-graph-arm-discovery/src/ndjson.rs @@ -0,0 +1,140 @@ +// SPDX-License-Identifier: Apache-2.0 +// SPDX-FileCopyrightText: Copyright The Lance Authors + +//! Newline-delimited JSON emit for [`CandidateTriple`]. +//! +//! The on-the-wire shape is **byte-compatible** with `ruff_spo_triplet::ndjson` +//! and with what `lance_graph::graph::spo::odoo_ontology::parse_triples` +//! reads: one `{"s","p","o","f","c"}` object per line, field order +//! `s, p, o, f, c`, trailing newline. Emitting through this module is what +//! lets an ARM-discovered rule load into the SPO store through the *same* +//! loader as the static `ruff` extractor — no transform, no second format. +//! +//! This is a zero-dependency hand emitter (no `serde_json`) so the crate +//! stays std-only and offline-buildable. It produces the same logical shape; +//! the downstream loader parses it with `serde_json::from_str`. + +use crate::translator::CandidateTriple; + +/// Serialise candidate triples to ndjson (one object per line, trailing +/// newline). Order is preserved as given. +#[must_use] +pub fn to_ndjson(triples: &[CandidateTriple]) -> String { + let mut out = String::new(); + for t in triples { + out.push('{'); + push_str_field(&mut out, "s", &t.s, true); + push_str_field(&mut out, "p", &t.p, false); + push_str_field(&mut out, "o", &t.o, false); + push_num_field(&mut out, "f", t.f, false); + push_num_field(&mut out, "c", t.c, false); + out.push('}'); + out.push('\n'); + } + out +} + +fn push_str_field(out: &mut String, key: &str, value: &str, first: bool) { + if !first { + out.push(','); + } + out.push('"'); + out.push_str(key); + out.push_str("\":\""); + escape_into(out, value); + out.push('"'); +} + +fn push_num_field(out: &mut String, key: &str, value: f32, first: bool) { + if !first { + out.push(','); + } + out.push('"'); + out.push_str(key); + out.push_str("\":"); + out.push_str(&fmt_f32(value)); +} + +/// Format an `f32` as a JSON-valid number, always with a fractional part so +/// the shape mirrors `serde_json`'s float rendering (`1` → `1.0`). +/// Non-finite values are coerced to `0.0` (defensive; truths are clamped to +/// `[0, 1]` upstream, so this never fires in practice). +fn fmt_f32(value: f32) -> String { + if !value.is_finite() { + return "0.0".to_string(); + } + let s = format!("{value}"); + if s.contains('.') || s.contains('e') || s.contains('E') { + s + } else { + format!("{s}.0") + } +} + +/// Minimal JSON string escaping for the characters that can appear in IRIs. +fn escape_into(out: &mut String, value: &str) { + for ch in value.chars() { + match ch { + '"' => out.push_str("\\\""), + '\\' => out.push_str("\\\\"), + '\n' => out.push_str("\\n"), + '\r' => out.push_str("\\r"), + '\t' => out.push_str("\\t"), + c if (c as u32) < 0x20 => out.push_str(&format!("\\u{:04x}", c as u32)), + c => out.push(c), + } + } +} + +#[cfg(test)] +mod tests { + use super::*; + + fn triple(s: &str, p: &str, o: &str, f: f32, c: f32) -> CandidateTriple { + CandidateTriple { + s: s.to_string(), + p: p.to_string(), + o: o.to_string(), + f, + c, + } + } + + #[test] + fn line_shape_matches_spo_loader_contract() { + let t = triple("arm:feat0=cat1", "implies", "arm:feat1=cat0", 0.9, 0.95); + let text = to_ndjson(&[t]); + // exact field order s, p, o, f, c — the shape parse_triples reads + assert_eq!( + text, + "{\"s\":\"arm:feat0=cat1\",\"p\":\"implies\",\"o\":\"arm:feat1=cat0\",\"f\":0.9,\"c\":0.95}\n" + ); + } + + #[test] + fn integral_truth_renders_with_fraction() { + // 1.0 must render as "1.0", not "1" — mirrors serde_json float output. + let t = triple("a", "implies", "b", 1.0, 1.0); + let text = to_ndjson(&[t]); + assert!(text.contains("\"f\":1.0"), "got: {text}"); + assert!(text.contains("\"c\":1.0"), "got: {text}"); + } + + #[test] + fn each_triple_is_one_line_with_trailing_newline() { + let text = to_ndjson(&[ + triple("a", "implies", "b", 0.5, 0.5), + triple("c", "implies", "d", 0.6, 0.6), + ]); + assert_eq!(text.lines().count(), 2); + assert!(text.ends_with('\n')); + } + + #[test] + fn quotes_and_backslashes_are_escaped() { + let t = triple("a\"x", "implies", "b\\y", 0.5, 0.5); + let text = to_ndjson(&[t]); + assert!(text.contains("a\\\"x"), "quote escaped: {text}"); + assert!(text.contains("b\\\\y"), "backslash escaped: {text}"); + } +} diff --git a/crates/lance-graph-arm-discovery/src/rule.rs b/crates/lance-graph-arm-discovery/src/rule.rs new file mode 100644 index 00000000..4d5ca367 --- /dev/null +++ b/crates/lance-graph-arm-discovery/src/rule.rs @@ -0,0 +1,130 @@ +// SPDX-License-Identifier: Apache-2.0 +// SPDX-FileCopyrightText: Copyright The Lance Authors + +//! The proposer output carrier — `CandidateRule` and the `Proposer` trait. +//! +//! This is the shape every Stage-A proposer (Aerial+, the pair-stats trunk, +//! the Python IPC fan-in) emits. It is the local mirror of the planned +//! `lance-graph-contract::CandidateRule` (D-ARM-2); when that contract type +//! lands, this re-exports it and the field shape stays identical. + +/// One `(feature, category)` atom — the unit an antecedent / consequent is +/// built from. +/// +/// `feature` indexes a column in the [`crate::FeatureSpec`]; `category` +/// indexes a value within that feature's category list. Both are small +/// integers so a rule is cheap to compare, hash, and serialise. +#[derive(Debug, Clone, Copy, PartialEq, Eq, PartialOrd, Ord, Hash)] +pub struct Item { + /// Feature (column) index into the [`crate::FeatureSpec`]. + pub feature: u32, + /// Category (value) index within the feature. + pub category: u32, +} + +impl Item { + /// Construct an item. + #[must_use] + pub const fn new(feature: u32, category: u32) -> Self { + Self { feature, category } + } +} + +/// A mined association rule `antecedent → consequent` with ARM statistics. +/// +/// Field-for-field the planned `lance-graph-contract::CandidateRule` +/// (D-ARM-2). `support` and `confidence` are the classical ARM quantities +/// (paper §2): +/// +/// - `support` = |{rows ⊇ antecedent ∪ consequent}| / n +/// - `confidence` = |{rows ⊇ antecedent ∪ consequent}| / |{rows ⊇ antecedent}| +/// +/// `n` is the window size the statistics were measured over — it is what +/// turns `support` into an evidential count for the NARS confidence mapping +/// in [`crate::translator::arm_to_nars`]. +#[derive(Debug, Clone, PartialEq)] +pub struct CandidateRule { + /// Left-hand side items (all must hold). Sorted, at most one per feature. + pub antecedent: Vec, + /// Right-hand side items. Aerial+ emits exactly one (single-consequent + /// rules); the `Vec` keeps shape-parity with the planned contract. + pub consequent: Vec, + /// `support ∈ [0, 1]` — co-occurrence fraction over the window. + pub support: f32, + /// `confidence ∈ [0, 1]` — `P(consequent | antecedent)`. + pub confidence: f32, + /// Window size the statistics were measured over. + pub n: u32, +} + +impl CandidateRule { + /// Evidential mass: `support × n`, the count of rows supporting the rule. + /// Drives the NARS confidence term `c = m / (m + k)`. + #[must_use] + pub fn evidence(&self) -> f32 { + self.support * self.n as f32 + } + + /// A rule survives the classical ARM gate iff it clears both the minimum + /// support and minimum confidence floors. The Jirak-bound significance + /// floor (`I-NOISE-FLOOR-JIRAK`) is a *separate*, stricter gate applied + /// upstream of this one — see the module-level docs of the proposer. + #[must_use] + pub fn passes(&self, min_support: f32, min_confidence: f32) -> bool { + self.support >= min_support && self.confidence >= min_confidence + } +} + +/// A Stage-A proposer: something that emits a batch of candidate rules. +/// +/// Mirrors the planned `lance-graph-contract::Proposer` (D-ARM-2) so the +/// discovery crate is dependency-injectable: the hypothesis-test stage takes +/// `&mut dyn Proposer` and does not care whether the rules came from the +/// deterministic pair-stats trunk or the Aerial+ fan-in. +pub trait Proposer { + /// Produce the next batch of candidate rules. Returning an empty `Vec` + /// signals the proposer is exhausted for now (e.g. the window closed and + /// no more rules cleared the gate). + fn next_batch(&mut self) -> Vec; +} + +#[cfg(test)] +mod tests { + use super::*; + + #[test] + fn evidence_is_support_times_n() { + let r = CandidateRule { + antecedent: vec![Item::new(0, 1)], + consequent: vec![Item::new(1, 0)], + support: 0.25, + confidence: 0.9, + n: 1000, + }; + assert!((r.evidence() - 250.0).abs() < 1e-3); + } + + #[test] + fn passes_gates_on_both_floors() { + let r = CandidateRule { + antecedent: vec![Item::new(0, 0)], + consequent: vec![Item::new(1, 1)], + support: 0.05, + confidence: 0.8, + n: 200, + }; + assert!(r.passes(0.01, 0.5)); + assert!(!r.passes(0.10, 0.5), "support floor not met"); + assert!(!r.passes(0.01, 0.9), "confidence floor not met"); + } + + #[test] + fn item_ordering_is_feature_then_category() { + let mut items = vec![Item::new(1, 0), Item::new(0, 2), Item::new(0, 1)]; + items.sort(); + assert_eq!( + items, + vec![Item::new(0, 1), Item::new(0, 2), Item::new(1, 0)] + ); + } +} diff --git a/crates/lance-graph-arm-discovery/src/translator.rs b/crates/lance-graph-arm-discovery/src/translator.rs new file mode 100644 index 00000000..b2b6bbba --- /dev/null +++ b/crates/lance-graph-arm-discovery/src/translator.rs @@ -0,0 +1,219 @@ +// SPDX-License-Identifier: Apache-2.0 +// SPDX-FileCopyrightText: Copyright The Lance Authors + +//! Stage B — translate an ARM [`CandidateRule`] into NARS truth + an SPO +//! triple carrier. +//! +//! The mapping is verbatim from Aerial+ §2 / §3.3 and the plan's FINDING: +//! +//! - ARM **confidence** = `P(Y|X)` → NARS **frequency** `f` +//! - ARM **support × n** (evidential mass `m`) → NARS **confidence** +//! `c = m / (m + k)`, NAL-9 personality constant `k` (default `1.0`) +//! +//! The resulting `(f, c)` is the exact pair consumed by +//! `lance_graph::graph::spo::TruthValue::new(f, c)` and carried by +//! `ruff_spo_triplet::Triple { f, c }`. That is the whole point: a rule mined +//! from runtime data and a triple extracted from static source land in the +//! SPO store on the *same* truth scale. + +use crate::rule::{CandidateRule, Item}; + +/// NAL-9 default personality constant for the support → confidence mapping. +/// Larger `k` ⇒ more evidence needed before confidence approaches 1. +pub const NARS_PERSONALITY_K: f32 = 1.0; + +/// A NARS truth value `(frequency, confidence)` — the translator's output. +/// +/// Deliberately *not* a re-implementation of the SPO store's `TruthValue`: +/// it is the `(f, c)` pair you feed to `TruthValue::new(f, c)`. Keeping it a +/// distinct, tiny carrier honours `E-SOA-IS-THE-ONLY` (no parallel truth +/// store) while keeping this crate zero-dep. +#[derive(Debug, Clone, Copy, PartialEq)] +pub struct NarsTruth { + /// NARS frequency `f ∈ [0, 1]`. + pub frequency: f32, + /// NARS confidence `c ∈ [0, 1)`. + pub confidence: f32, +} + +impl NarsTruth { + /// NARS expectation `e = c·(f − 0.5) + 0.5` — matches + /// `lance_graph::graph::spo::TruthValue::expectation` exactly. + #[must_use] + pub fn expectation(&self) -> f32 { + self.confidence * (self.frequency - 0.5) + 0.5 + } +} + +/// Translate a candidate rule's ARM statistics into NARS truth. +/// +/// `k` is the NARS personality constant ([`NARS_PERSONALITY_K`] by default, +/// per-feed configurable per OQ-ARM-3). +#[must_use] +pub fn arm_to_nars(rule: &CandidateRule, k: f32) -> NarsTruth { + // ARM confidence is P(Y|X) — directly the NARS frequency. + let frequency = rule.confidence.clamp(0.0, 1.0); + // Evidential mass m = support × n; c = m / (m + k) → 1 as evidence grows. + let m = rule.evidence().max(0.0); + let confidence = m / (m + k); + NarsTruth { + frequency, + confidence, + } +} + +/// Projects rule items into SPO IRIs for a particular feed/domain. +/// +/// An Odoo feed projects `(model, predicate, value)`; an MedCare feed +/// projects differently. The projector is the only domain-specific seam in +/// Stage B — everything else is feed-agnostic. +pub trait FeedProjector { + /// IRI for the subject built from the antecedent items. + fn subject(&self, antecedent: &[Item]) -> String; + /// The predicate IRI for a discovered implication. + fn predicate(&self) -> String; + /// IRI for the object built from the consequent items. + fn object(&self, consequent: &[Item]) -> String; +} + +/// One SPO triple with NARS truth — byte-compatible with +/// `ruff_spo_triplet::Triple` and the SPO store's ndjson loader. +/// +/// `(s, p, o)` is the identity; `(f, c)` is the data-derived truth. The +/// `origin` byte records that this triple came from the ARM-discovery +/// proposer (bits per `discovery_origin` in the plan §7) — it is metadata, +/// dropped on the ndjson wire, not part of the triple identity. +#[derive(Debug, Clone, PartialEq)] +pub struct CandidateTriple { + /// Subject IRI. + pub s: String, + /// Predicate IRI. + pub p: String, + /// Object IRI. + pub o: String, + /// NARS frequency. + pub f: f32, + /// NARS confidence. + pub c: f32, +} + +impl CandidateTriple { + /// Build a triple from a candidate rule, a projector, and a NARS `k`. + #[must_use] + pub fn from_rule( + rule: &CandidateRule, + projector: &dyn FeedProjector, + k: f32, + ) -> Self { + let truth = arm_to_nars(rule, k); + Self { + s: projector.subject(&rule.antecedent), + p: projector.predicate(), + o: projector.object(&rule.consequent), + f: truth.frequency, + c: truth.confidence, + } + } +} + +/// A minimal projector that renders items as `feat=cat` and joins an +/// antecedent with `&`. Useful for tests and ndjson smoke checks; real feeds +/// supply a domain projector that emits proper namespaced IRIs. +#[derive(Debug, Clone)] +pub struct DebugProjector { + /// Predicate IRI to stamp on every emitted implication. + pub predicate: String, +} + +impl Default for DebugProjector { + fn default() -> Self { + Self { + // Not in `ruff_spo_triplet`'s *current* closed vocabulary — see the + // synergy doc: flowing ARM rules through that loader needs an + // `implies` predicate added there first. + predicate: "implies".to_string(), + } + } +} + +fn render_items(items: &[Item]) -> String { + let mut parts: Vec = items.to_vec(); + parts.sort(); + parts + .iter() + .map(|it| format!("feat{}=cat{}", it.feature, it.category)) + .collect::>() + .join("&") +} + +impl FeedProjector for DebugProjector { + fn subject(&self, antecedent: &[Item]) -> String { + format!("arm:{}", render_items(antecedent)) + } + fn predicate(&self) -> String { + self.predicate.clone() + } + fn object(&self, consequent: &[Item]) -> String { + format!("arm:{}", render_items(consequent)) + } +} + +#[cfg(test)] +mod tests { + use super::*; + + fn rule(support: f32, confidence: f32, n: u32) -> CandidateRule { + CandidateRule { + antecedent: vec![Item::new(0, 1)], + consequent: vec![Item::new(1, 0)], + support, + confidence, + n, + } + } + + #[test] + fn frequency_is_arm_confidence() { + let t = arm_to_nars(&rule(0.2, 0.83, 1000), NARS_PERSONALITY_K); + assert!((t.frequency - 0.83).abs() < 1e-6); + } + + #[test] + fn confidence_grows_with_evidence() { + // m = support × n = 0.2 × 1000 = 200; c = 200/201 ≈ 0.995 + let strong = arm_to_nars(&rule(0.2, 0.83, 1000), NARS_PERSONALITY_K); + // m = 0.2 × 10 = 2; c = 2/3 ≈ 0.667 + let weak = arm_to_nars(&rule(0.2, 0.83, 10), NARS_PERSONALITY_K); + assert!(strong.confidence > weak.confidence); + assert!((strong.confidence - 200.0 / 201.0).abs() < 1e-4); + assert!((weak.confidence - 2.0 / 3.0).abs() < 1e-4); + } + + #[test] + fn larger_k_demands_more_evidence() { + let k1 = arm_to_nars(&rule(0.1, 0.7, 50), 1.0).confidence; + let k10 = arm_to_nars(&rule(0.1, 0.7, 50), 10.0).confidence; + assert!(k10 < k1, "larger k lowers confidence for the same evidence"); + } + + #[test] + fn expectation_matches_spo_formula() { + let t = NarsTruth { + frequency: 0.9, + confidence: 0.8, + }; + // 0.8 * (0.9 - 0.5) + 0.5 = 0.82 + assert!((t.expectation() - 0.82).abs() < 1e-6); + } + + #[test] + fn triple_projection_and_truth() { + let r = rule(0.25, 0.9, 400); // m = 100; c = 100/101 + let t = CandidateTriple::from_rule(&r, &DebugProjector::default(), NARS_PERSONALITY_K); + assert_eq!(t.s, "arm:feat0=cat1"); + assert_eq!(t.p, "implies"); + assert_eq!(t.o, "arm:feat1=cat0"); + assert!((t.f - 0.9).abs() < 1e-6); + assert!((t.c - 100.0 / 101.0).abs() < 1e-4); + } +} From 8edd63bb581ae1ffd0bbd394600fc0a13d8c775f Mon Sep 17 00:00:00 2001 From: Claude Date: Sat, 30 May 2026 17:53:47 +0000 Subject: [PATCH 2/7] review(arm-discovery): 3-savant brutal review of D-ARM-13 + honesty revisions MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Convened a 3-savant brutal review (brutally-honest-tester, iron-rule-savant, dto-soa-savant — the Stage-D ratification ensemble applied to the ARM code). Consolidated verdict: LAND-with-revision (zero P0; code independently re-verified 35/35 + 17/17 no-default-features + clippy -D warnings clean). All converged findings addressed (prose/honesty/tech-debt, no logic change): - "loads through the same loader / byte-compatible" was split-true: lance_graph::parse_triples accepts `implies`, but ruff_spo_triplet::from_ndjson rejects it (closed vocab) until D-ARM-SYN-1. Downgraded to "shape-compatible" with the precise caveat in ndjson/translator/lib/README. - rule::passes doc claimed a Jirak floor that does not exist (D-ARM-7 Queued, `jirak` appears 0x). Made honest; filed ISSUE ARM-JIRAK-FLOOR as a hard prerequisite before wiring to a live SpoStore (D-ARM-5). - contract-homing drift: local CandidateRule (n:u32) disagrees with planned D-ARM-2 (WindowMetadata); "shape identical" promise was already false. Corrected docs; filed TD-ARM-CARRIER-FORK with the pub-use-when-D-ARM-2-lands path (firewall forbids depending on lance-graph, not on zero-dep lance-graph-contract). - "bit-identical weights" -> intra-platform reproducibility footnote. Refuted by the tester: suspected fmt_f32 drift vs serde_json (0 mismatches). Iron-rule confirmed arm_to_nars round-trips into TruthValue::revision (w=m) with no rival kernel; I-SUBSTRATE-MARKOV / I-VSA-IDENTITIES / I-LEGACY-API all yield. Board: ISSUES (ARM-JIRAK-FLOOR), TECH_DEBT (TD-ARM-CARRIER-FORK), AGENT_LOG, reviews/ (council-verdict + iron-rule-savant). https://claude.ai/code/session_012SorR8UbtEvYmbX8cXftj7 --- .claude/board/AGENT_LOG.md | 15 +++ .claude/board/ISSUES.md | 4 + .claude/board/TECH_DEBT.md | 24 ++++ .../aerial-d-arm-13-council-verdict.md | 42 ++++++ .../aerial-d-arm-13-iron-rule-savant.md | 121 ++++++++++++++++++ crates/lance-graph-arm-discovery/README.md | 14 +- crates/lance-graph-arm-discovery/src/lib.rs | 16 ++- .../lance-graph-arm-discovery/src/ndjson.rs | 19 ++- crates/lance-graph-arm-discovery/src/rule.rs | 32 +++-- .../src/translator.rs | 6 +- 10 files changed, 265 insertions(+), 28 deletions(-) create mode 100644 .claude/board/reviews/aerial-d-arm-13-council-verdict.md create mode 100644 .claude/board/reviews/aerial-d-arm-13-iron-rule-savant.md diff --git a/.claude/board/AGENT_LOG.md b/.claude/board/AGENT_LOG.md index 79012f73..cf25ca5c 100644 --- a/.claude/board/AGENT_LOG.md +++ b/.claude/board/AGENT_LOG.md @@ -1,3 +1,18 @@ +## [Main thread / Opus + 3 savant agents] D-ARM-13 brutal review (council) + honesty revisions + +**Branch:** claude/jolly-cori-clnf9 | **Agents:** 3 background Opus savants (brutally-honest-tester, iron-rule-savant, dto-soa-savant) — the Stage-D ratification ensemble applied to the ARM code. **Files:** +- `.claude/board/reviews/aerial-d-arm-13-{council-verdict,iron-rule-savant}.md` (council consolidation + 1 persisted review; the other 2 agents were write-denied and returned inline, captured in the consolidation) +- `crates/lance-graph-arm-discovery/src/{rule,ndjson,translator,lib}.rs` + `README.md` — honesty revisions +- `.claude/board/ISSUES.md` (ARM-JIRAK-FLOOR), `.claude/board/TECH_DEBT.md` (TD-ARM-CARRIER-FORK) + +**Cargo:** tester independently re-ran `cargo test` (35/35) + `--no-default-features` (17/17) + clippy `-D warnings` (clean); main thread re-verified 35/35 + clippy clean after the doc edits. + +**Verdict:** **LAND-with-revision** (2 LAND-with-revision + 1 HOLD-on-P1; zero P0). Code sound; all revisions are prose/honesty/tech-debt, no logic change. + +**Outcome:** DONE. Convened a 3-savant brutal review (the same family as the plan's ratification gate). Converged findings, all addressed: (1) the "loads through the *same* loader / byte-compatible" claim was split-true — `lance_graph::parse_triples` accepts `implies`, but `ruff_spo_triplet::from_ndjson` rejects it (closed vocab) until D-ARM-SYN-1 — downgraded to "shape-compatible" with the precise caveat across `ndjson`/`translator`/`lib`/`README`; (2) the `rule::passes` doc claimed a Jirak floor that doesn't exist (D-ARM-7 Queued, `jirak` appears 0×) — made honest + filed ISSUE ARM-JIRAK-FLOOR (hard prerequisite before wiring to a live SpoStore / D-ARM-5); (3) contract-homing drift — local `CandidateRule` (`n:u32`) disagrees with planned D-ARM-2 (`WindowMetadata`), "shape identical" promise was already false — corrected docs + filed TD-ARM-CARRIER-FORK with the `pub use`-when-D-ARM-2-lands path (firewall forbids depending on `lance-graph`, NOT on zero-dep `lance-graph-contract`); (4) "bit-identical weights" → intra-platform reproducibility footnote. The tester **refuted** the suspected `fmt_f32` drift (0 mismatches vs serde_json across [0,1]). Iron-rule confirmed `arm_to_nars` is a single-observation constructor that round-trips into `TruthValue::revision` (w=m) with no rival kernel, and `expectation` is byte-identical to `spo::truth` — I-SUBSTRATE-MARKOV/I-VSA-IDENTITIES/I-LEGACY-API all yield. Determinism firewall structurally intact. PR #436 updated. + +--- + ## [Main thread / Opus] Aerial+ Rust transcode (D-ARM-13) + ruff DTO/SPO/codegen synergy map **Branch:** claude/jolly-cori-clnf9 | **Files:** diff --git a/.claude/board/ISSUES.md b/.claude/board/ISSUES.md index 62572fb2..3256889e 100644 --- a/.claude/board/ISSUES.md +++ b/.claude/board/ISSUES.md @@ -72,6 +72,10 @@ gets buried. ## Open Issues +## 2026-05-30 — [ARM-JIRAK-FLOOR] Aerial+ proposer (D-ARM-13) ships without the mandatory Jirak Stage-A floor + +**Status: OPEN.** Surfaced by the 3-savant brutal review of D-ARM-13 (iron-rule-savant #1 finding, brutally-honest-tester P1). The transcoded Aerial+ proposer (`crates/lance-graph-arm-discovery`) gates rule emission only on classical `min_support`/`min_confidence` (`extract.rs` → `rule::CandidateRule::passes`). `I-NOISE-FLOOR-JIRAK` and `streaming-arm-nars-discovery-v1.md` §4 (line 395 "This is not optional") + §11.1 declare the Jirak weak-dependence significance floor **mandatory at Stage A** — but `jirak` exists nowhere in the crate and **D-ARM-7 (the Jirak module) is Queued**. Consequence: with `c = m/(m+k)` saturating as `m = support×n` grows, a thin-but-frequent spurious rule at a 200K window becomes a high-confidence candidate → "substrate calcifies on noise." **Hard prerequisite:** D-ARM-7 MUST land before this proposer is wired into D-ARM-5 (the first stage where `(f,c)` meets a live `SpoStore` + `TruthValue::revision`). Documented honestly in `rule.rs::passes` doc + the synergy doc §4. Resolve by: implement D-ARM-7 and route `extract_rules` emission through `jirak_significance_threshold` BEFORE the classical floor. + ## 2026-04-20 — [E-MEMB-1] Python↔Rust slice layouts are incompatible at the 10 kD membrane **Status:** Open diff --git a/.claude/board/TECH_DEBT.md b/.claude/board/TECH_DEBT.md index a9d985ac..bbe29c5c 100644 --- a/.claude/board/TECH_DEBT.md +++ b/.claude/board/TECH_DEBT.md @@ -13,6 +13,30 @@ --- +### TD-ARM-CARRIER-FORK (D-ARM-13 / streaming-arm-nars-discovery-v1) + +**Status: Open.** Surfaced by the 3-savant brutal review of D-ARM-13 +(dto-soa-savant Finding 3, the load-bearing one). `crates/lance-graph-arm-discovery` +defines **local** `rule::{CandidateRule, Proposer, Item}` + `translator::NarsTruth` +because the planned contract homes — `lance-graph-contract::{CandidateRule, +Proposer, ProvenanceTier}` (D-ARM-1/D-ARM-2) — are still **Queued** and do not +exist yet (verified: empty grep in the contract crate). Two debts: (a) the local +`CandidateRule` carries a bare `n: u32` while D-ARM-2 specs `WindowMetadata` — +the field sets already disagree, so the "re-export, shape identical" promise is +not yet true (doc now states this honestly, `rule.rs` module + struct docs); +(b) `rule`/`translator` are re-exported at `lib.rs` top level, so if D-ARM-2 +stays Queued, downstream may import the excluded crate's types and make them the +de-facto canonical — the ThinkingStyle "contract exists to unify, nobody depends +on it" CRITICAL pattern (`TYPE_DUPLICATION_MAP.md`). **Pay by:** when D-ARM-1/2 +land, `path`-dep the zero-dep `lance-graph-contract` (the determinism firewall +forbids depending on `lance-graph`, NOT on the contract crate) and convert the +locals to `pub use` re-exports; freeze the D-ARM-2 field set (recommend bare +`n: u32`, not `WindowMetadata`) so the shape genuinely matches. Secondary P2 +test debt (brutally-honest-tester): the `max_antecedent ≥ 2` recovery path is +enumerated but untested; reproducibility tests assert bit-exact f32 (intra-target +only). Both non-blocking; add coverage when D-ARM-3 (pair-stats) lands the +multi-antecedent path for real. + ### TD-RESONANCEDTO-DUP-1 (bindspace-singleton-to-mailbox-soa-v1) - **Severity:** P3 (name collision; two distinct `ResonanceDto` structs under the same name) diff --git a/.claude/board/reviews/aerial-d-arm-13-council-verdict.md b/.claude/board/reviews/aerial-d-arm-13-council-verdict.md new file mode 100644 index 00000000..86bdfad0 --- /dev/null +++ b/.claude/board/reviews/aerial-d-arm-13-council-verdict.md @@ -0,0 +1,42 @@ +# Council verdict — D-ARM-13 (Aerial+ Rust transcode) + +> 3-savant brutal review convened 2026-05-30 on branch `claude/jolly-cori-clnf9` +> (the Stage-D ratification ensemble applied to the ARM *code*). Background +> Opus agents; two were write-denied at the tool level and returned their +> reviews inline (captured below); iron-rule-savant persisted its own file +> (`aerial-d-arm-13-iron-rule-savant.md`). + +## Consolidated verdict: **LAND-with-revision** + +| Savant | Verdict | Headline | +| --- | --- | --- | +| brutally-honest-tester | **HOLD pending fixes** (0 P0, 2 P1) | "loads through the same loader" is split-true; Jirak floor missing | +| iron-rule-savant | **LAND-with-revision** | code yields (doesn't violate) every iron rule; the Jirak *doc-comment* writes a cheque the code can't cash | +| dto-soa-savant | **LAND-with-revision** | no parallel store / no fifth column; contract-homing drift is the real risk | + +**No P0. Code is sound** — independently re-verified by the tester: `cargo test` +35/35, `--no-default-features` 17/17, clippy `-D warnings` clean, `#![forbid(unsafe_code)]`, +all 9 modules registered, `exclude` standalone done right (AP3/AP4/AP6/AP7 clean). +Every revision below is **prose / honesty / tech-debt**, not logic. + +## Converged action ledger (all applied this commit unless noted) + +| # | Finding (who) | Severity | Fix | Done | +| --- | --- | --- | --- | --- | +| 1 | "loads through the *same* loader / byte-compatible" overstates — `lance_graph::parse_triples` **accepts** `implies`, but `ruff_spo_triplet::from_ndjson` **rejects** it (closed vocab) until D-ARM-SYN-1 (tester P1, dto-soa F2, iron-rule) | P1 | Downgrade to "shape-compatible" + precise loader caveat in `ndjson.rs`, `translator.rs`, `lib.rs`, `README.md` | ✅ | +| 2 | Jirak floor doc-comment claims an upstream gate that doesn't exist; D-ARM-7 Queued (iron-rule #1, tester P1) | P1 | Make `rule::passes` doc honest ("not implemented; MUST NOT wire to live SpoStore until D-ARM-7"); file **ISSUE ARM-JIRAK-FLOOR** | ✅ | +| 3 | Contract-homing drift — local `CandidateRule` disagrees with D-ARM-2 (`n:u32` vs `WindowMetadata`); "shape identical" promise already false (dto-soa F3) | P1 | Honest `rule.rs` module+struct docs; file **TD-ARM-CARRIER-FORK** w/ the `pub use`-when-D-ARM-2-lands path | ✅ | +| 4 | "bit-identical weights" overclaims portability (iron-rule secondary, tester P2) | P2 | Footnote: intra-platform reproducibility, not bitwise-portable determinism (`lib.rs`, `README`) | ✅ | +| 5 | `max_antecedent ≥ 2` recovery untested; reproducibility asserts bit-exact f32 (tester P2) | P2 | Logged in TD-ARM-CARRIER-FORK; add coverage when D-ARM-3 lands the real multi-antecedent path | ⏶ logged | +| — | **Refuted:** `fmt_f32` drift vs `serde_json` — tester ran the full [0,1] grid: **0 mismatches** (Rust `{}` already shortest-round-trip; `1`→`1.0` is exactly what it patches) | — | no action | ✅ refuted | + +## Cross-agent agreements (high confidence) + +- **D-ARM-SYN-1 is the one genuine blocker** for the "same ruff loader" story: `ruff_spo_triplet::Predicate` is closed and `from_str`/`from_ndjson` hard-reject `implies`. All three flagged it; correctly council-gated (not silently patched). +- **D-ARM-7 (Jirak) is a doctrinal — not compile — dependency.** Enforce D-ARM-7 *before* any D-ARM-5 that consumes this proposer (D-ARM-5 = where `(f,c)` first meets live `revision` + `SpoStore`). +- **The determinism firewall is structurally intact** (excluded, std-only, seeded, proposal-only output). `arm_to_nars`'s `c = m/(m+k)` round-trips into the canonical `TruthValue::revision` as evidence `w = m` exactly; `expectation` is byte-identical to `spo::truth::TruthValue::expectation` — no rival revision kernel (iron-rule, I-SUBSTRATE-MARKOV check). +- **`NarsTruth` is a transport handle, not a parallel truth store** — the proof is it carries *no* revision algebra (dto-soa F1). + +## Adjacency (council consensus) + +D-ARM-13 = the **D-ARM-9 Aerial leg pulled in-process** (supersedes the plan §14 Python-IPC deferral per user directive). Hardest tie: **D-ARM-7 (Jirak)**. Pre-stages **D-ARM-1/D-ARM-2** (contract carriers — re-export when they land). Partially lands **D-ARM-4** (`translator`, now ahead of its own spec). Feeds **D-ARM-5** (hypothesis test) and the **D-ARM-10/11** codegen gates. Spawns **D-ARM-SYN-1/2/3**. Realises **E-DISCOVERY-CODEGEN-BRACKET-1** and **E-INTERPRET-NOT-STORE-1** at the shape level — qualified: truth scale genuinely unified, triple shape unified-by-convention, contract home not yet unified. diff --git a/.claude/board/reviews/aerial-d-arm-13-iron-rule-savant.md b/.claude/board/reviews/aerial-d-arm-13-iron-rule-savant.md new file mode 100644 index 00000000..20bec684 --- /dev/null +++ b/.claude/board/reviews/aerial-d-arm-13-iron-rule-savant.md @@ -0,0 +1,121 @@ +# IRON_RULE_SAVANT review — D-ARM-13 (Aerial+ Rust transcode) + +> **Subject:** `crates/lance-graph-arm-discovery/` on branch `claude/jolly-cori-clnf9` +> **Council question:** does this candidate violate an iron rule or doctrinal invariant — especially **I-NOISE-FLOOR-JIRAK** by claiming significance the n-bound doesn't support? +> **Reviewer lens:** substrate-veto angle. Any VIOLATES is an automatic REJECT for the council. +> **Reviewed:** 2026-05-30. Read-only doctrine pass; no cargo, no edits. + +--- + +## Verdict: **LAND-with-revision** + +No iron rule is *violated* by the code as written. The crate is honest about what it is (an upstream, seeded, feature-gated *proposer*) and it does **not** make any statistical-significance claim in code — so I-NOISE-FLOOR-JIRAK has nothing to bite on yet. But the crate ships a **doc-comment promissory note** (`rule.rs:70-71`) that asserts a Jirak floor exists "upstream of this one," and **that gate does not exist anywhere in the crate or its callers**. That is not a violation today; it is a *primed* violation — the moment this proposer is wired to a live `SpoStore` (D-ARM-5) without D-ARM-7 landing first, plan §11.1's named failure (substrate calcifies on noise) fires. The revision required before LAND is narrow and is spelled out in Finding 1. + +This is **not a HOLD**, because: +- the crate is `exclude`d from the workspace (root `Cargo.toml:46`, under `exclude`, confirmed absent from `members`), +- it is std-only / zero-dep (no external `use` beyond `crate::`/`super::`/`std::`), +- its output type is `CandidateRule` / `CandidateTriple` — a *proposal*, never a committed triple, with no `SpoStore` write path in this crate, + +so nothing it produces can reach the deterministic compile path or the SPO store *from within this PR*. The firewall is structurally intact at the crate boundary. The HOLD would only be warranted if this crate itself performed the Stage-C revision; it does not (Stage C lives in `lance-graph`, D-ARM-5, still Queued). + +--- + +## Per-iron-rule findings + +### I-NOISE-FLOOR-JIRAK — **YIELDS (with a mandatory pre-wire condition)** — the big one + +**What the rule demands:** any noise-floor or statistical-significance claim on this substrate must cite Jirak 2016 weak-dependence rates, never classical IID Berry-Esseen (`CLAUDE.md:311-343`). The plan elevates this to MANDATORY at Stage A: §4 "This is not optional," §2.1 termination criterion `n × support ≥ JIRAK_MIN_EVIDENCE`, §11.1 risk, §15 invariant table. + +**What the code actually does — the gates, concretely:** + +- `rule.rs:73-75` `CandidateRule::passes(min_support, min_confidence)` gates ONLY on the two classical ARM floors: `self.support >= min_support && self.confidence >= min_confidence`. No Jirak term. +- `extract.rs:172` is the single emission gate in the Aerial proposer: `if rule.passes(params.min_support, params.min_confidence) { out.push(rule); }`. Classical-only. +- `extract.rs:78` the apriori pre-prune also uses `params.min_support` only. +- `ExtractParams` (`extract.rs:46-56`) carries `min_support: 0.01`, `min_confidence: 0.5` — the plan §2.1 classical defaults — and **no** `jirak_*` field. +- `aerial/mod.rs:103-115` `AerialProposer::mine()` / `next_batch()` returns `extract_rules(...)` directly. No post-filter. +- **Grep confirms:** the token `jirak` appears **zero times** in `crates/lance-graph-arm-discovery/src/`. There is no `jirak` module, no `JIRAK_MIN_EVIDENCE` const, no significance-deviation test against the independence null. + +**Is this a VIOLATES?** No — and the distinction is load-bearing for the council. I-NOISE-FLOOR-JIRAK is triggered by *making a significance claim* ("N σ above noise floor," a calibrated threshold presented as principled). **This crate makes no such claim.** `support`/`confidence` are honest descriptive statistics measured on the window (`encode.rs:165-184`), and `passes()` is explicitly documented as the *classical* gate. The doc-comment even names the rule and says the Jirak floor is separate. A descriptive statistic with no significance assertion does not invoke Berry-Esseen, so there is nothing to mis-cite. **YIELDS.** + +**BUT — the doc-comment writes a cheque the codebase can't cash.** `rule.rs:68-71` states verbatim: + +> "The Jirak-bound significance floor (`I-NOISE-FLOOR-JIRAK`) is a *separate*, stricter gate applied **upstream of this one** — see the module-level docs of the proposer." + +Following that pointer: the proposer module docs (`aerial/mod.rs:17-24`) describe the determinism rationale and say output "is gated by the downstream ratification council" — they do **not** describe, implement, or reference any Jirak upstream gate. The synergy doc (`aerial-arm-ruff-spo-codegen-synergies.md:171, §4`) repeats the same claim ("the `I-NOISE-FLOOR-JIRAK` floor (D-ARM-7, separate gate) keeps the thin tail from ever being emitted"). **D-ARM-7 is Queued, not Shipped** (`STATUS_BOARD.md:630`). So the "upstream gate" the comment promises is a *future deliverable that does not yet exist*. The code therefore documents a safety property it does not possess. + +This is the precise risk plan §11.1 names: "Stage A emits rules at high rates that scrape past `MIN_CONFIDENCE` but below the Jirak floor; Stage C's revise weights them in; the substrate calcifies on weak signal." With `min_confidence` defaulting to 0.5 (`extract.rs:53`) and the NARS confidence mapping `c = m/(m+k)` saturating toward 1 as `m = support×n` grows (`translator.rs:57-58`), a thin-but-frequent spurious correlation at a 200K window produces a *high-c* candidate — exactly the "weak signal the revise cannot down-weight fast enough" case. The Jirak floor is the only thing in the design that stops it, and it is absent. + +**Required revision before LAND (any one of these closes it):** +1. **Preferred:** weaken the `rule.rs:70-71` doc-comment from a present-tense assertion ("is a separate gate applied upstream") to a forward reference ("**will be** gated upstream by D-ARM-7 `jirak`; until that lands, `passes()` is the *only* gate and this proposer MUST NOT be wired to a live `SpoStore`"). Make the absence honest. Cheapest; no code. +2. Add the `jirak` floor field to `ExtractParams` now (even if the default is a permissive `0.0` placeholder that cites D-ARM-7 as the source of the real bound), so the gate *site* exists in `extract.rs:172` and D-ARM-7 only has to supply the number. Removes the "primed violation" entirely. +3. Add a crate-level `// DOCTRINE:` note + a `#[test]` canary that asserts the proposer is not exported for SPO ingestion until a Jirak gate is present (a compile-time tripwire mirroring plan §11.1's canary). + +**Council bottom line on the big one:** the *finding the council asked me to test* — "does this claim significance the n-bound doesn't support?" — **the code does not; the doc-comment claims a floor that doesn't exist.** Land only after the comment is made honest (or the gate site is stubbed). Do **not** let D-ARM-5 (the live-`SpoStore` round-trip) reference this proposer until D-ARM-7 lands. The exclusion + proposal-only output is what downgrades this from HOLD to LAND-with-revision. + +### I-SUBSTRATE-MARKOV — **YIELDS** (verified against the canonical revision) + +**What I checked:** does the truth mapping silently diverge from `lance_graph::graph::spo::truth::TruthValue::revision` (`truth.rs:57-72`) and thereby break the Chapman-Kolmogorov/NARS revision invariant? + +**The two functions are different operations and must not be confused:** +- The crate's `arm_to_nars` (`translator.rs:53-63`) is a **single-observation constructor**: it turns one ARM rule's raw stats into an *initial* `(f, c)`. `f = confidence` (P(Y|X)); `c = m/(m+k)`, `m = support×n`, `k=1.0`. +- The canonical `TruthValue::revision` (`truth.rs:58-72`) is a **two-belief combinator**: `w_i = c_i/(1-c_i)`, `w = w1+w2`, `f = (w1·f1+w2·f2)/w`, `c = w/(w+k)`, `k=1.0`. + +**The crate does NOT reimplement revision** — and that is correct. It stops at producing the `(f,c)` that a *downstream* Stage C (D-ARM-5, in `lance-graph`, not this crate) feeds into the canonical `revision`. So there is no parallel/divergent revision kernel here to violate the semigroup property. `translator.rs:27-30` is explicit about this intent ("Deliberately *not* a re-implementation of the SPO store's `TruthValue`... honours `E-SOA-IS-THE-ONLY`"). + +**Algebraic consistency check (the subtle part):** for the downstream `revision` to be coherent, the crate's `c` must be the inverse of revision's confidence→evidence map. Revision recovers evidence as `w = c/(1-c)`. The crate sets `c = m/(m+k)` ⇒ `c/(1-c) = m/k = m` (at `k=1`). So a candidate carrying `c` round-trips into `revision` as evidence weight `w = m = support×n` — i.e. the crate's "evidential mass" IS exactly the NARS evidence count the revision arithmetic expects. **They agree by construction.** No divergence. + +**The one place the crate touches the SPO truth algebra directly** is `NarsTruth::expectation` (`translator.rs:43-45`): `c·(f−0.5)+0.5`. This is **byte-identical** to `TruthValue::expectation` (`truth.rs:48-50`) and is asserted by `translator.rs:200-207`. Same `TruthGate` will gate an ARM-mined rule and a structurally-extracted triple. Correct. + +Bundle math (vsa_bind / vsa_bundle / d=16384 / concentration-of-measure) is entirely untouched — this crate never enters that path. **YIELDS.** + +### I-VSA-IDENTITIES — **YIELDS** (clean separation; no register touched) + +ARM operates strictly on identity-typed atoms and never superposes content: +- `Item { feature: u32, category: u32 }` (`rule.rs:17-23`) is a pure identity pair — small ints, `Eq + Hash + Ord`, the Test-0 "natural ID/enum" register the rule blesses. +- `CandidateTriple { s, p, o, f, c }` (`translator.rs:86-98`) is `(s,p,o)` identity IRIs + scalar truth — never a fingerprint. +- The autoencoder's `Vec` one-hot input (`encode.rs:90-98`) and decoder probabilities (`autoencoder.rs:96-124`) are **a learned compressor's internal activations**, not a VSA carrier. They are never `vsa_bundle`d, never written to a `BindSpace` column, never persisted. They live and die inside one `extract_rules` call. +- **No `Vsa16kF32` / `Binary16K` / CAM-PQ / palette-codebook / quantized register appears anywhere in the crate** (no such import; std-only). `arm_to_nars` and the ndjson emitter touch only `f32` scalars + `String` IRIs. + +The four tests are respected by virtue of the crate using the register (HashMap/enum-style `Item`) for exact-match work rather than reaching for VSA at all. There is no superposition of content here to destroy. **YIELDS.** + +### I-LEGACY-API-FEATURE-GATED — **YIELDS (NA-leaning)** + +No v1/v2 layout reclaim is in play. The crate defines no packed bitfield accessor (`pack`/`unpack`/`with_*`/`set_*`) over a versioned layout; `CausalEdge64` is not touched. The `aerial` feature (`Cargo.toml:34-36`, `lib.rs:63-64`) gates the *autoencoder module*, not a v1 accessor — and it is gated cleanly: `#[cfg(feature = "aerial")] pub mod aerial;` with the carriers (`rule`/`translator`/`ndjson`/`encode`) compiling feature-free, so a deployment can take the truth/triple contract without the AE. That is the *correct* application of feature-gating doctrine (isolate the heavy/nondeterministic path), not the anti-pattern (silent semantic divergence under a flag). No AP1 alias. **YIELDS.** + +--- + +## Determinism boundary — enforced or merely documented? + +**Structurally enforced at the crate boundary, but "seeded ⇒ reproducible" is NOT "deterministic" and the docs overclaim by one word.** + +What IS enforced (genuine, not vibes): +- The crate is in `exclude` (root `Cargo.toml:46`), confirmed **absent** from `members`. The nondeterministic AE cannot enter the `lance-graph` compile graph — verified, not asserted. +- Output is `CandidateRule` — a proposal. There is **no `SpoStore` write, no codegen call, no ratification-bypass** path in the crate. The only sink is `ndjson::to_ndjson` (a `String`), which a *downstream* loader must choose to ingest. Nothing nondeterministic crosses into compile output from here. +- The AE is seeded through one `Rng` stream (`aerial/mod.rs:84`, `rng.rs` SplitMix64) covering weight init, denoising mask, and epoch shuffle. `reproducible_from_seed` (`mod.rs:194-205`) and `training_is_reproducible_from_seed` (`autoencoder.rs:337-347`) assert bit-identical rules / weights for same seed+data+build. + +Where the claim is too strong — **"seeded ⇒ reproducible" ≠ "deterministic across platforms":** +- The forward/backward pass is single-threaded fixed-order f32 accumulation (`autoencoder.rs:100-117, 199-232`) — good, that removes *reduction-order* nondeterminism **within one binary**. +- But the AE uses f32 **transcendental / libm** functions whose last-ULP results are **not guaranteed identical across platforms, libm versions, or `-ffast-math`-style codegen**: `tanh` (`autoencoder.rs:106`), `exp` (`autoencoder.rs:246`), `ln` (`autoencoder.rs:263`; `rng.rs:51`), `cos` + `sqrt` (`rng.rs:51-52`). A one-ULP difference in `tanh` early in training, amplified over 500 epochs of SGD, can flip a borderline `p > τ_c` reconstruction-probe decision (`extract.rs:155`) and thus *add or drop a rule* on a different machine. So same-seed reproducibility holds **on the same platform/build**, which is all the tests check and all the audit story needs — but the lib-doc phrase "Same seed, same data, and same hyper-parameters give **bit-identical** weights and identical rules" (`lib.rs:39-42`) is true only intra-platform. It should say "bit-identical *on a fixed platform/toolchain*." + +Why this is acceptable (not a HOLD): the determinism that the doctrine *requires* is determinism of the **compile path downstream of the ratification firewall** — `op_emitter` Rust codegen, `ruff_python_codegen`. This crate sits **upstream** of that firewall (synergy doc §5 table; plan §0 Stage-D firewall). A proposer is *allowed* to be nondeterministic precisely because the council + hypothesis-test re-derive and ratify before anything compiles. Platform-variance in the proposer changes *which candidates get proposed*, not *what compiles* — and the council is the gate that absorbs that. So "seeded for auditability, fenced behind exclusion + proposal-only output" is the correct posture. The only fix needed is the one-word doc honesty (`lib.rs:41` "bit-identical" → "bit-identical on a fixed platform"). + +**Determinism verdict:** boundary is real and enforced by construction; the reproducibility *claim* is mildly overstated (cross-platform f32 transcendental variance) and should be footnoted. Folds into the LAND-with-revision doc pass. + +--- + +## Adjacency / epiphany (which D-ARM-* this most depends on or blocks) + +D-ARM-13 is the **transcode of D-ARM-9's Aerial leg pulled in-process** (the plan §14 had deferred the autoencoder to Python-over-IPC; the user directive superseded that — see `AGENT_LOG` top entry + `STATUS_BOARD.md:636`). Its hardest dependency is **D-ARM-7 (Jirak)**: not a compile dependency, but a *doctrinal* one — D-ARM-13's own doc-comments (`rule.rs:70`, synergy §4) promise the Jirak floor that only D-ARM-7 supplies, and plan §11.1 makes that floor the sole defense against the calcify-on-noise failure. **D-ARM-13 should be treated as blocked-for-SPO-ingestion-purposes by D-ARM-7**, even though it ships green standalone. It also pre-stages **D-ARM-1/D-ARM-2** (the contract carriers): `rule.rs:8-9, 84` and `translator.rs` are deliberate *local mirrors* of the planned `lance-graph-contract::{CandidateRule, Proposer}` — when D-ARM-1/2 land, this crate should re-export them rather than keep the duplicates, or the AP-style "two CandidateRule shapes" drift the plan warns against (§3.2 seam) sets in. It most directly **blocks/feeds D-ARM-5** (hypothesis-test): D-ARM-5 is where this proposer's `(f,c)` actually meets `TruthValue::revision` and the live `SpoStore` — and D-ARM-5 is exactly the wave where the missing Jirak gate becomes load-bearing, so **the sequencing constraint is firm: D-ARM-7 before D-ARM-5-consuming-this-proposer.** The epiphany I see, and endorse: the transcode correctly realizes `E-DISCOVERY-CODEGEN-BRACKET-1` (Aerial = the runtime-data frontend of the three-frontend/one-substrate/two-codegen bracket; synergy doc §0) and respects `E-INTERPRET-NOT-STORE-1` (ARM rules are one interpretation projection, emitted as ndjson, never the canonical store). The genuinely useful *new* finding surfaced here is the **predicate-vocabulary gap** (D-ARM-SYN-1): `ruff_spo_triplet::Predicate` is a closed set with no `Implies`/`CoOccursWith`, so the very ndjson this crate emits (`DebugProjector` → `"implies"`, `translator.rs:131-135`) cannot yet load through the canonical `parse_triples` loader — a real, deliberate ontology decision the council must take before the bracket closes end-to-end. That gap is correctly flagged as council-gated rather than silently patched, which is itself doctrine-respecting. + +--- + +## Summary table + +| Rule | Verdict | One-line rationale | +|---|---|---| +| I-NOISE-FLOOR-JIRAK | YIELDS* | code makes no significance claim; but `rule.rs:70-71` doc asserts an upstream Jirak gate that does not exist (D-ARM-7 Queued) — make the comment honest before LAND, and never wire to live SpoStore (D-ARM-5) until D-ARM-7 lands | +| I-SUBSTRATE-MARKOV | YIELDS | `arm_to_nars` is a single-obs constructor, not a rival revision; `c=m/(m+k)` round-trips into canonical `TruthValue::revision` as `w=m` exactly; `expectation` is byte-identical; bundle math untouched | +| I-VSA-IDENTITIES | YIELDS | operates on identity `Item{feature,category}` + `(s,p,o)` IRIs; AE activations are a compressor's internals, never bundled/persisted; no Vsa/CAM-PQ/quantized register touched | +| I-LEGACY-API-FEATURE-GATED | YIELDS | no v1/v2 layout reclaim; `aerial` feature gates the module cleanly (carriers compile feature-free), not a silently-divergent v1 accessor | + +\* the `*` is the LAND-condition: one honest-doc edit (plus the `lib.rs:41` "bit-identical" → "bit-identical on a fixed platform" footnote) and a firm D-ARM-7-before-D-ARM-5 sequencing note. No code logic change required; the firewall (exclusion + proposal-only output) holds today. diff --git a/crates/lance-graph-arm-discovery/README.md b/crates/lance-graph-arm-discovery/README.md index cffbc101..cf524f99 100644 --- a/crates/lance-graph-arm-discovery/README.md +++ b/crates/lance-graph-arm-discovery/README.md @@ -9,9 +9,11 @@ Rust transcode of **Aerial+** — neurosymbolic association-rule mining (Karabulut, Groth, Degeler, *Neurosymbolic Association Rule Mining from Tabular Data*, arXiv 2504.19354v1). The **upstream proposer leg** of `streaming-arm-nars-discovery-v1.md`: it mines `(X → Y)` rules from tabular -runtime data and lifts each into a NARS-truth SPO candidate that the -`lance_graph` SPO store consumes through the **same ndjson contract** as the -static `ruff_spo_triplet` extractor. +runtime data and lifts each into a NARS-truth SPO candidate emitted in the +**same ndjson shape** (`{s,p,o,f,c}`) as the static `ruff_spo_triplet` +extractor. The lance-graph `parse_triples` loader consumes it directly; the +`ruff` `from_ndjson` loader accepts it only after its closed predicate +vocabulary gains an implication relation (D-ARM-SYN-1). ```text rows ─► one-hot ─► denoising autoencoder ─► Algorithm 1 probe ─► CandidateRule @@ -34,8 +36,10 @@ like `bgz17` / `deepnsm`) and depends on `std` only. Two reasons: must vet before any codegen leg consumes it. 2. **Reproducibility.** Every random source draws from one seeded SplitMix64 stream (`aerial::Rng`), so the same seed + data + hyper-parameters give - bit-identical weights and identical mined rules — auditable despite being - a neural net. + reproducible weights and identical mined rules *on a given target* — + auditable despite being a neural net. (Intra-platform reproducibility, not + bitwise-portable determinism: float `tanh`/`exp` + FMA can differ across + targets.) ## Layout diff --git a/crates/lance-graph-arm-discovery/src/lib.rs b/crates/lance-graph-arm-discovery/src/lib.rs index a154589c..97c77bc1 100644 --- a/crates/lance-graph-arm-discovery/src/lib.rs +++ b/crates/lance-graph-arm-discovery/src/lib.rs @@ -9,9 +9,11 @@ //! Degeler — *Neurosymbolic Association Rule Mining from Tabular Data*, //! arXiv 2504.19354v1, Apr 2025), the neurosymbolic association-rule miner. //! It mines `(X → Y)` rules from tabular runtime data and lifts each rule -//! into a NARS-truth-carrying SPO candidate that the existing -//! `lance_graph::graph::spo` store consumes through the **same ndjson -//! contract** as the static `ruff_spo_triplet` extractor. +//! into a NARS-truth-carrying SPO candidate emitted in the **same ndjson +//! shape** (`{s,p,o,f,c}`) as the static `ruff_spo_triplet` extractor. The +//! lance-graph `parse_triples` loader consumes it directly; the `ruff` +//! `from_ndjson` loader will accept it only once its closed predicate +//! vocabulary gains an implication relation (D-ARM-SYN-1) — see [`ndjson`]. //! //! # Pipeline position //! @@ -37,9 +39,11 @@ //! consequences are baked into this crate: //! //! 1. The autoencoder is **seedable** ([`aerial::Rng`]). Same seed, same data, -//! and same hyper-parameters give bit-identical weights and identical -//! rules. This makes the proposer reproducible for tests and audits even -//! though it is not the canonical deterministic path. +//! and same hyper-parameters give reproducible weights and identical rules +//! *on a given target*. (This is intra-platform reproducibility, not +//! bitwise-portable determinism: seeded f32 `tanh`/`exp`/`ln` and FMA +//! contraction can differ across targets.) It makes the proposer auditable +//! even though it is not the canonical deterministic path. //! 2. The output is a plain [`rule::CandidateRule`] — a *proposal*, not a //! committed triple. Promotion to the SPO store is the downstream //! hypothesis-test + council's job, not this crate's. diff --git a/crates/lance-graph-arm-discovery/src/ndjson.rs b/crates/lance-graph-arm-discovery/src/ndjson.rs index 4026b6aa..74a16b86 100644 --- a/crates/lance-graph-arm-discovery/src/ndjson.rs +++ b/crates/lance-graph-arm-discovery/src/ndjson.rs @@ -3,12 +3,19 @@ //! Newline-delimited JSON emit for [`CandidateTriple`]. //! -//! The on-the-wire shape is **byte-compatible** with `ruff_spo_triplet::ndjson` -//! and with what `lance_graph::graph::spo::odoo_ontology::parse_triples` -//! reads: one `{"s","p","o","f","c"}` object per line, field order -//! `s, p, o, f, c`, trailing newline. Emitting through this module is what -//! lets an ARM-discovered rule load into the SPO store through the *same* -//! loader as the static `ruff` extractor — no transform, no second format. +//! The on-the-wire shape is **shape-compatible** (same `{"s","p","o","f","c"}` +//! object per line, field order `s, p, o, f, c`, trailing newline) with +//! `ruff_spo_triplet::ndjson` and with what +//! `lance_graph::graph::spo::odoo_ontology::parse_triples` reads. +//! +//! **Loader caveat (verified by review):** the lance-graph `parse_triples` +//! loader does *not* check the predicate and **accepts** the output as-is. But +//! `ruff_spo_triplet::from_ndjson` enforces a *closed predicate vocabulary* +//! and **rejects** the `implies` predicate this crate emits — so output flows +//! into the SPO store via `parse_triples` today, but NOT through the `ruff` +//! loader until `Implies` is added to that vocabulary (D-ARM-SYN-1). "Same +//! loader as the static ruff extractor" is the *goal*, gated on SYN-1; it is +//! not true on this branch. //! //! This is a zero-dependency hand emitter (no `serde_json`) so the crate //! stays std-only and offline-buildable. It produces the same logical shape; diff --git a/crates/lance-graph-arm-discovery/src/rule.rs b/crates/lance-graph-arm-discovery/src/rule.rs index 4d5ca367..fa2cebbc 100644 --- a/crates/lance-graph-arm-discovery/src/rule.rs +++ b/crates/lance-graph-arm-discovery/src/rule.rs @@ -4,9 +4,14 @@ //! The proposer output carrier — `CandidateRule` and the `Proposer` trait. //! //! This is the shape every Stage-A proposer (Aerial+, the pair-stats trunk, -//! the Python IPC fan-in) emits. It is the local mirror of the planned -//! `lance-graph-contract::CandidateRule` (D-ARM-2); when that contract type -//! lands, this re-exports it and the field shape stays identical. +//! the Python IPC fan-in) emits. It is the **local mirror** of the planned +//! `lance-graph-contract::CandidateRule` (D-ARM-2) — *not yet field-frozen*: +//! the planned carrier pairs the rule with a `WindowMetadata`, whereas this +//! local one carries a bare `n: u32`. When D-ARM-2 lands, adopt it by +//! `pub use` re-export — the determinism firewall forbids depending on +//! `lance-graph`, NOT on the zero-dep `lance-graph-contract`, so this crate +//! can path-dep the contract and stay zero-dep-from-the-spine. Until then +//! treat this as the migration seam, not the canonical type (TD-ARM-CARRIER-FORK). /// One `(feature, category)` atom — the unit an antecedent / consequent is /// built from. @@ -32,9 +37,11 @@ impl Item { /// A mined association rule `antecedent → consequent` with ARM statistics. /// -/// Field-for-field the planned `lance-graph-contract::CandidateRule` -/// (D-ARM-2). `support` and `confidence` are the classical ARM quantities -/// (paper §2): +/// The local proposer carrier — mirrors the planned +/// `lance-graph-contract::CandidateRule` (D-ARM-2) but does **not** yet match +/// it field-for-field (D-ARM-2 pairs the rule with `WindowMetadata`; this +/// carries a bare `n`). `support` and `confidence` are the classical ARM +/// quantities (paper §2): /// /// - `support` = |{rows ⊇ antecedent ∪ consequent}| / n /// - `confidence` = |{rows ⊇ antecedent ∪ consequent}| / |{rows ⊇ antecedent}| @@ -66,9 +73,16 @@ impl CandidateRule { } /// A rule survives the classical ARM gate iff it clears both the minimum - /// support and minimum confidence floors. The Jirak-bound significance - /// floor (`I-NOISE-FLOOR-JIRAK`) is a *separate*, stricter gate applied - /// upstream of this one — see the module-level docs of the proposer. + /// support and minimum confidence floors. + /// + /// **Not implemented yet:** the Jirak-bound significance floor + /// (`I-NOISE-FLOOR-JIRAK`, deliverable D-ARM-7) is the *mandatory* Stage-A + /// gate per the plan, but it does **not** exist in this crate today + /// (`jirak` appears nowhere; D-ARM-7 is Queued). Until it lands, `passes()` + /// is the ONLY gate, and this proposer MUST NOT be wired to a live + /// `SpoStore`: the classical floor alone leaks thin-but-frequent rules past + /// the substrate noise floor (plan §11.1 — "the substrate calcifies on + /// noise"). Tracked as ISSUE ARM-JIRAK-FLOOR. #[must_use] pub fn passes(&self, min_support: f32, min_confidence: f32) -> bool { self.support >= min_support && self.confidence >= min_confidence diff --git a/crates/lance-graph-arm-discovery/src/translator.rs b/crates/lance-graph-arm-discovery/src/translator.rs index b2b6bbba..289251a7 100644 --- a/crates/lance-graph-arm-discovery/src/translator.rs +++ b/crates/lance-graph-arm-discovery/src/translator.rs @@ -76,8 +76,10 @@ pub trait FeedProjector { fn object(&self, consequent: &[Item]) -> String; } -/// One SPO triple with NARS truth — byte-compatible with -/// `ruff_spo_triplet::Triple` and the SPO store's ndjson loader. +/// One SPO triple with NARS truth — shape-compatible with +/// `ruff_spo_triplet::Triple` and the SPO store's ndjson loader (same +/// `{s,p,o,f,c}` fields; see [`crate::ndjson`] for the predicate-vocabulary +/// caveat that gates the `ruff` loader on D-ARM-SYN-1). /// /// `(s, p, o)` is the identity; `(f, c)` is the data-derived truth. The /// `origin` byte records that this triple came from the ARM-discovery From 1e7eea202bbabf42649ff1ee0ae540a620af71f4 Mon Sep 17 00:00:00 2001 From: Claude Date: Sat, 30 May 2026 17:56:46 +0000 Subject: [PATCH 3/7] board(status): sync adjacent D-ARM rows to D-ARM-13 transcode + review state MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - D-ARM-9 (Aerial+ IPC client) → Superseded by D-ARM-13: the native in-process transcode replaces the Python IPC client; the determinism-boundary rationale is met in-process via seed + feature-gate + exclude. - D-ARM-4 (translator) → Partially shipped (branch): arm_to_nars / NarsTruth / CandidateTriple / FeedProjector landed inside D-ARM-13; remaining = real Odoo projector + contract homing. - D-ARM-7 (Jirak) → Queued — HARD PREREQUISITE: ISSUE ARM-JIRAK-FLOOR; must land before D-ARM-5 wires the proposer to a live SpoStore. - D-ARM-2 (Proposer/CandidateRule carrier) → note local mirrors shipped in D-ARM-13; TD-ARM-CARRIER-FORK; field set diverges (n:u32 vs WindowMetadata). https://claude.ai/code/session_012SorR8UbtEvYmbX8cXftj7 --- .claude/board/STATUS_BOARD.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/.claude/board/STATUS_BOARD.md b/.claude/board/STATUS_BOARD.md index cf389d0a..f126c24a 100644 --- a/.claude/board/STATUS_BOARD.md +++ b/.claude/board/STATUS_BOARD.md @@ -622,14 +622,14 @@ The missing UPSTREAM discovery leg. Today's proposers (curated L-docs + AST-extr | D-id | Title | Crate | Lines | Conf | Status | Notes | |---|---|---|---|---|---|---| | D-ARM-1 | `ProvenanceTier::{Curated,Extracted,ArmDiscovered,Ratified,Conjecture}` enum + ordering | lance-graph-contract | 50 | HIGH | **Queued** | blocks all other D-ARM-*; additive | -| D-ARM-2 | `Proposer` trait + `CandidateRule` carrier + `WindowMetadata` | lance-graph-contract | 100 | HIGH | **Queued** | blocks D-ARM-3, D-ARM-9 | +| D-ARM-2 | `Proposer` trait + `CandidateRule` carrier + `WindowMetadata` | lance-graph-contract | 100 | HIGH | **Queued** | blocks D-ARM-3, D-ARM-9. D-ARM-13 shipped **local mirrors** (`rule::{CandidateRule, Proposer, Item}`) ahead of this — see **TD-ARM-CARRIER-FORK**: re-export via `pub use` when this lands (firewall allows path-dep on zero-dep contract). Field set diverges — local carries bare `n: u32`, this plans `WindowMetadata`; reconcile (recommend `n: u32`) so the shape matches. | | D-ARM-3 | Pair-stats proposer (default trunk, deterministic, k² pair counters per window) | lance-graph-arm-discovery::proposer::pair_stats | 400 | HIGH | **Queued** | depends on D-ARM-1/2/7; blocks D-ARM-12 | -| D-ARM-4 | ARM-truth → NARS-truth translator + Odoo `FeedProjector` impl | lance-graph-arm-discovery::translator | 200 | HIGH | **Queued** | depends on D-ARM-1/2 | +| D-ARM-4 | ARM-truth → NARS-truth translator + Odoo `FeedProjector` impl | lance-graph-arm-discovery::translator | 200 | HIGH | **Partially shipped (branch)** | The translator substance landed early inside D-ARM-13: `translator::{arm_to_nars, NarsTruth, CandidateTriple, FeedProjector}` (verbatim paper §2/§3.3 mapping, 35/35 tests). REMAINING: the real Odoo `FeedProjector` (currently a `DebugProjector` stub emitting `implies`) + contract homing on D-ARM-1/2. Depends on D-ARM-1/2. | | D-ARM-5 | Hypothesis test: SpoStore round-trip, NARS revision, contradiction commit per The Click | lance-graph-arm-discovery::hypothesis | 350 | MED | **Queued** | depends on D-ARM-4; verifies `spo::truth::Contradiction` primitive exists | | D-ARM-6 | `RatificationQueue` ring buffer + corrections-to-#434 spec PR (`discovery_arc D=8`, `discovery_origin u8`) | lance-graph-arm-discovery::queue + #434 spec follow-up | 200 + spec | MED | **Queued** | depends on PR #434 D-MBX-A3 landing | -| D-ARM-7 | Jirak-2016 weak-dependence significance thresholds (mandatory Stage A floor) | lance-graph-arm-discovery::jirak | 150 | HIGH | **Queued** | blocks D-ARM-3; cites I-NOISE-FLOOR-JIRAK | +| D-ARM-7 | Jirak-2016 weak-dependence significance thresholds (mandatory Stage A floor) | lance-graph-arm-discovery::jirak | 150 | HIGH | **Queued — HARD PREREQUISITE** | blocks D-ARM-3; cites I-NOISE-FLOOR-JIRAK. **ISSUE ARM-JIRAK-FLOOR (2026-05-30, 3-savant review):** D-ARM-13 ships the Aerial proposer with NO Jirak floor (classical `min_support`/`min_confidence` only). MUST land before D-ARM-5 wires the proposer to a live `SpoStore`, else the substrate calcifies on thin-but-frequent noise (plan §11.1). | | D-ARM-8 | `Feed` + `FeedProjector` + window-size config + Odoo `account.move` projector example | lance-graph-arm-discovery::feed | 250 | MED | **Queued** | depends on D-ARM-2 | -| D-ARM-9 | Aerial+ IPC client (feature-gated `arm-aerial`, NDJSON over Unix socket) | lance-graph-arm-discovery::proposer::aerial_ipc | 200 | MED | **Queued** | optional; depends on D-ARM-2 | +| D-ARM-9 | Aerial+ IPC client (feature-gated `arm-aerial`, NDJSON over Unix socket) | lance-graph-arm-discovery::proposer::aerial_ipc | 200 | MED | **Superseded by D-ARM-13** | The native in-process Aerial+ transcode (D-ARM-13, branch `claude/jolly-cori-clnf9`) replaces the need for the Python IPC client. The determinism-boundary rationale the IPC was designed for (keep the nondeterministic autoencoder out of the Rust path) is now met in-process via seed (`aerial::Rng`) + `aerial` feature gate + workspace `exclude`. Keep this row ONLY if a Python-only Aerial variant is later required; otherwise close as Abandoned-by-replacement. | | D-ARM-10 | `op_emitter::bucket_corpus` ratification filter (`confidence ≥ Ratified`) + 2 tests | lance-graph-ontology::op_emitter | 30 | HIGH | **Queued** | depends on D-ARM-1 | | D-ARM-11 | `style_recipe.rs` rule 8 — ArmDiscovered backing adds `DAtom::Compute` weight 2 (provisional) | lance-graph-ontology::style_recipe | 80 | MED | **Queued** | depends on D-ARM-1 | | D-ARM-12 | End-to-end pipeline test + bench (synthetic Odoo feed → all 5 stages → council micro-batch) | lance-graph-arm-discovery::tests + benches | 400 | MED | **Queued** | depends on Waves 1-6; informs OQ-ARM-2 + OQ-ARM-7 | From 8681cdfe0a7f809526dbad159b1650bec38d867a Mon Sep 17 00:00:00 2001 From: Claude Date: Sat, 30 May 2026 18:45:19 +0000 Subject: [PATCH 4/7] =?UTF-8?q?refactor(arm-discovery):=20de-float=20?= =?UTF-8?q?=E2=80=94=20autoencoder=20=E2=86=92=20deterministic=20codebook-?= =?UTF-8?q?probe=20(palette256)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit The v1 D-ARM-13 transcode reproduced Aerial+ literally — an f32 denoising autoencoder. That was a substrate regression: this stack addresses by exact codebook CAM, never float similarity (faiss-homology-cam-pq.md; I-VSA-IDENTITIES). Aerial+'s reconstruction probe ("mark the antecedent, read off the high-probability consequents") is mechanically a nearest-neighbour query, which the palette256 distance table answers exactly and in integers at rho=0.9973 vs cosine. So the float net was a slow, seed-dependent approximation of a lookup the substrate already performs. Changes: - DELETE aerial/autoencoder.rs + aerial/rng.rs (the f32 net + its seed) - ADD aerial/codebook.rs: zero-dep CodebookDistance trait + MatrixDistance reference impl (real impl = bgz17::PaletteDistanceTable / BLASGraph splat top-k / HDR-popcount, injected consumer-side) - aerial/extract.rs: codebook top-k within theta + integer data confirmation - rule.rs: integer evidence counts (cooccur/antecedent_count/window) + ppm cross-multiply gates (no f32 in the decision path) - translator.rs: TruthU8 (= CausalEdge64 confidence_u8 + i4 mantissa); f32 NarsTruth kept only as the downstream-contract edge - encode.rs: integer-only (dropped the one-hot f32 vector, bin(), and the f32 support/confidence helpers) Verified: 28/28 tests, clippy -D warnings clean, zero f32 in the aerial/ discovery path (audit). Float survives only at the TruthValue/Triple serialization edge (those downstream contracts are themselves f32). Structural payoff: float was the only nondeterminism. Removing it makes the probe bitwise-deterministic, so it joins the deterministic trunk beside pair-stats (D-ARM-3); the nondeterminism firewall and D-ARM-9 (Python-IPC isolation) are moot. EPIPHANIES E-ARM-PROBE-IS-CODEBOOK-TOPK. https://claude.ai/code/session_012SorR8UbtEvYmbX8cXftj7 --- .claude/board/AGENT_LOG.md | 12 + .claude/board/EPIPHANIES.md | 16 + .claude/board/STATUS_BOARD.md | 2 +- crates/lance-graph-arm-discovery/Cargo.toml | 23 +- crates/lance-graph-arm-discovery/README.md | 109 +++--- .../src/aerial/autoencoder.rs | 348 ------------------ .../src/aerial/codebook.rs | 125 +++++++ .../src/aerial/extract.rs | 314 ++++++++-------- .../src/aerial/mod.rs | 216 ++++------- .../src/aerial/rng.rs | 125 ------- .../lance-graph-arm-discovery/src/encode.rs | 132 ++----- crates/lance-graph-arm-discovery/src/lib.rs | 95 +++-- crates/lance-graph-arm-discovery/src/rule.rs | 201 +++++----- .../src/translator.rs | 183 ++++----- 14 files changed, 728 insertions(+), 1173 deletions(-) delete mode 100644 crates/lance-graph-arm-discovery/src/aerial/autoencoder.rs create mode 100644 crates/lance-graph-arm-discovery/src/aerial/codebook.rs delete mode 100644 crates/lance-graph-arm-discovery/src/aerial/rng.rs diff --git a/.claude/board/AGENT_LOG.md b/.claude/board/AGENT_LOG.md index cf25ca5c..91baae92 100644 --- a/.claude/board/AGENT_LOG.md +++ b/.claude/board/AGENT_LOG.md @@ -1,3 +1,15 @@ +## [Main thread / Opus] D-ARM-13 de-float — autoencoder → deterministic codebook-probe (palette256) + +**Branch:** claude/jolly-cori-clnf9 | **Files:** +- `crates/lance-graph-arm-discovery/` — DELETED `aerial/{autoencoder,rng}.rs`; ADDED `aerial/codebook.rs` (`CodebookDistance` trait + `MatrixDistance`); rewrote `aerial/{extract,mod}.rs` (codebook probe), `rule.rs` (integer counts + ppm gates), `translator.rs` (`TruthU8` + f32 edge), `encode.rs` (integer-only, dropped one-hot/`bin`/f32 helpers), `lib.rs`, `README.md`, `Cargo.toml` (dropped `aerial` feature) +- `.claude/board/`: EPIPHANIES (E-ARM-PROBE-IS-CODEBOOK-TOPK), STATUS_BOARD (D-ARM-13 row), AGENT_LOG + +**Cargo:** `cargo test --manifest-path …` → **28/28**; `cargo clippy … -D warnings` → clean; float audit → **zero f32 in `aerial/` discovery path**. + +**Outcome:** DONE. User directive: "neither cam_pq nor any crate uses (or should) float … all is deterministic [a,b] codebook distance, ρ=0.9973 spearman." Conceded — the v1 transcode's `f32` denoising autoencoder was a substrate regression. Replaced it with an integer **codebook-probe** backend: Aerial+'s reconstruction probe is mechanically a nearest-neighbour query, which the **palette256 distance table** answers exactly at ρ=0.9973 vs cosine. The oracle is injected via a zero-dep `CodebookDistance` trait (real impl = `bgz17::PaletteDistanceTable` / BLASGraph splat top-k / HDR-popcount, consumer-side; `MatrixDistance` in tests) so the crate stays standalone. Discovery path is now all integers (codebook distance `u32`, evidence counts `u32`, ppm gates); truth is `TruthU8` (= CausalEdge64 `confidence_u8` + i4 mantissa); the only residual f32 is the `TruthValue`/`Triple` serialization edge (those downstream contracts are themselves f32). Structural payoff: float was the only nondeterminism, so removing it makes the probe bitwise-deterministic ⇒ it joins the deterministic trunk; the nondeterminism firewall and D-ARM-9 (Python-IPC isolation) are moot; the seeded-reproducibility caveat closes. See EPIPHANIES E-ARM-PROBE-IS-CODEBOOK-TOPK. PR #436 updated. + +--- + ## [Main thread / Opus + 3 savant agents] D-ARM-13 brutal review (council) + honesty revisions **Branch:** claude/jolly-cori-clnf9 | **Agents:** 3 background Opus savants (brutally-honest-tester, iron-rule-savant, dto-soa-savant) — the Stage-D ratification ensemble applied to the ARM code. **Files:** diff --git a/.claude/board/EPIPHANIES.md b/.claude/board/EPIPHANIES.md index a1ef6493..0a6ac034 100644 --- a/.claude/board/EPIPHANIES.md +++ b/.claude/board/EPIPHANIES.md @@ -1,3 +1,19 @@ +## 2026-05-30 — E-ARM-PROBE-IS-CODEBOOK-TOPK — Aerial+'s reconstruction probe IS a codebook top-k; the autoencoder was a float approximation of a lookup the substrate already does exactly; de-floating it dissolves the determinism firewall + +**Status:** FINDING (de-float shipped on `claude/jolly-cori-clnf9`, 28/28 tests, zero f32 in the discovery path). User-directed: "neither cam_pq nor any other crate uses (or should) float … all is deterministic [a,b] codebook distance, ρ=0.9973 spearman." + +The first D-ARM-13 transcode reproduced Aerial+ literally — an `f32` denoising autoencoder. That was a substrate regression: this stack addresses by **exact codebook CAM, never float similarity** (`faiss-homology-cam-pq.md`; `I-VSA-IDENTITIES` — CAM-PQ codes are for *search*, integer, never superposed as float). The autoencoder's reconstruction probe ("mark the antecedent, read off the high-probability consequents") is, mechanically, a **nearest-neighbour query** — and the **palette256 distance table** answers that *exactly and in integers* at ρ=0.9973 vs cosine. So the float net was a slow, seed-dependent approximation of a lookup the substrate already performs. + +The de-float (codebook-probe backend): +- autoencoder (`f32` weights) → injected integer `CodebookDistance` oracle (palette256; zero-dep trait, real impl is `bgz17::PaletteDistanceTable` / BLASGraph splat top-k / HDR-popcount); +- reconstruction probe → codebook top-k from the antecedent within `θ` (integer); +- softmax ranking → integer distance ranking; support/confidence → integer counts + ppm cross-multiply gates; +- `(f,c)` `f32` → `TruthU8` (= CausalEdge64 `confidence_u8` + i4 mantissa); seeded RNG → deleted. + +**The payoff is structural, not cosmetic.** Float was the *only* source of nondeterminism, so removing it dissolves three things at once: (1) the "seeded ≠ bitwise-deterministic" caveat closes — the probe is bitwise-identical on every target; (2) Aerial is no longer a *nondeterministic fan-in* that must hide behind the ratification gate / out of the compile path — it joins the **deterministic trunk** beside pair-stats (D-ARM-3); (3) **D-ARM-9** (Python-IPC to isolate nondeterminism) is fully moot — there is no nondeterminism to isolate. The ratification council still governs *promotion to the SPO store*, but the firewall is now about ratification, not float. General rule this crystallises: **a "learned" similarity that the codebook reproduces at ρ≈1.0 should never be reached for as float — the codebook IS the learned-once model, frozen and integer.** Code: `crates/lance-graph-arm-discovery/src/aerial/{codebook,extract,mod}.rs`. Cross-ref: `faiss-homology-cam-pq.md`, `cognitive-risc-classes.md` (CAM-not-ANN), `I-VSA-IDENTITIES`, the prior float transcode's review (`.claude/board/reviews/`). + +--- + ## 2026-05-30 — E-DISCOVERY-CODEGEN-BRACKET-1 (realised) — the Aerial+ transcode is the runtime-data frontend of a bracket whose substrate + codegen legs ALREADY EXIST in the ruff fork; the ruff SPO predicate vocabulary is the only missing seam **Status:** FINDING (type-level, grounded in source read 2026-05-30) + CONJECTURE (the three D-ARM-SYN wiring deliverables). Author-stated; the three wirings are council-gated. diff --git a/.claude/board/STATUS_BOARD.md b/.claude/board/STATUS_BOARD.md index f126c24a..f12a45e3 100644 --- a/.claude/board/STATUS_BOARD.md +++ b/.claude/board/STATUS_BOARD.md @@ -633,7 +633,7 @@ The missing UPSTREAM discovery leg. Today's proposers (curated L-docs + AST-extr | D-ARM-10 | `op_emitter::bucket_corpus` ratification filter (`confidence ≥ Ratified`) + 2 tests | lance-graph-ontology::op_emitter | 30 | HIGH | **Queued** | depends on D-ARM-1 | | D-ARM-11 | `style_recipe.rs` rule 8 — ArmDiscovered backing adds `DAtom::Compute` weight 2 (provisional) | lance-graph-ontology::style_recipe | 80 | MED | **Queued** | depends on D-ARM-1 | | D-ARM-12 | End-to-end pipeline test + bench (synthetic Odoo feed → all 5 stages → council micro-batch) | lance-graph-arm-discovery::tests + benches | 400 | MED | **Queued** | depends on Waves 1-6; informs OQ-ARM-2 + OQ-ARM-7 | -| D-ARM-13 | **Aerial+ Rust transcode** — denoising autoencoder (softmax/feature + CE, paper §3.2) + Algorithm 1 reconstruction-probe rule extraction (§3.3) + seeded `Rng` + `AerialProposer` impl of `Proposer`. User-directed port that plan §14 had deferred ("autoencoder stays in Python"); supersedes that deferral. | lance-graph-arm-discovery::aerial | ~1.2K | HIGH | **Shipped (branch)** | branch `claude/jolly-cori-clnf9`; standalone zero-dep crate (excluded); 35/35 tests, clippy `-D warnings` clean. Keeps the nondeterministic AE OUT of the deterministic compile path; output is a *proposal* gated by Stage D. | +| D-ARM-13 | **Aerial+ Rust transcode — deterministic codebook-probe backend** (float-free). The paper's `f32` denoising autoencoder is REPLACED by an integer `CodebookDistance` oracle (palette256 distance, ρ=0.9973 vs cosine): the reconstruction probe is a codebook top-k, not a softmax over float weights. Integer evidence counts + ppm gates + `TruthU8` (= CausalEdge64 wire). `AerialProposer` impl of `Proposer`. | lance-graph-arm-discovery::aerial | ~1.0K | HIGH | **Shipped (branch)** | branch `claude/jolly-cori-clnf9`; standalone zero-dep crate (excluded); 28/28 tests, clippy `-D warnings` clean; **zero f32 in the discovery path** (audit), float only at the `TruthValue`/`Triple` serialization edge. Bitwise-deterministic ⇒ joins the trunk; the nondeterminism firewall + D-ARM-9 IPC rationale are moot. v1 (autoencoder) superseded per the user's no-float directive. | | D-ARM-SYN-1 | Add `Implies`/`CoOccursWith` to `ruff_spo_triplet::Predicate` closed vocabulary (+ `Provenance` tier) so ARM rules load through the same `parse_triples` ndjson path as the static extractor | ruff/ruff_spo_triplet | 40 | MED | **Queued** | council-gated (deliberate ontology change); blocks SYN-2; see `.claude/knowledge/aerial-arm-ruff-spo-codegen-synergies.md` §1 | | D-ARM-SYN-2 | `CandidateRule → ruff_spo_triplet::ModelGraph` adapter so the Aerial runtime-data leg joins the `ruff_python_dto_check` static-AST leg in one graph before `expand()` | lance-graph-arm-discovery + ruff_spo_triplet | 120 | MED | **Queued** | depends on SYN-1; synergy doc §2 | | D-ARM-SYN-3 | Calibrate `ProvenanceTier::ArmDiscovered` `(f,c)` below the `op_emitter` ratification gate + below static `Inferred (0.85,0.75)` so un-ratified ARM truth is council-visible but codegen-filtered | lance-graph-contract + lance-graph-ontology::op_emitter | 30 | MED | **Queued** | depends on D-ARM-1 + SYN-1; synergy doc §3/§4 | diff --git a/crates/lance-graph-arm-discovery/Cargo.toml b/crates/lance-graph-arm-discovery/Cargo.toml index ad1c15a6..bd8fae30 100644 --- a/crates/lance-graph-arm-discovery/Cargo.toml +++ b/crates/lance-graph-arm-discovery/Cargo.toml @@ -5,29 +5,22 @@ name = "lance-graph-arm-discovery" version = "0.1.0" edition = "2021" -description = "Streaming association-rule discovery (Aerial+ transcode) → NARS-truth SPO candidates" +description = "Deterministic codebook-probe association-rule discovery (Aerial+ transcode) → NARS-truth SPO candidates" license = "Apache-2.0" # Standalone, zero-dependency crate — EXCLUDED from the workspace (see root -# Cargo.toml `exclude`), exactly like bgz17 / deepnsm / bgz-tensor. It is -# verified independently with: +# Cargo.toml `exclude`), like bgz17 / deepnsm / bgz-tensor. Verified with: # # cargo test --manifest-path crates/lance-graph-arm-discovery/Cargo.toml # -# This keeps the nondeterministic Aerial+ autoencoder OUT of the deterministic -# lance-graph compile path (the determinism-boundary doctrine of -# `streaming-arm-nars-discovery-v1.md`). When D-ARM-1/D-ARM-2 land the shared -# `lance-graph-contract` carriers, this crate adopts them by path dep; the -# local `rule`/`translator` types are the seam until then. +# The discovery path is float-free and bitwise-deterministic (integer codebook +# distance, integer evidence counts). The similarity oracle is injected through +# the zero-dep `aerial::CodebookDistance` trait, so the real distance table +# (`bgz17::PaletteDistanceTable`, the BLASGraph splat top-k, or an HDR-popcount +# Hamming primitive) lives on the consumer side and this crate stays standalone. [dependencies] -# none — std only (deterministic, offline-buildable, no neural framework). - -[features] -default = ["aerial"] -# The Aerial+ neurosymbolic proposer. Gated so a deployment can compile the -# carriers (`rule`, `translator`, `ndjson`) without the autoencoder. -aerial = [] +# none — std only. [lib] doctest = true diff --git a/crates/lance-graph-arm-discovery/README.md b/crates/lance-graph-arm-discovery/README.md index cf524f99..a50b11d6 100644 --- a/crates/lance-graph-arm-discovery/README.md +++ b/crates/lance-graph-arm-discovery/README.md @@ -6,53 +6,67 @@ SPDX-FileCopyrightText: Copyright The Lance Authors # lance-graph-arm-discovery Rust transcode of **Aerial+** — neurosymbolic association-rule mining -(Karabulut, Groth, Degeler, *Neurosymbolic Association Rule Mining from -Tabular Data*, arXiv 2504.19354v1). The **upstream proposer leg** of -`streaming-arm-nars-discovery-v1.md`: it mines `(X → Y)` rules from tabular -runtime data and lifts each into a NARS-truth SPO candidate emitted in the -**same ndjson shape** (`{s,p,o,f,c}`) as the static `ruff_spo_triplet` -extractor. The lance-graph `parse_triples` loader consumes it directly; the -`ruff` `from_ndjson` loader accepts it only after its closed predicate -vocabulary gains an implication relation (D-ARM-SYN-1). +(Karabulut, Groth, Degeler, arXiv 2504.19354v1) — with the paper's `f32` +autoencoder **replaced by an integer codebook distance oracle**. The +**upstream proposer leg** of `streaming-arm-nars-discovery-v1.md`: it mines +`(X → Y)` rules from tabular runtime data and lifts each into a NARS-truth SPO +candidate emitted in the `{s,p,o,f,c}` shape the SPO store loader reads. ```text - rows ─► one-hot ─► denoising autoencoder ─► Algorithm 1 probe ─► CandidateRule - (softmax/feature, CE) (reconstruction) │ - ▼ - arm_to_nars: (support, confidence, n) → NARS (f, c) - ▼ - {"s","p","o","f","c"} ndjson ─► SPO store loader + rows ─► codebook items ─► codebook probe (nearest consequents within θ) + (integer CodebookDistance oracle = palette256) + ─► confirm on data: integer support/confidence counts + ─► CandidateRule (integer evidence) + ─► arm_to_truth_u8 → TruthU8 (= CausalEdge64 wire) + ─► {s,p,o,f,c} ndjson ─► SPO store loader ``` -## Why standalone & zero-dep +## Float-free by design -This crate is **excluded from the workspace** (built via `--manifest-path`, -like `bgz17` / `deepnsm`) and depends on `std` only. Two reasons: +The autoencoder was a slow, seed-dependent, `f32` way to approximate a +nearest-neighbour query — the very thing this substrate answers *exactly* and +in integers via the **palette256 distance table** (`[a,b] → u32`, ρ=0.9973 vs +cosine). So Aerial+'s reconstruction probe becomes a **codebook top-k**: -1. **Determinism boundary.** The autoencoder is nondeterministic in general; - keeping it out of the `lance-graph` compile path enforces the plan's - firewall — Aerial+ is a *fan-in* proposer, never the deterministic trunk, - and its output is a *proposal* that the downstream ratification council - must vet before any codegen leg consumes it. -2. **Reproducibility.** Every random source draws from one seeded SplitMix64 - stream (`aerial::Rng`), so the same seed + data + hyper-parameters give - reproducible weights and identical mined rules *on a given target* — - auditable despite being a neural net. (Intra-platform reproducibility, not - bitwise-portable determinism: float `tanh`/`exp` + FMA can differ across - targets.) +| Aerial+ float piece | Deterministic replacement | +| --- | --- | +| denoising autoencoder (`f32` weights) | frozen `CodebookDistance` oracle (palette256) | +| reconstruction probe (forward pass) | codebook top-k from the antecedent, within `θ` | +| softmax ranking | integer distance ranking | +| support/confidence (`f32`) | integer counts; ppm cross-multiply gates | +| `(f,c)` `f32` truth | `TruthU8` = `confidence_u8` + i4 mantissa | +| seeded RNG | **deleted** — codebook lookup is bitwise-exact | + +The only `f32` left is the **serialization edge** (`arm_to_nars`, +`CandidateTriple`, `ndjson`), present solely because the downstream +`spo::truth::TruthValue` and `ruff_spo_triplet::Triple` are themselves `f32`. +Nothing in the discovery path consumes it. + +The similarity oracle is a **zero-dep trait** (`aerial::CodebookDistance`), so +the real distance table — `bgz17::PaletteDistanceTable`, the BLASGraph +Gaussian-splat top-k, or an HDR-popcount Hamming primitive — lives on the +consumer side and this crate stays standalone. `aerial::MatrixDistance` is the +in-crate reference impl. + +## No determinism firewall needed + +The codebook probe is **bitwise-deterministic by construction** (same data + +oracle + `θ` ⇒ identical rules, every target). It can sit in the deterministic +trunk beside pair-stats (D-ARM-3); the ratification council still governs +*promotion to the SPO store*, but no longer because of any nondeterminism here. +The Python-IPC isolation rationale (D-ARM-9) is fully moot. ## Layout -| Module | Stage | Role | -| --- | --- | --- | -| `encode` | — | one-hot `FeatureSpec` + `Dataset` + support/confidence counting | -| `aerial::rng` | A | seeded SplitMix64 PRNG | -| `aerial::autoencoder` | A | under-complete denoising AE (softmax-per-feature, CE, hand backprop) | -| `aerial::extract` | A | Algorithm 1 — reconstruction-probe rule extraction | -| `aerial` (`AerialProposer`) | A | `Proposer` impl: `fit` (train) + `next_batch` (mine) | -| `translator` | B | `arm_to_nars` + `CandidateTriple{s,p,o,f,c}` + `FeedProjector` | -| `ndjson` | emit | `{"s","p","o","f","c"}` lines = the SPO-loader contract | -| `rule` | — | `Item`, `CandidateRule`, `Proposer` trait | +| Module | Stage | Role | +| --- | --- | --- | +| `encode` | — | integer `FeatureSpec` + `Dataset` + `count_matching` | +| `aerial::codebook` | A | `CodebookDistance` trait + `MatrixDistance` reference impl | +| `aerial::extract` | A | codebook-probe rule extraction (Algorithm 1, integer) | +| `aerial` (`AerialProposer`) | A | `Proposer` impl over a data window + oracle | +| `translator` | B | `arm_to_truth_u8` (`TruthU8`) + `f32` edge + `FeedProjector` | +| `ndjson` | emit | `{s,p,o,f,c}` lines = the SPO-loader contract | +| `rule` | — | `Item`, `CandidateRule` (integer counts, ppm gates), `Proposer` | ## Build & test @@ -61,19 +75,10 @@ cargo test --manifest-path crates/lance-graph-arm-discovery/Cargo.toml cargo clippy --manifest-path crates/lance-graph-arm-discovery/Cargo.toml --all-targets -- -D warnings ``` -## Truth mapping (verbatim from the paper) - -- ARM **confidence** = `P(Y|X)` → NARS **frequency** `f` -- ARM **support × n** (evidential mass `m`) → NARS **confidence** `c = m/(m+k)` - -The resulting `(f, c)` is exactly the pair `TruthValue::new(f, c)` and -`ruff_spo_triplet::Triple{f, c}` carry — see -`.claude/knowledge/aerial-arm-ruff-spo-codegen-synergies.md` for the full -synergy map with the `ruff` DTO / SPO / codegen crates. - ## Status -Implements the Aerial+ leg (D-ARM-9) of the plan. The pair-stats deterministic -trunk (D-ARM-3), the `lance-graph-contract` carriers (D-ARM-1/2), and the -hypothesis-test / queue stages (D-ARM-5/6) remain to land; the local `rule` / -`translator` types are the seam until the contract carriers exist. +Implements the Aerial+ leg of the plan as a deterministic codebook backend +(D-ARM-13). Still open: D-ARM-7 Jirak floor (ISSUE ARM-JIRAK-FLOOR — hard +prerequisite before wiring to a live `SpoStore`), the `lance-graph-contract` +carriers (D-ARM-1/2, TD-ARM-CARRIER-FORK), and the real palette256 oracle wiring +(consumer-side `CodebookDistance` impl over `bgz17::PaletteDistanceTable`). diff --git a/crates/lance-graph-arm-discovery/src/aerial/autoencoder.rs b/crates/lance-graph-arm-discovery/src/aerial/autoencoder.rs deleted file mode 100644 index f7b6298e..00000000 --- a/crates/lance-graph-arm-discovery/src/aerial/autoencoder.rs +++ /dev/null @@ -1,348 +0,0 @@ -// SPDX-License-Identifier: Apache-2.0 -// SPDX-FileCopyrightText: Copyright The Lance Authors - -//! The Aerial+ under-complete **denoising autoencoder** (paper §3.2). -//! -//! Architecture (one hidden layer, the paper's minimal form): -//! -//! ```text -//! x (D one-hot slots) ──noise──► x̃ -//! │ encoder h = tanh(We·x̃ + be) (H < D, under-complete) -//! ▼ -//! h (H) -//! │ decoder z = Wd·h + bd -//! ▼ softmax PER FEATURE BLOCK -//! p (D, each block a probability distribution over its categories) -//! ``` -//! -//! Loss is **categorical cross-entropy per block** summed over features — -//! the multi-class form of the paper's BCE-per-feature objective. The -//! softmax+CE pairing gives the clean logit gradient `dz = p − target`, -//! which keeps the hand-written backprop short and correct. -//! -//! Denoising (random input masking) is **load-bearing, not decorative**: it -//! is what forces the bottleneck to predict a feature from the *other* -//! features, which is exactly what the reconstruction probe in -//! [`crate::aerial::extract`] reads back out as an association rule. - -// Dense GEMV / backprop kernels below are clearest as explicit index loops -// over the row-major weight matrices (multiple parallel arrays indexed by the -// same `i`/`j`). Clippy's `enumerate().take().skip()` rewrite would obscure -// the linear-algebra structure, so the lint is allowed module-wide here. -#![allow(clippy::needless_range_loop)] - -use crate::aerial::rng::Rng; -use crate::encode::{Dataset, FeatureSpec}; - -/// A trained (or freshly-initialised) Aerial+ autoencoder. -#[derive(Debug, Clone)] -pub struct AerialAutoencoder { - dim: usize, - hidden: usize, - /// Per-feature `[start, end)` blocks for the softmax. - blocks: Vec<(usize, usize)>, - /// Encoder weights, row-major `[hidden][dim]`. - we: Vec, - /// Encoder bias `[hidden]`. - be: Vec, - /// Decoder weights, row-major `[dim][hidden]`. - wd: Vec, - /// Decoder bias `[dim]`. - bd: Vec, -} - -impl AerialAutoencoder { - /// Initialise an autoencoder for `spec` with `hidden` latent units. - /// Weights are Xavier-scaled draws from the seeded `rng`; biases zero. - #[must_use] - pub fn new(spec: &FeatureSpec, hidden: usize, rng: &mut Rng) -> Self { - let dim = spec.dim(); - assert!(hidden > 0, "hidden dim must be ≥ 1"); - let blocks: Vec<(usize, usize)> = - (0..spec.num_features()).map(|f| spec.block(f)).collect(); - - let enc_scale = (1.0 / dim as f32).sqrt(); - let dec_scale = (1.0 / hidden as f32).sqrt(); - let we = (0..hidden * dim).map(|_| rng.normal() * enc_scale).collect(); - let wd = (0..dim * hidden).map(|_| rng.normal() * dec_scale).collect(); - - Self { - dim, - hidden, - blocks, - we, - be: vec![0.0; hidden], - wd, - bd: vec![0.0; dim], - } - } - - /// Input dimension `D`. - #[must_use] - pub fn dim(&self) -> usize { - self.dim - } - - /// Hidden dimension `H`. - #[must_use] - pub fn hidden(&self) -> usize { - self.hidden - } - - /// Forward pass on an arbitrary input vector (one-hot row OR a probe - /// vector with uniform blocks). Returns `(hidden, probs)` where `probs` - /// is per-block softmax over the decoder logits. - #[must_use] - pub fn forward(&self, input: &[f32]) -> (Vec, Vec) { - debug_assert_eq!(input.len(), self.dim); - // Encoder: h = tanh(We·x + be) - let mut h = vec![0.0f32; self.hidden]; - for j in 0..self.hidden { - let mut acc = self.be[j]; - let base = j * self.dim; - for i in 0..self.dim { - acc += self.we[base + i] * input[i]; - } - h[j] = acc.tanh(); - } - // Decoder logits: z = Wd·h + bd - let mut z = vec![0.0f32; self.dim]; - for i in 0..self.dim { - let mut acc = self.bd[i]; - let base = i * self.hidden; - for j in 0..self.hidden { - acc += self.wd[base + j] * h[j]; - } - z[i] = acc; - } - // Per-block softmax. - let mut p = z; - for &(s, e) in &self.blocks { - softmax_in_place(&mut p[s..e]); - } - (h, p) - } - - /// Reconstruct per-block probabilities for an input (forward, probs only). - #[must_use] - pub fn reconstruct(&self, input: &[f32]) -> Vec { - self.forward(input).1 - } - - /// Mean cross-entropy loss over a dataset's clean one-hot encodings. - /// Used as a training-progress signal in tests and diagnostics. - #[must_use] - pub fn mean_loss(&self, data: &Dataset) -> f32 { - if data.is_empty() { - return 0.0; - } - let mut total = 0.0f32; - for row in &data.rows { - let x = data.spec.encode(row); - let p = self.reconstruct(&x); - total += cross_entropy(&x, &p, &self.blocks); - } - total / data.len() as f32 - } - - /// Train in place with denoising SGD. Returns the final epoch's mean loss. - /// - /// One pass per epoch over a shuffled index set; per-sample weight update. - /// Each epoch corrupts inputs afresh (mask each slot with probability - /// `noise`) while the reconstruction target stays the clean one-hot row. - pub fn train( - &mut self, - data: &Dataset, - epochs: usize, - learning_rate: f32, - noise: f32, - rng: &mut Rng, - ) -> f32 { - if data.is_empty() { - return 0.0; - } - // Pre-encode clean targets once. - let targets: Vec> = data.rows.iter().map(|r| data.spec.encode(r)).collect(); - let mut order: Vec = (0..targets.len()).collect(); - let mut last = 0.0f32; - - for _epoch in 0..epochs { - rng.shuffle(&mut order); - let mut epoch_loss = 0.0f32; - for &idx in &order { - let target = &targets[idx]; - // Corrupt: copy then mask slots to 0 with probability `noise`. - let mut input = target.clone(); - if noise > 0.0 { - for v in &mut input { - if rng.bernoulli(noise) { - *v = 0.0; - } - } - } - epoch_loss += self.train_step(&input, target, learning_rate); - } - last = epoch_loss / targets.len() as f32; - } - last - } - - /// One forward+backward+update on a single (corrupted input, clean - /// target) pair. Returns the cross-entropy loss before the update. - fn train_step(&mut self, input: &[f32], target: &[f32], lr: f32) -> f32 { - // Forward (recompute hidden so we can backprop through it). - let (h, p) = self.forward(input); - let loss = cross_entropy(target, &p, &self.blocks); - - // Output gradient: dz = p − target (softmax + CE). - let mut dz = vec![0.0f32; self.dim]; - for i in 0..self.dim { - dz[i] = p[i] - target[i]; - } - - // Backprop into hidden: dh[j] = Σ_i Wd[i][j]·dz[i] - let mut dh = vec![0.0f32; self.hidden]; - for i in 0..self.dim { - let base = i * self.hidden; - let dzi = dz[i]; - for j in 0..self.hidden { - dh[j] += self.wd[base + j] * dzi; - } - } - - // Decoder update: Wd[i][j] -= lr·dz[i]·h[j]; bd[i] -= lr·dz[i] - for i in 0..self.dim { - let base = i * self.hidden; - let dzi = dz[i]; - for j in 0..self.hidden { - self.wd[base + j] -= lr * dzi * h[j]; - } - self.bd[i] -= lr * dzi; - } - - // Through tanh: da[j] = dh[j]·(1 − h[j]²) - // Encoder update: We[j][i] -= lr·da[j]·input[i]; be[j] -= lr·da[j] - for j in 0..self.hidden { - let da = dh[j] * (1.0 - h[j] * h[j]); - let base = j * self.dim; - for i in 0..self.dim { - self.we[base + i] -= lr * da * input[i]; - } - self.be[j] -= lr * da; - } - - loss - } -} - -/// Numerically-stable softmax in place over a slice (one feature block). -fn softmax_in_place(block: &mut [f32]) { - if block.is_empty() { - return; - } - let max = block.iter().copied().fold(f32::NEG_INFINITY, f32::max); - let mut sum = 0.0f32; - for v in block.iter_mut() { - *v = (*v - max).exp(); - sum += *v; - } - if sum > 0.0 { - for v in block.iter_mut() { - *v /= sum; - } - } -} - -/// Cross-entropy `−Σ target·ln(p)` summed over all blocks. `target` is the -/// clean one-hot vector; `p` is the per-block softmax output. -fn cross_entropy(target: &[f32], p: &[f32], blocks: &[(usize, usize)]) -> f32 { - let mut loss = 0.0f32; - for &(s, e) in blocks { - for i in s..e { - if target[i] > 0.0 { - loss -= target[i] * (p[i].max(1e-9)).ln(); - } - } - } - loss -} - -#[cfg(test)] -mod tests { - use super::*; - - fn corr_dataset(n: usize, seed: u64) -> Dataset { - // feature1 == feature0 (deterministic dependency); feature2 random. - let spec = FeatureSpec::new(vec![2, 2, 2]); - let mut rng = Rng::new(seed); - let rows = (0..n) - .map(|_| { - let a = (rng.next_u64() % 2) as u32; - let c = (rng.next_u64() % 2) as u32; - vec![a, a, c] - }) - .collect(); - Dataset::new(spec, rows) - } - - #[test] - fn forward_blocks_are_probability_distributions() { - let spec = FeatureSpec::new(vec![2, 3]); - let mut rng = Rng::new(1); - let ae = AerialAutoencoder::new(&spec, 4, &mut rng); - let (_, p) = ae.forward(&spec.encode(&[0, 2])); - // each block sums to ~1 - assert!((p[0..2].iter().sum::() - 1.0).abs() < 1e-5); - assert!((p[2..5].iter().sum::() - 1.0).abs() < 1e-5); - } - - #[test] - fn training_reduces_loss() { - let data = corr_dataset(300, 7); - let mut rng = Rng::new(42); - let mut ae = AerialAutoencoder::new(&data.spec, 4, &mut rng); - let before = ae.mean_loss(&data); - ae.train(&data, 400, 0.1, 0.3, &mut rng); - let after = ae.mean_loss(&data); - assert!( - after < before * 0.7, - "loss should drop substantially: before={before}, after={after}" - ); - } - - #[test] - fn denoising_ae_learns_cross_feature_dependency() { - // After training, probing "feature0 = 0" (one-hot) with the rest of - // the input left at uniform should reconstruct feature1 = 0 with - // high probability — that IS the learned A→B dependency. - let data = corr_dataset(400, 11); - let mut rng = Rng::new(42); - let mut ae = AerialAutoencoder::new(&data.spec, 4, &mut rng); - ae.train(&data, 600, 0.1, 0.3, &mut rng); - - // Probe vector: feature0 block = one-hot(0), features 1 and 2 uniform. - let mut probe = vec![0.5f32; data.spec.dim()]; // all uniform (cards=2) - probe[0] = 1.0; // feature0 = cat0 - probe[1] = 0.0; - let p = ae.reconstruct(&probe); - // feature1 block is slots [2,4): expect cat0 (slot 2) dominant. - assert!( - p[2] > 0.6, - "feature1=0 should be reconstructed from feature0=0; p={:?}", - &p[2..4] - ); - assert!(p[2] > p[3], "the correlated category must win"); - } - - #[test] - fn training_is_reproducible_from_seed() { - let data = corr_dataset(200, 5); - let mut ae1 = AerialAutoencoder::new(&data.spec, 4, &mut Rng::new(42)); - let mut ae2 = AerialAutoencoder::new(&data.spec, 4, &mut Rng::new(42)); - let l1 = ae1.train(&data, 100, 0.1, 0.2, &mut Rng::new(123)); - let l2 = ae2.train(&data, 100, 0.1, 0.2, &mut Rng::new(123)); - assert_eq!(l1, l2, "same seeds ⇒ identical training"); - assert_eq!(ae1.we, ae2.we); - assert_eq!(ae1.wd, ae2.wd); - } -} diff --git a/crates/lance-graph-arm-discovery/src/aerial/codebook.rs b/crates/lance-graph-arm-discovery/src/aerial/codebook.rs new file mode 100644 index 00000000..09732e22 --- /dev/null +++ b/crates/lance-graph-arm-discovery/src/aerial/codebook.rs @@ -0,0 +1,125 @@ +// SPDX-License-Identifier: Apache-2.0 +// SPDX-FileCopyrightText: Copyright The Lance Authors + +//! The deterministic similarity oracle — `CodebookDistance`. +//! +//! This is the float-free replacement for Aerial+'s autoencoder. The paper's +//! reconstruction probe ("mark the antecedent, read off the high-probability +//! consequents") is, mechanically, a **nearest-neighbour query in the learned +//! co-occurrence space** — and this substrate already answers that exactly, +//! in integers, via the **palette256 distance table** (`[a,b] → u32`, ρ=0.9973 +//! vs cosine). So the probe ranks consequents by integer codebook distance, +//! never by a softmax over float weights. +//! +//! The oracle is a **trait**, not a concrete table, so this crate stays +//! zero-dep and standalone: the real implementation is +//! `bgz17::PaletteDistanceTable` (or the BLASGraph Gaussian-splat top-k, or an +//! HDR-popcount Hamming primitive) wired in by the consumer. [`MatrixDistance`] +//! is the in-crate reference impl used by tests. +//! +//! Invariant (per `faiss-homology-cam-pq.md`): this distance is **discovery / +//! shape-family only**, never identity addressing. Identity is the exact CAM +//! hash over canonicalized `(s,p,o)`. Same triples, two indexes, never swapped. + +use crate::encode::FeatureSpec; +use crate::rule::Item; + +/// A deterministic, integer distance between two codebook items. Lower means +/// more associated (nearer). No float, no seed — bitwise-identical on every +/// target. +pub trait CodebookDistance { + /// Distance between two items' codebook codes. Lower = nearer. + fn distance(&self, a: Item, b: Item) -> u32; +} + +/// Reference [`CodebookDistance`] over a flat `dim × dim` `u32` table keyed by +/// the [`FeatureSpec`] slot of each item. Zero-dep; the in-crate stand-in for +/// `bgz17::PaletteDistanceTable`. +#[derive(Debug, Clone)] +pub struct MatrixDistance { + dim: usize, + offsets: Vec, + table: Vec, +} + +impl MatrixDistance { + /// Build from a spec and a flat `dim × dim` row-major distance table. + /// + /// # Panics + /// If `table.len() != spec.dim()²`. + #[must_use] + pub fn new(spec: &FeatureSpec, table: Vec) -> Self { + let dim = spec.dim(); + assert_eq!(table.len(), dim * dim, "distance table must be dim × dim"); + let offsets: Vec = (0..spec.num_features()) + .map(|f| spec.block(f).0) + .collect(); + Self { dim, offsets, table } + } + + /// The flat code (slot index) of an item. + #[must_use] + fn code(&self, it: Item) -> usize { + self.offsets[it.feature as usize] + it.category as usize + } +} + +impl CodebookDistance for MatrixDistance { + fn distance(&self, a: Item, b: Item) -> u32 { + self.table[self.code(a) * self.dim + self.code(b)] + } +} + +/// Aggregate the distance from a (multi-item) antecedent to a candidate +/// consequent item: the **nearest** antecedent item wins (min). For a +/// single-item antecedent this is just `distance(a, item)`. +#[must_use] +pub fn antecedent_distance( + oracle: &dyn CodebookDistance, + antecedent: &[Item], + item: Item, +) -> u32 { + antecedent + .iter() + .map(|&a| oracle.distance(a, item)) + .min() + .unwrap_or(u32::MAX) +} + +#[cfg(test)] +mod tests { + use super::*; + + #[test] + fn matrix_distance_is_slot_addressed_and_symmetric_if_built_so() { + let spec = FeatureSpec::new(vec![2, 2]); // dim 4 + // 4×4 table; (f0,c0)=slot0 near (f1,c0)=slot2 (dist 1), far from slot3. + #[rustfmt::skip] + let table = vec![ + 0, 5, 1, 9, + 5, 0, 9, 1, + 1, 9, 0, 5, + 9, 1, 5, 0, + ]; + let d = MatrixDistance::new(&spec, table); + assert_eq!(d.distance(Item::new(0, 0), Item::new(1, 0)), 1); + assert_eq!(d.distance(Item::new(0, 0), Item::new(1, 1)), 9); + assert_eq!(d.distance(Item::new(0, 0), Item::new(0, 0)), 0); + } + + #[test] + fn antecedent_distance_takes_the_nearest_item() { + let spec = FeatureSpec::new(vec![2, 2]); + #[rustfmt::skip] + let table = vec![ + 0, 5, 1, 9, + 5, 0, 9, 1, + 1, 9, 0, 5, + 9, 1, 5, 0, + ]; + let d = MatrixDistance::new(&spec, table); + // antecedent {(f0,c0)=slot0, (f0,c1)=slot1} to (f1,c0)=slot2: min(1, 9) = 1 + let ant = [Item::new(0, 0), Item::new(0, 1)]; + assert_eq!(antecedent_distance(&d, &ant, Item::new(1, 0)), 1); + } +} diff --git a/crates/lance-graph-arm-discovery/src/aerial/extract.rs b/crates/lance-graph-arm-discovery/src/aerial/extract.rs index ebf824c2..db1572b8 100644 --- a/crates/lance-graph-arm-discovery/src/aerial/extract.rs +++ b/crates/lance-graph-arm-discovery/src/aerial/extract.rs @@ -1,65 +1,62 @@ // SPDX-License-Identifier: Apache-2.0 // SPDX-FileCopyrightText: Copyright The Lance Authors -//! Aerial+ **Algorithm 1** — rule extraction by reconstruction probing -//! (paper §3.3). +//! Aerial+ rule extraction — the **codebook-probe** backend (float-free). //! -//! The idea, verbatim: to test antecedent `X`, build an input where `X`'s -//! categories are marked at probability 1 and **every other feature is left -//! uniform** ("unknown"); forward-pass the trained autoencoder; then +//! Aerial+'s reconstruction probe (paper §3.3) marks an antecedent, leaves +//! the rest "unknown", and reads off the consequents the network reconstructs +//! with high probability. Mechanically that is: *which other items are +//! nearest to the antecedent in the learned co-occurrence space?* This backend +//! answers it deterministically with an integer [`CodebookDistance`] oracle +//! (the palette256 table) instead of a float autoencoder: //! -//! 1. **antecedent test** — every marked category must still come back with -//! probability `≥ τ_a` (`ant_similarity`); a self-inconsistent antecedent -//! the network cannot hold is rejected, and -//! 2. **consequent test** — for every *other* feature, if its top category -//! `Y` reconstructs with probability `≥ τ_c` (`cons_similarity`), the rule -//! `X → Y` is proposed. +//! 1. **probe** — for every feature not in the antecedent, take the category +//! whose codebook distance to the antecedent is minimal; if it is within +//! `theta`, propose it as a consequent (the τ_c analogue, integer); +//! 2. **confirm** — measure `support`/`confidence` on the actual data as +//! integer counts and gate by the classical ARM floors (ppm). //! -//! Each proposed rule's `support`/`confidence` are then measured **on the -//! actual data** (`encode::Dataset`) and gated by the classical ARM floors. -//! This is the double gate that makes Aerial+ robust: the network *proposes* -//! a rule from its learned conditional structure; the data *confirms* it. -//! An independent feature whose marginal happens to clear `τ_c` is dropped -//! at the confidence floor because `P(Y|X) ≈ P(Y)` for independent `Y`. +//! Double gate, exactly as the paper: the codebook *proposes* (near pairs), +//! the data *confirms* (co-occurrence). An independent feature whose nearest +//! category clears `theta` is dropped at the confidence floor because +//! `P(Y|X) ≈ P(Y)` for independent `Y`. No softmax, no seed, no `f32`. -use crate::aerial::autoencoder::AerialAutoencoder; +use crate::aerial::codebook::{antecedent_distance, CodebookDistance}; use crate::encode::Dataset; use crate::rule::{CandidateRule, Item}; -/// Thresholds + bounds for [`extract_rules`]. +/// Thresholds + bounds for [`extract_rules`]. All integer. #[derive(Debug, Clone, Copy)] pub struct ExtractParams { - /// `τ_a` — minimum reconstructed probability of each marked antecedent - /// category for the antecedent to be considered coherent. - pub ant_similarity: f32, - /// `τ_c` — minimum reconstructed probability of a consequent category. - pub cons_similarity: f32, - /// Maximum antecedent size `|X|` (paper default 2; pair-stats trunk caps - /// here, the Aerial fan-in can go higher). + /// Maximum codebook distance for a category to be a candidate consequent + /// (the τ_c analogue, in the oracle's integer units — e.g. palette256 + /// distance). `u32::MAX` = no codebook prune (always take the nearest + /// category, let the data gates decide). Tune to your distance scale. + pub theta: u32, + /// Maximum antecedent size `|X|`. pub max_antecedent: usize, - /// Classical minimum support floor, measured on the data. - pub min_support: f32, - /// Classical minimum confidence floor, measured on the data. - pub min_confidence: f32, + /// Classical minimum support floor, parts-per-million. + pub min_support_ppm: u32, + /// Classical minimum confidence floor, parts-per-million. + pub min_confidence_ppm: u32, } impl Default for ExtractParams { fn default() -> Self { Self { - ant_similarity: 0.5, - cons_similarity: 0.6, + theta: u32::MAX, max_antecedent: 2, - min_support: 0.01, - min_confidence: 0.5, + min_support_ppm: 10_000, // 1% + min_confidence_ppm: 500_000, // 50% } } } -/// Run Algorithm 1 against a trained autoencoder and the data it was trained -/// on. Returns deterministically-sorted candidate rules. +/// Run the codebook probe against a distance oracle and the data, returning +/// deterministically-sorted candidate rules. #[must_use] pub fn extract_rules( - ae: &AerialAutoencoder, + oracle: &dyn CodebookDistance, data: &Dataset, params: &ExtractParams, ) -> Vec { @@ -69,13 +66,15 @@ pub fn extract_rules( return Vec::new(); } - // Frequent single items (support floor) — the apriori-style prune that - // bounds the antecedent search, grouped by feature. + // Frequent single items (support floor) — the apriori prune that bounds + // the antecedent search, grouped by feature. Integer ppm. let mut frequent_by_feature: Vec> = vec![Vec::new(); spec.num_features()]; for (f, bucket) in frequent_by_feature.iter_mut().enumerate() { for cat in 0..spec.cardinality(f) { let item = Item::new(f as u32, cat); - if data.support(&[item]) >= params.min_support { + let count = data.count_matching(&[item]); + let support_ppm = ((count as u64 * crate::rule::PPM) / n as u64) as u32; + if support_ppm >= params.min_support_ppm { bucket.push(item); } } @@ -84,17 +83,12 @@ pub fn extract_rules( .filter(|&f| !frequent_by_feature[f].is_empty()) .collect(); - // The neutral probe: every block uniform (= "unknown"). - let neutral = neutral_probe(spec); - let mut rules: Vec = Vec::new(); - let max_ant = params.max_antecedent.max(1); for size in 1..=max_ant { for feature_combo in feature_combinations(&candidate_features, size) { - // Cartesian product of the frequent items of each chosen feature. for antecedent in item_product(&feature_combo, &frequent_by_feature) { - probe_antecedent(ae, spec, &neutral, &antecedent, &feature_combo, data, params, &mut rules); + probe(oracle, data, &antecedent, &feature_combo, params, &mut rules); } } } @@ -108,87 +102,61 @@ pub fn extract_rules( rules } -/// Probe one antecedent and append every confirmed rule it yields. -#[allow(clippy::too_many_arguments)] -fn probe_antecedent( - ae: &AerialAutoencoder, - spec: &crate::encode::FeatureSpec, - neutral: &[f32], +/// Probe one antecedent: for each other feature, take the codebook-nearest +/// category; if within `theta`, confirm on data and emit if it clears the +/// ARM floors. +fn probe( + oracle: &dyn CodebookDistance, + data: &Dataset, antecedent: &[Item], antecedent_features: &[usize], - data: &Dataset, params: &ExtractParams, out: &mut Vec, ) { - // Build the probe: neutral, then mark the antecedent blocks one-hot. - let mut probe = neutral.to_vec(); - for &it in antecedent { - let (s, e) = spec.block(it.feature as usize); - probe[s..e].fill(0.0); - probe[spec.slot(it)] = 1.0; - } - - let p = ae.reconstruct(&probe); - - // Antecedent test: every marked category must survive at ≥ τ_a. - for &it in antecedent { - if p[spec.slot(it)] < params.ant_similarity { - return; - } + let spec = &data.spec; + let n = data.len() as u32; + let antecedent_count = data.count_matching(antecedent); + if antecedent_count == 0 { + return; } - // Consequent test: for each feature NOT in the antecedent, take its top - // category; if it clears τ_c, propose X → (g, w) and confirm on data. - let n = data.len() as u32; for g in 0..spec.num_features() { if antecedent_features.contains(&g) { continue; } - let (s, e) = spec.block(g); - let (mut best_cat, mut best_p) = (0usize, f32::NEG_INFINITY); - for (cat, &prob) in p[s..e].iter().enumerate() { - if prob > best_p { - best_p = prob; + // Codebook-nearest category of feature g to the antecedent. + let (mut best_cat, mut best_dist) = (0u32, u32::MAX); + for cat in 0..spec.cardinality(g) { + let d = antecedent_distance(oracle, antecedent, Item::new(g as u32, cat)); + if d < best_dist { + best_dist = d; best_cat = cat; } } - if best_p < params.cons_similarity { + if best_dist > params.theta { continue; } - let consequent = vec![Item::new(g as u32, best_cat as u32)]; - let support = { - let mut both = antecedent.to_vec(); - both.extend_from_slice(&consequent); - data.support(&both) - }; - let confidence = data.confidence(antecedent, &consequent); + + let consequent = vec![Item::new(g as u32, best_cat)]; + let mut both = antecedent.to_vec(); + both.extend_from_slice(&consequent); + let cooccur = data.count_matching(&both); + let rule = CandidateRule { antecedent: antecedent.to_vec(), consequent, - support, - confidence, - n, + cooccur, + antecedent_count, + window: n, }; - if rule.passes(params.min_support, params.min_confidence) { + if rule.passes(params.min_support_ppm, params.min_confidence_ppm) { out.push(rule); } } } -/// The neutral probe vector: every feature block set to its uniform -/// distribution `1/cardinality`. -fn neutral_probe(spec: &crate::encode::FeatureSpec) -> Vec { - let mut v = vec![0.0f32; spec.dim()]; - for f in 0..spec.num_features() { - let (s, e) = spec.block(f); - let u = 1.0 / (e - s) as f32; - v[s..e].fill(u); - } - v -} - -/// All size-`k` combinations of the given feature indices (order preserved, -/// ascending), produced deterministically. +/// All size-`k` combinations of the given feature indices (ascending, +/// deterministic). fn feature_combinations(features: &[usize], k: usize) -> Vec> { let mut out = Vec::new(); let mut combo = Vec::with_capacity(k); @@ -235,99 +203,111 @@ fn item_product(combo: &[usize], frequent_by_feature: &[Vec]) -> Vec Dataset { - let spec = FeatureSpec::new(vec![2, 2, 2]); - let mut rng = Rng::new(seed); - let rows = (0..n) - .map(|_| { - let a = (rng.next_u64() % 2) as u32; - let b = if rng.bernoulli(noise) { 1 - a } else { a }; - let c = (rng.next_u64() % 2) as u32; + /// 3 features × 2 cats. Codebook places (f*,c0) near each other and + /// (f*,c1) near each other; feature 2 is far from features 0/1 (independent + /// in codebook space). Data plants f0 == f1 with a little noise; f2 random. + fn fixture(noise_every: usize) -> (Dataset, MatrixDistance) { + let spec = FeatureSpec::new(vec![2, 2, 2]); // dim 6 + // slots: f0c0=0 f0c1=1 | f1c0=2 f1c1=3 | f2c0=4 f2c1=5 + // near = 1, mid = 5, far = 50. + let near = 1u32; + let mid = 5u32; + let far = 50u32; + let mut table = vec![0u32; 36]; + let set = |t: &mut Vec, a: usize, b: usize, v: u32| { + t[a * 6 + b] = v; + t[b * 6 + a] = v; + }; + // f0c0(0) ~ f1c0(2) near; f0c1(1) ~ f1c1(3) near; cross pairs mid. + set(&mut table, 0, 2, near); + set(&mut table, 1, 3, near); + set(&mut table, 0, 3, mid); + set(&mut table, 1, 2, mid); + set(&mut table, 0, 1, mid); + set(&mut table, 2, 3, mid); + // feature 2 (slots 4,5) far from everything. + for a in 0..4 { + set(&mut table, a, 4, far); + set(&mut table, a, 5, far); + } + set(&mut table, 4, 5, mid); + + let rows: Vec> = (0..600) + .map(|i| { + let a = (i % 2) as u32; + let b = if noise_every != 0 && i % noise_every == 0 { 1 - a } else { a }; + let c = ((i / 2) % 2) as u32; vec![a, b, c] }) .collect(); - Dataset::new(spec, rows) - } - - #[test] - fn feature_combinations_are_correct() { - assert_eq!( - feature_combinations(&[0, 1, 2], 2), - vec![vec![0, 1], vec![0, 2], vec![1, 2]] - ); - assert_eq!(feature_combinations(&[0, 1, 2], 1), vec![vec![0], vec![1], vec![2]]); - } - - #[test] - fn item_product_enumerates_category_assignments() { - let freq = vec![ - vec![Item::new(0, 0), Item::new(0, 1)], - vec![Item::new(1, 0)], - ]; - let prod = item_product(&[0, 1], &freq); - assert_eq!(prod.len(), 2); - assert!(prod.contains(&vec![Item::new(0, 0), Item::new(1, 0)])); - assert!(prod.contains(&vec![Item::new(0, 1), Item::new(1, 0)])); - } - - #[test] - fn neutral_probe_is_uniform_per_block() { - let spec = FeatureSpec::new(vec![2, 4]); - let v = neutral_probe(&spec); - assert!((v[0] - 0.5).abs() < 1e-6); - assert!((v[2] - 0.25).abs() < 1e-6); - assert!((v[0..2].iter().sum::() - 1.0).abs() < 1e-6); - assert!((v[2..6].iter().sum::() - 1.0).abs() < 1e-6); + (Dataset::new(spec.clone(), rows), MatrixDistance::new(&spec, table)) } #[test] fn recovers_planted_rule_and_rejects_independent_feature() { - let data = planted(500, 11, 0.05); // 5% label noise on f1 - let mut rng = Rng::new(42); - let mut ae = AerialAutoencoder::new(&data.spec, 4, &mut rng); - ae.train(&data, 600, 0.1, 0.3, &mut rng); - + let (data, dist) = fixture(20); // 5% label noise on f1 let params = ExtractParams { - ant_similarity: 0.5, - cons_similarity: 0.6, + theta: 2, // codebook prune: only "near" (dist ≤ 2) consequents max_antecedent: 1, - min_support: 0.05, - min_confidence: 0.7, + min_support_ppm: 50_000, // 5% + min_confidence_ppm: 700_000, // 70% }; - let rules = extract_rules(&ae, &data, ¶ms); + let rules = extract_rules(&dist, &data, ¶ms); - // The planted dependency f0 → f1 must surface (both directions of the - // category mapping are valid rules). - let has_f0_implies_f1 = rules.iter().any(|r| { + // planted f0=0 ⇒ f1=0 (codebook-near AND data-confirmed) + let has = rules.iter().any(|r| { r.antecedent == vec![Item::new(0, 0)] && r.consequent == vec![Item::new(1, 0)] }); - assert!(has_f0_implies_f1, "planted f0=0 ⇒ f1=0 not recovered: {rules:#?}"); + assert!(has, "planted f0=0 ⇒ f1=0 not recovered: {rules:#?}"); - // No rule should have feature 2 (independent) as a consequent of f0. + // feature 2 is codebook-far → pruned by theta → never a consequent of f0 let spurious = rules.iter().any(|r| { r.antecedent == vec![Item::new(0, 0)] && r.consequent[0].feature == 2 }); - assert!(!spurious, "independent feature 2 leaked as a consequent: {rules:#?}"); + assert!(!spurious, "independent feature 2 leaked: {rules:#?}"); - // Recovered rule confidence reflects the data (≈0.95 with 5% noise). let rule = rules .iter() .find(|r| r.antecedent == vec![Item::new(0, 0)] && r.consequent == vec![Item::new(1, 0)]) .unwrap(); - assert!(rule.confidence >= 0.7, "confidence {} below floor", rule.confidence); + assert!(rule.confidence_ppm() >= 700_000, "confidence below floor"); + } + + #[test] + fn fully_deterministic_no_seed() { + let (data, dist) = fixture(20); + let p = ExtractParams { theta: 2, max_antecedent: 1, min_support_ppm: 50_000, min_confidence_ppm: 700_000 }; + let a = extract_rules(&dist, &data, &p); + let b = extract_rules(&dist, &data, &p); + assert_eq!(a, b, "codebook probe is bitwise-deterministic by construction"); + } + + #[test] + fn theta_prune_blocks_far_consequents() { + // With a generous theta the far feature is *tested* (then data-gated); + // with a tight theta it is pruned before the data even sees it. + let (data, dist) = fixture(0); // no noise: f1==f0 exactly + let tight = ExtractParams { theta: 2, max_antecedent: 1, min_support_ppm: 50_000, min_confidence_ppm: 600_000 }; + let rules = extract_rules(&dist, &data, &tight); + assert!(rules.iter().all(|r| r.consequent[0].feature != 2)); + } + + #[test] + fn feature_combinations_are_correct() { + assert_eq!( + feature_combinations(&[0, 1, 2], 2), + vec![vec![0, 1], vec![0, 2], vec![1, 2]] + ); } #[test] fn empty_dataset_yields_no_rules() { let spec = FeatureSpec::new(vec![2, 2]); - let data = Dataset::new(spec, Vec::new()); - let mut rng = Rng::new(1); - let ae = AerialAutoencoder::new(&data.spec, 3, &mut rng); - assert!(extract_rules(&ae, &data, &ExtractParams::default()).is_empty()); + let data = Dataset::new(spec.clone(), Vec::new()); + let dist = MatrixDistance::new(&spec, vec![0u32; 16]); + assert!(extract_rules(&dist, &data, &ExtractParams::default()).is_empty()); } } diff --git a/crates/lance-graph-arm-discovery/src/aerial/mod.rs b/crates/lance-graph-arm-discovery/src/aerial/mod.rs index ed1476d4..69ef577a 100644 --- a/crates/lance-graph-arm-discovery/src/aerial/mod.rs +++ b/crates/lance-graph-arm-discovery/src/aerial/mod.rs @@ -1,111 +1,75 @@ // SPDX-License-Identifier: Apache-2.0 // SPDX-FileCopyrightText: Copyright The Lance Authors -//! Aerial+ — the neurosymbolic association-rule proposer (Stage A fan-in). +//! Aerial+ rule discovery — the **deterministic codebook-probe** backend. //! //! Transcode of Karabulut, Groth, Degeler, *Neurosymbolic Association Rule -//! Mining from Tabular Data* (arXiv 2504.19354v1). Two pieces: +//! Mining from Tabular Data* (arXiv 2504.19354v1), with the paper's float +//! autoencoder **replaced by an integer codebook distance oracle**. The +//! reconstruction probe is a nearest-neighbour query; this substrate answers +//! it exactly via the palette256 distance table (ρ=0.9973 vs cosine), so no +//! `f32`, no SGD, no seed enter the proposer. //! -//! - [`autoencoder::AerialAutoencoder`] — the under-complete denoising AE -//! (softmax-per-feature, cross-entropy), §3.2. -//! - [`extract::extract_rules`] — Algorithm 1, the reconstruction probe, §3.3. +//! - [`codebook::CodebookDistance`] — the injected integer similarity oracle +//! (palette256 / BLASGraph splat / HDR-popcount; `MatrixDistance` in tests). +//! - [`extract::extract_rules`] — Algorithm 1 as a codebook top-k + data +//! confirmation. +//! - [`AerialProposer`] — wires them into the [`crate::rule::Proposer`] +//! contract. //! -//! [`AerialProposer`] wires them into the [`crate::rule::Proposer`] contract: -//! `fit` trains the AE on a window; `next_batch` runs Algorithm 1 once and -//! returns the confirmed rules. +//! # No determinism firewall needed //! -//! # Why this is a *fan-in*, not the trunk -//! -//! Per `streaming-arm-nars-discovery-v1.md` the deterministic pair-stats -//! proposer is the default trunk; Aerial+ earns its keep only on -//! high-dimensional sparse data where pair/triple counters blow up. It is -//! nondeterministic in the general case, so it is seeded here for -//! reproducibility and its output is gated by the downstream ratification -//! council before any triple it proposes can reach the codegen path. - -pub mod autoencoder; +//! The autoencoder transcode was a *nondeterministic fan-in* that had to stay +//! behind the ratification gate and out of the compile path. The codebook +//! probe is **bitwise-deterministic by construction** — same data + same +//! oracle + same `theta` ⇒ identical rules on every target. It can sit in the +//! deterministic trunk beside pair-stats (D-ARM-3); the ratification gate +//! still governs *promotion to the SPO store*, but no longer because of any +//! nondeterminism here. + +pub mod codebook; pub mod extract; -pub mod rng; -pub use autoencoder::AerialAutoencoder; +pub use codebook::{antecedent_distance, CodebookDistance, MatrixDistance}; pub use extract::{extract_rules, ExtractParams}; -pub use rng::Rng; use crate::encode::Dataset; use crate::rule::{CandidateRule, Proposer}; -/// Full configuration for an [`AerialProposer`]: the autoencoder -/// hyper-parameters, the extraction thresholds, and the seed that makes the -/// whole run reproducible. -#[derive(Debug, Clone, Copy)] -pub struct AerialParams { - /// Latent dimension `H` (under-complete: keep `H < D`). - pub hidden_dim: usize, - /// Training epochs. - pub epochs: usize, - /// SGD learning rate. - pub learning_rate: f32, - /// Denoising mask probability (load-bearing — see autoencoder docs). - pub noise: f32, - /// Algorithm 1 extraction thresholds + ARM floors. - pub extract: ExtractParams, - /// PRNG seed — fixes weight init, denoising mask, and epoch shuffle. - pub seed: u64, -} +/// Configuration for an [`AerialProposer`] — the extraction thresholds. +/// (No autoencoder hyper-parameters and no seed: the backend is deterministic.) +pub type AerialParams = ExtractParams; -impl Default for AerialParams { - fn default() -> Self { - Self { - hidden_dim: 8, - epochs: 500, - learning_rate: 0.1, - noise: 0.3, - extract: ExtractParams::default(), - seed: 0x5EED, - } - } -} - -/// A trained Aerial+ proposer over one data window. +/// An Aerial+ codebook-probe proposer over one data window + a distance oracle. #[derive(Debug)] -pub struct AerialProposer { +pub struct AerialProposer { data: Dataset, + oracle: D, params: AerialParams, - ae: AerialAutoencoder, - /// Set once `next_batch` has run, so a second call returns `[]` - /// (the window is exhausted until `fit` is called on a fresh window). drained: bool, } -impl AerialProposer { - /// Train an autoencoder on the window and return a ready proposer. +impl AerialProposer { + /// Build a proposer over a window and an injected codebook oracle. + /// No training step — the oracle is the frozen co-occurrence model. #[must_use] - pub fn fit(data: Dataset, params: AerialParams) -> Self { - let mut rng = Rng::new(params.seed); - let mut ae = AerialAutoencoder::new(&data.spec, params.hidden_dim, &mut rng); - ae.train(&data, params.epochs, params.learning_rate, params.noise, &mut rng); + pub fn new(data: Dataset, oracle: D, params: AerialParams) -> Self { Self { data, + oracle, params, - ae, drained: false, } } - /// Borrow the trained autoencoder (for probes / diagnostics). - #[must_use] - pub fn autoencoder(&self) -> &AerialAutoencoder { - &self.ae - } - - /// Run Algorithm 1 once, regardless of the drained flag. + /// Run the codebook probe once, regardless of the drained flag. #[must_use] pub fn mine(&self) -> Vec { - extract_rules(&self.ae, &self.data, &self.params.extract) + extract_rules(&self.oracle, &self.data, &self.params) } } -impl Proposer for AerialProposer { +impl Proposer for AerialProposer { fn next_batch(&mut self) -> Vec { if self.drained { return Vec::new(); @@ -123,92 +87,72 @@ mod tests { use crate::rule::Item; use crate::translator::{CandidateTriple, DebugProjector, NARS_PERSONALITY_K}; - fn planted(n: usize, seed: u64) -> Dataset { - // f0 → f1 deterministic; f2 independent. + fn fixture() -> (Dataset, MatrixDistance) { let spec = FeatureSpec::new(vec![2, 2, 2]); - let mut rng = Rng::new(seed); - let rows = (0..n) - .map(|_| { - let a = (rng.next_u64() % 2) as u32; - let c = (rng.next_u64() % 2) as u32; + // Initialise every off-diagonal pair to "far" (50), diagonal to 0, then + // mark only the near pairs — so an unset cell can never read as nearest. + let mut table = vec![50u32; 36]; + for d in 0..6 { + table[d * 6 + d] = 0; + } + let set = |a: usize, b: usize, v: u32, t: &mut Vec| { + t[a * 6 + b] = v; + t[b * 6 + a] = v; + }; + // f0c0(0)~f1c0(2) near; f0c1(1)~f1c1(3) near. f2 (4,5) stays far. + set(0, 2, 1, &mut table); + set(1, 3, 1, &mut table); + let rows = (0..600) + .map(|i| { + let a = (i % 2) as u32; + let c = ((i / 2) % 2) as u32; vec![a, a, c] }) .collect(); - Dataset::new(spec, rows) + (Dataset::new(spec.clone(), rows), MatrixDistance::new(&spec, table)) } #[test] - fn proposer_end_to_end_yields_then_drains() { - let data = planted(400, 9); - let mut params = AerialParams { - hidden_dim: 4, - epochs: 600, - ..AerialParams::default() + fn proposer_yields_then_drains() { + let (data, dist) = fixture(); + let params = AerialParams { + theta: 2, + max_antecedent: 1, + min_support_ppm: 50_000, + min_confidence_ppm: 700_000, }; - params.extract.max_antecedent = 1; - params.extract.min_support = 0.05; - params.extract.min_confidence = 0.7; - - let mut proposer = AerialProposer::fit(data, params); - let batch = proposer.next_batch(); - assert!(!batch.is_empty(), "should mine the planted rule"); - // Drains: a second call is empty until re-fit on a new window. - assert!(proposer.next_batch().is_empty()); + let mut p = AerialProposer::new(data, dist, params); + assert!(!p.next_batch().is_empty()); + assert!(p.next_batch().is_empty()); } #[test] fn mined_rules_serialise_to_spo_ndjson() { - // End-to-end synergy check: mine → translate → ndjson in the exact - // {s,p,o,f,c} shape the SPO store loader reads. - let data = planted(400, 13); - let mut params = AerialParams { - hidden_dim: 4, - epochs: 600, - ..AerialParams::default() - }; - params.extract.max_antecedent = 1; - params.extract.min_support = 0.05; - params.extract.min_confidence = 0.7; - - let proposer = AerialProposer::fit(data, params); - let rules = proposer.mine(); + let (data, dist) = fixture(); + let params = AerialParams { theta: 2, max_antecedent: 1, min_support_ppm: 50_000, min_confidence_ppm: 700_000 }; + let p = AerialProposer::new(data, dist, params); + let rules = p.mine(); assert!(!rules.is_empty()); - - let projector = DebugProjector::default(); + let proj = DebugProjector::default(); let triples: Vec = rules .iter() - .map(|r| CandidateTriple::from_rule(r, &projector, NARS_PERSONALITY_K)) + .map(|r| CandidateTriple::from_rule(r, &proj, NARS_PERSONALITY_K)) .collect(); let ndjson = to_ndjson(&triples); - - // Every line is a {s,p,o,f,c} object terminated by newline. for line in ndjson.lines() { assert!(line.starts_with("{\"s\":\""), "line: {line}"); - assert!(line.contains("\"p\":\"implies\""), "line: {line}"); - assert!(line.contains("\"f\":"), "line: {line}"); - assert!(line.contains("\"c\":"), "line: {line}"); + assert!(line.contains("\"p\":\"implies\"")); } - assert!(ndjson.ends_with('\n')); - } - - #[test] - fn reproducible_from_seed() { - let p = AerialParams { - hidden_dim: 4, - epochs: 200, - seed: 777, - ..AerialParams::default() - }; - let r1 = AerialProposer::fit(planted(200, 4), p).mine(); - let r2 = AerialProposer::fit(planted(200, 4), p).mine(); - assert_eq!(r1, r2, "same seed + data ⇒ identical rules"); } #[test] - fn default_params_are_under_complete_on_small_specs() { - // sanity: default hidden 8 ≥ a 6-slot toy spec, so toy tests pass a - // smaller hidden explicitly; this asserts the default is documented. + fn reproducible_no_seed_needed() { + let (d1, dist1) = fixture(); + let (d2, dist2) = fixture(); + let p = AerialParams { theta: 2, max_antecedent: 1, min_support_ppm: 50_000, min_confidence_ppm: 700_000 }; + let r1 = AerialProposer::new(d1, dist1, p).mine(); + let r2 = AerialProposer::new(d2, dist2, p).mine(); + assert_eq!(r1, r2); let _ = Item::new(0, 0); - assert_eq!(AerialParams::default().hidden_dim, 8); } } diff --git a/crates/lance-graph-arm-discovery/src/aerial/rng.rs b/crates/lance-graph-arm-discovery/src/aerial/rng.rs deleted file mode 100644 index 4dd68d17..00000000 --- a/crates/lance-graph-arm-discovery/src/aerial/rng.rs +++ /dev/null @@ -1,125 +0,0 @@ -// SPDX-License-Identifier: Apache-2.0 -// SPDX-FileCopyrightText: Copyright The Lance Authors - -//! A tiny deterministic PRNG (SplitMix64) — zero-dep, seedable. -//! -//! The whole reason Aerial+ can be a *reproducible* fan-in proposer despite -//! being a neural net is that every source of randomness (weight init, -//! denoising mask, epoch shuffle) draws from one seeded stream. Same seed ⇒ -//! identical weights ⇒ identical mined rules. - -/// SplitMix64 — fast, well-distributed, fully deterministic from a `u64` seed. -#[derive(Debug, Clone)] -pub struct Rng { - state: u64, -} - -impl Rng { - /// Seed the generator. Any `u64` is a valid seed. - #[must_use] - pub fn new(seed: u64) -> Self { - Self { state: seed } - } - - /// Next raw `u64`. - pub fn next_u64(&mut self) -> u64 { - // SplitMix64 (Steele, Lea, Flood 2014). - self.state = self.state.wrapping_add(0x9E37_79B9_7F4A_7C15); - let mut z = self.state; - z = (z ^ (z >> 30)).wrapping_mul(0xBF58_476D_1CE4_E5B9); - z = (z ^ (z >> 27)).wrapping_mul(0x94D0_49BB_1331_11EB); - z ^ (z >> 31) - } - - /// Uniform `f32` in `[0, 1)`. - pub fn next_f32(&mut self) -> f32 { - // Top 24 bits → mantissa precision of f32. - let bits = (self.next_u64() >> 40) as u32; // 24 bits - bits as f32 / (1u32 << 24) as f32 - } - - /// Uniform `f32` in `[-range, range)`. - pub fn uniform(&mut self, range: f32) -> f32 { - (self.next_f32() * 2.0 - 1.0) * range - } - - /// Standard-normal `f32` via Box–Muller. - pub fn normal(&mut self) -> f32 { - // Guard u1 away from 0 so ln is finite. - let u1 = (self.next_f32()).max(1e-7); - let u2 = self.next_f32(); - let r = (-2.0 * u1.ln()).sqrt(); - r * (std::f32::consts::TAU * u2).cos() - } - - /// Fisher–Yates shuffle of an index slice in place. - pub fn shuffle(&mut self, idx: &mut [usize]) { - let len = idx.len(); - for i in (1..len).rev() { - let j = (self.next_u64() % (i as u64 + 1)) as usize; - idx.swap(i, j); - } - } - - /// Bernoulli draw: `true` with probability `p`. - pub fn bernoulli(&mut self, p: f32) -> bool { - self.next_f32() < p - } -} - -#[cfg(test)] -mod tests { - use super::*; - - #[test] - fn deterministic_from_seed() { - let mut a = Rng::new(42); - let mut b = Rng::new(42); - for _ in 0..100 { - assert_eq!(a.next_u64(), b.next_u64()); - } - } - - #[test] - fn different_seeds_diverge() { - let mut a = Rng::new(1); - let mut b = Rng::new(2); - assert_ne!(a.next_u64(), b.next_u64()); - } - - #[test] - fn uniform_in_range() { - let mut r = Rng::new(7); - for _ in 0..1000 { - let x = r.next_f32(); - assert!((0.0..1.0).contains(&x)); - } - } - - #[test] - fn shuffle_is_a_permutation() { - let mut r = Rng::new(99); - let mut idx: Vec = (0..50).collect(); - r.shuffle(&mut idx); - let mut sorted = idx.clone(); - sorted.sort_unstable(); - assert_eq!(sorted, (0..50).collect::>()); - } - - #[test] - fn normal_has_reasonable_spread() { - let mut r = Rng::new(3); - let n = 5000; - let mut sum = 0.0f64; - let mut sumsq = 0.0f64; - for _ in 0..n { - let x = r.normal() as f64; - sum += x; - sumsq += x * x; - } - let mean = sum / n as f64; - let var = sumsq / n as f64 - mean * mean; - assert!(mean.abs() < 0.1, "mean ~0, got {mean}"); - assert!((var - 1.0).abs() < 0.2, "var ~1, got {var}"); - } -} diff --git a/crates/lance-graph-arm-discovery/src/encode.rs b/crates/lance-graph-arm-discovery/src/encode.rs index cb785f10..7af2a5c1 100644 --- a/crates/lance-graph-arm-discovery/src/encode.rs +++ b/crates/lance-graph-arm-discovery/src/encode.rs @@ -1,29 +1,25 @@ // SPDX-License-Identifier: Apache-2.0 // SPDX-FileCopyrightText: Copyright The Lance Authors -//! One-hot categorical encoding — the input layer of the Aerial+ transcode. +//! Codebook items + the data window — **integer only**. //! -//! Aerial+ (paper §3.1) one-hot encodes each row into a concatenation of -//! per-feature blocks: feature `i` with `k_i` categories occupies a block of -//! `k_i` slots, exactly one of which is `1.0` (the observed category). The -//! full input vector has dimension `D = Σ k_i`. -//! -//! Numerical columns must be discretised into bins *before* they reach this -//! layer (the paper bins numerics; [`FeatureSpec::bin`] is the helper). +//! Each row is a list of category indices, one per feature (a tuple of +//! codebook codes). There is no one-hot float vector and no embedding: the +//! codebook-probe backend ([`crate::aerial`]) reasons over category indices +//! and integer co-occurrence counts, and the similarity it needs comes from +//! the injected [`crate::aerial::CodebookDistance`] oracle. Numeric columns +//! must be discretised into category indices *before* they reach this layer +//! (that quantisation belongs to the CAM/palette codebook, not here). use crate::rule::Item; -/// The schema for a one-hot encoding: the ordered list of features and how -/// many categories each has. -/// -/// Block offsets are derived once and cached so encode/decode is a pointer -/// add, not a scan. +/// The schema for a window: the ordered features and how many categories each +/// has. Block offsets are derived once and cached. #[derive(Debug, Clone, PartialEq, Eq)] pub struct FeatureSpec { - /// Number of categories per feature, in feature order. cardinalities: Vec, - /// Prefix-sum offsets: `offsets[i]` is the first input slot of feature `i`. - /// Length is `cardinalities.len() + 1`; the last entry is the total dim. + /// Prefix-sum offsets: `offsets[i]` is the first code slot of feature `i`; + /// the last entry is the total code space size. offsets: Vec, } @@ -31,8 +27,7 @@ impl FeatureSpec { /// Build a spec from per-feature cardinalities. /// /// # Panics - /// If any cardinality is zero (a feature with no categories cannot be - /// one-hot encoded). + /// If any cardinality is zero. #[must_use] pub fn new(cardinalities: Vec) -> Self { assert!( @@ -52,7 +47,7 @@ impl FeatureSpec { } } - /// Total input dimension `D = Σ k_i`. + /// Total code space `Σ kᵢ` (the side length of a per-item distance table). #[must_use] pub fn dim(&self) -> usize { *self.offsets.last().expect("offsets always has ≥1 entry") @@ -70,50 +65,21 @@ impl FeatureSpec { self.cardinalities[feature] } - /// The `[start, end)` input-slot range owned by a feature's block. + /// The `[start, end)` code range owned by a feature's block. #[must_use] pub fn block(&self, feature: usize) -> (usize, usize) { (self.offsets[feature], self.offsets[feature + 1]) } - /// The absolute input slot for an `(feature, category)` item. + /// The absolute code slot for an `(feature, category)` item. #[must_use] pub fn slot(&self, item: Item) -> usize { self.offsets[item.feature as usize] + item.category as usize } - - /// One-hot encode a row given as one category index per feature. - /// - /// `row[i]` is the observed category of feature `i`. Returns a `D`-length - /// vector with one `1.0` per block. - #[must_use] - pub fn encode(&self, row: &[u32]) -> Vec { - assert_eq!(row.len(), self.num_features(), "row arity mismatch"); - let mut v = vec![0.0f32; self.dim()]; - for (feature, &cat) in row.iter().enumerate() { - debug_assert!(cat < self.cardinalities[feature], "category out of range"); - v[self.offsets[feature] + cat as usize] = 1.0; - } - v - } - - /// Discretise a numeric value into `[0, bins)` over `[lo, hi]` (uniform - /// width). Values at or below `lo` land in bin 0; at or above `hi` in the - /// last bin. This is the pre-encoding step for numerical columns. - #[must_use] - pub fn bin(value: f32, lo: f32, hi: f32, bins: u32) -> u32 { - debug_assert!(bins > 0 && hi > lo, "bad binning parameters"); - let t = ((value - lo) / (hi - lo)).clamp(0.0, 1.0); - let idx = (t * bins as f32) as u32; - idx.min(bins - 1) - } } /// A window of rows in category-index form, sharing one [`FeatureSpec`]. -/// -/// This is the unit Aerial+ trains on and the unit support/confidence are -/// counted over. `rows.len()` is the `n` that the NARS confidence mapping -/// uses as evidential mass. +/// `rows.len()` is the `n` the evidence counts are measured over. #[derive(Debug, Clone)] pub struct Dataset { /// The shared schema. @@ -148,6 +114,7 @@ impl Dataset { } /// Count of rows containing every item in `items` (an AND over items). + /// The one and only evidence primitive — integer, exact. #[must_use] pub fn count_matching(&self, items: &[Item]) -> u32 { self.rows @@ -159,29 +126,6 @@ impl Dataset { }) .count() as u32 } - - /// Classical support of an itemset: `count_matching / n`. - #[must_use] - pub fn support(&self, items: &[Item]) -> f32 { - if self.rows.is_empty() { - return 0.0; - } - self.count_matching(items) as f32 / self.rows.len() as f32 - } - - /// Classical confidence of `antecedent → consequent`: - /// `count(antecedent ∪ consequent) / count(antecedent)`. - /// Returns `0.0` when the antecedent never occurs. - #[must_use] - pub fn confidence(&self, antecedent: &[Item], consequent: &[Item]) -> f32 { - let ant = self.count_matching(antecedent); - if ant == 0 { - return 0.0; - } - let mut both: Vec = antecedent.to_vec(); - both.extend_from_slice(consequent); - self.count_matching(&both) as f32 / ant as f32 - } } #[cfg(test)] @@ -198,16 +142,6 @@ mod tests { assert_eq!(spec.block(2), (5, 6)); } - #[test] - fn one_hot_sets_one_per_block() { - let spec = FeatureSpec::new(vec![2, 3]); - let v = spec.encode(&[1, 2]); - assert_eq!(v, vec![0.0, 1.0, 0.0, 0.0, 1.0]); - // exactly one 1.0 per block - assert_eq!(v[0..2].iter().sum::(), 1.0); - assert_eq!(v[2..5].iter().sum::(), 1.0); - } - #[test] fn slot_addressing() { let spec = FeatureSpec::new(vec![2, 3]); @@ -216,31 +150,13 @@ mod tests { } #[test] - fn binning_clamps_edges() { - assert_eq!(FeatureSpec::bin(-5.0, 0.0, 10.0, 5), 0); - assert_eq!(FeatureSpec::bin(0.0, 0.0, 10.0, 5), 0); - assert_eq!(FeatureSpec::bin(9.99, 0.0, 10.0, 5), 4); - assert_eq!(FeatureSpec::bin(100.0, 0.0, 10.0, 5), 4); - assert_eq!(FeatureSpec::bin(5.0, 0.0, 10.0, 5), 2); - } - - #[test] - fn support_and_confidence_counts() { + fn count_matching_is_an_and_over_items() { let spec = FeatureSpec::new(vec![2, 2]); // rows: (0,0),(0,0),(0,1),(1,1) - let data = Dataset::new( - spec, - vec![vec![0, 0], vec![0, 0], vec![0, 1], vec![1, 1]], - ); - // support of feature0=0 : 3/4 - assert!((data.support(&[Item::new(0, 0)]) - 0.75).abs() < 1e-6); - // confidence (f0=0 -> f1=0): count(0,0)=2 / count(f0=0)=3 - let c = data.confidence(&[Item::new(0, 0)], &[Item::new(1, 0)]); - assert!((c - 2.0 / 3.0).abs() < 1e-6); - // confidence on never-seen antecedent is 0 - assert_eq!( - data.confidence(&[Item::new(0, 5)], &[Item::new(1, 0)]), - 0.0 - ); + let data = Dataset::new(spec, vec![vec![0, 0], vec![0, 0], vec![0, 1], vec![1, 1]]); + assert_eq!(data.count_matching(&[Item::new(0, 0)]), 3); + assert_eq!(data.count_matching(&[Item::new(0, 0), Item::new(1, 0)]), 2); + assert_eq!(data.count_matching(&[Item::new(0, 5)]), 0); // absent category + assert_eq!(data.len(), 4); } } diff --git a/crates/lance-graph-arm-discovery/src/lib.rs b/crates/lance-graph-arm-discovery/src/lib.rs index 97c77bc1..68359d51 100644 --- a/crates/lance-graph-arm-discovery/src/lib.rs +++ b/crates/lance-graph-arm-discovery/src/lib.rs @@ -5,71 +5,62 @@ //! streaming association-rule discovery → NARS revision → ratified SPO → //! deterministic codegen pipeline (`streaming-arm-nars-discovery-v1.md`). //! -//! This crate is the Rust transcoding of **Aerial+** (Karabulut, Groth, -//! Degeler — *Neurosymbolic Association Rule Mining from Tabular Data*, -//! arXiv 2504.19354v1, Apr 2025), the neurosymbolic association-rule miner. -//! It mines `(X → Y)` rules from tabular runtime data and lifts each rule -//! into a NARS-truth-carrying SPO candidate emitted in the **same ndjson -//! shape** (`{s,p,o,f,c}`) as the static `ruff_spo_triplet` extractor. The -//! lance-graph `parse_triples` loader consumes it directly; the `ruff` -//! `from_ndjson` loader will accept it only once its closed predicate -//! vocabulary gains an implication relation (D-ARM-SYN-1) — see [`ndjson`]. -//! -//! # Pipeline position +//! A Rust transcoding of **Aerial+** (Karabulut, Groth, Degeler — +//! *Neurosymbolic Association Rule Mining from Tabular Data*, arXiv +//! 2504.19354v1) — with the paper's `f32` autoencoder **replaced by an +//! integer codebook distance oracle**. Aerial+'s reconstruction probe is a +//! nearest-neighbour query, and this substrate answers it exactly via the +//! palette256 distance table (`[a,b] → u32`, ρ=0.9973 vs cosine). So the +//! discovery path is **float-free and bitwise-deterministic** — no autoencoder, +//! no SGD, no seed. //! //! ```text -//! tabular rows ─┐ -//! ▼ Stage A — Aerial+ proposer (THIS CRATE, `aerial`) -//! one-hot encode → denoising autoencoder (softmax-per-feature, BCE) -//! → Algorithm 1 reconstruction probe → CandidateRule -//! ▼ Stage B — translator (THIS CRATE, `translator`) -//! arm_to_nars: (support, confidence, n) → NARS (frequency, confidence) -//! ▼ emit (THIS CRATE, `ndjson`) -//! {"s","p","o","f","c"} ndjson ─► lance_graph SPO store loader -//! ▼ Stage C/D/E — hypothesis test → council → op_emitter -//! (downstream, other crates) +//! tabular rows ─► one-hot codebook items +//! ─► codebook probe: nearest consequents within θ (Stage A) +//! (integer CodebookDistance oracle = palette256) +//! ─► confirm on data: integer support/confidence counts +//! ─► CandidateRule (integer evidence) +//! ─► arm_to_truth_u8: (cooccur, antecedent_count) → TruthU8 (Stage B) +//! (= CausalEdge64 confidence_u8 + i4 mantissa) +//! ─► {s,p,o,f,c} ndjson ─► SPO store loader //! ``` //! -//! # The determinism boundary (read before extending) -//! -//! Aerial+'s autoencoder is **nondeterministic** in the general case (random -//! init, denoising noise, float reduction order). Per the plan's determinism -//! firewall it is a **fan-in proposer**, NEVER the deterministic trunk and -//! NEVER allowed to cross the ratification gate (Stage D) un-vetted. Two -//! consequences are baked into this crate: +//! # Float discipline (why this exists) //! -//! 1. The autoencoder is **seedable** ([`aerial::Rng`]). Same seed, same data, -//! and same hyper-parameters give reproducible weights and identical rules -//! *on a given target*. (This is intra-platform reproducibility, not -//! bitwise-portable determinism: seeded f32 `tanh`/`exp`/`ln` and FMA -//! contraction can differ across targets.) It makes the proposer auditable -//! even though it is not the canonical deterministic path. -//! 2. The output is a plain [`rule::CandidateRule`] — a *proposal*, not a -//! committed triple. Promotion to the SPO store is the downstream -//! hypothesis-test + council's job, not this crate's. +//! This substrate addresses by exact codebook CAM, never by float similarity +//! (`faiss-homology-cam-pq.md`: similarity lives in the discovery layer as an +//! *integer codebook distance*, never as float, never in addressing). The +//! discovery path here is therefore all integers: codebook distance (`u32`), +//! evidence counts (`u32`), thresholds in parts-per-million. `f32` appears +//! only at the [`translator::arm_to_nars`] / [`ndjson`] edge, because the +//! downstream `spo::truth::TruthValue` and `ruff_spo_triplet::Triple` are +//! themselves `f32`; the canonical truth is the quantised +//! [`translator::TruthU8`]. //! -//! # Truth mapping (verbatim from the paper, §2 / §3.3) +//! # No nondeterminism to fence //! -//! - ARM **confidence** = P(Y|X) → NARS **frequency** `f` -//! - ARM **support × n** (evidential mass) → NARS **confidence** `c = m/(m+k)` -//! -//! See [`translator::arm_to_nars`]. The resulting `(f, c)` is exactly the pair -//! `lance_graph::graph::spo::TruthValue::new(f, c)` and -//! `ruff_spo_triplet::Triple { f, c }` carry. +//! The codebook probe is deterministic by construction (same data + oracle + +//! `theta` ⇒ identical rules, every target). Unlike the autoencoder it +//! replaced, it does not need to hide behind the ratification gate or stay out +//! of the compile path — it can join the deterministic trunk beside +//! pair-stats (D-ARM-3). The ratification council still governs *promotion to +//! the SPO store*, but no longer because of any nondeterminism here. #![forbid(unsafe_code)] +pub mod aerial; pub mod encode; pub mod ndjson; pub mod rule; pub mod translator; -#[cfg(feature = "aerial")] -pub mod aerial; - +pub use aerial::{ + antecedent_distance, extract_rules, AerialParams, AerialProposer, CodebookDistance, + ExtractParams, MatrixDistance, +}; pub use encode::{Dataset, FeatureSpec}; -pub use rule::{CandidateRule, Item, Proposer}; -pub use translator::{arm_to_nars, CandidateTriple, FeedProjector, NarsTruth}; - -#[cfg(feature = "aerial")] -pub use aerial::{AerialAutoencoder, AerialParams, AerialProposer, Rng}; +pub use rule::{CandidateRule, Item, Proposer, PPM}; +pub use translator::{ + arm_to_nars, arm_to_truth_u8, CandidateTriple, FeedProjector, NarsTruth, TruthU8, + NARS_PERSONALITY_K, +}; diff --git a/crates/lance-graph-arm-discovery/src/rule.rs b/crates/lance-graph-arm-discovery/src/rule.rs index fa2cebbc..c3733dc2 100644 --- a/crates/lance-graph-arm-discovery/src/rule.rs +++ b/crates/lance-graph-arm-discovery/src/rule.rs @@ -3,22 +3,27 @@ //! The proposer output carrier — `CandidateRule` and the `Proposer` trait. //! -//! This is the shape every Stage-A proposer (Aerial+, the pair-stats trunk, -//! the Python IPC fan-in) emits. It is the **local mirror** of the planned -//! `lance-graph-contract::CandidateRule` (D-ARM-2) — *not yet field-frozen*: -//! the planned carrier pairs the rule with a `WindowMetadata`, whereas this -//! local one carries a bare `n: u32`. When D-ARM-2 lands, adopt it by -//! `pub use` re-export — the determinism firewall forbids depending on -//! `lance-graph`, NOT on the zero-dep `lance-graph-contract`, so this crate -//! can path-dep the contract and stay zero-dep-from-the-spine. Until then -//! treat this as the migration seam, not the canonical type (TD-ARM-CARRIER-FORK). +//! **Float-free.** A rule carries integer evidence counts, not `f32` +//! statistics. Support and confidence are rationals (`count / count`); they +//! are compared as integers (parts-per-million, cross-multiplied) so the +//! discovery decision path never touches a float. `f32` appears only at the +//! explicit edge accessors that feed the downstream `f32` contracts +//! (`spo::truth::TruthValue`, `ruff_spo_triplet::Triple`), and the canonical +//! truth wire is the quantized `CausalEdge64` form — see +//! [`crate::translator::arm_to_truth_u8`]. +//! +//! This is the shape every Stage-A proposer (the codebook-probe Aerial+ +//! backend, the pair-stats trunk) emits. It is the **local mirror** of the +//! planned `lance-graph-contract::CandidateRule` (D-ARM-2) — *not yet +//! field-frozen*; adopt by `pub use` re-export when the contract carrier +//! lands (the determinism firewall forbids depending on `lance-graph`, NOT on +//! the zero-dep `lance-graph-contract`). Until then this is the migration +//! seam, not the canonical type (TD-ARM-CARRIER-FORK). /// One `(feature, category)` atom — the unit an antecedent / consequent is -/// built from. -/// -/// `feature` indexes a column in the [`crate::FeatureSpec`]; `category` -/// indexes a value within that feature's category list. Both are small -/// integers so a rule is cheap to compare, hash, and serialise. +/// built from. `feature` indexes a [`crate::FeatureSpec`] column; `category` +/// indexes a value within it. Both are small integers — a codebook code, not +/// a vector. #[derive(Debug, Clone, Copy, PartialEq, Eq, PartialOrd, Ord, Hash)] pub struct Item { /// Feature (column) index into the [`crate::FeatureSpec`]. @@ -35,70 +40,92 @@ impl Item { } } -/// A mined association rule `antecedent → consequent` with ARM statistics. -/// -/// The local proposer carrier — mirrors the planned -/// `lance-graph-contract::CandidateRule` (D-ARM-2) but does **not** yet match -/// it field-for-field (D-ARM-2 pairs the rule with `WindowMetadata`; this -/// carries a bare `n`). `support` and `confidence` are the classical ARM -/// quantities (paper §2): +/// Fixed-point scale for support/confidence comparison (parts per million). +/// A threshold of "1%" is `10_000`; "50%" is `500_000`. Integer throughout. +pub const PPM: u64 = 1_000_000; + +/// A mined association rule `antecedent → consequent` with **integer** +/// ARM evidence. /// -/// - `support` = |{rows ⊇ antecedent ∪ consequent}| / n -/// - `confidence` = |{rows ⊇ antecedent ∪ consequent}| / |{rows ⊇ antecedent}| +/// Mirrors the planned `lance-graph-contract::CandidateRule` (D-ARM-2) but +/// does not yet match it field-for-field (D-ARM-2 pairs the rule with +/// `WindowMetadata`; this carries a bare `window`). The classical ARM +/// quantities (paper §2) are derived from the three counts: /// -/// `n` is the window size the statistics were measured over — it is what -/// turns `support` into an evidential count for the NARS confidence mapping -/// in [`crate::translator::arm_to_nars`]. -#[derive(Debug, Clone, PartialEq)] +/// - `support = cooccur / window` +/// - `confidence = cooccur / antecedent_count` (= `P(consequent | antecedent)`) +#[derive(Debug, Clone, PartialEq, Eq)] pub struct CandidateRule { /// Left-hand side items (all must hold). Sorted, at most one per feature. pub antecedent: Vec, - /// Right-hand side items. Aerial+ emits exactly one (single-consequent - /// rules); the `Vec` keeps shape-parity with the planned contract. + /// Right-hand side items. The codebook probe emits exactly one; the `Vec` + /// keeps shape-parity with the planned contract. pub consequent: Vec, - /// `support ∈ [0, 1]` — co-occurrence fraction over the window. - pub support: f32, - /// `confidence ∈ [0, 1]` — `P(consequent | antecedent)`. - pub confidence: f32, - /// Window size the statistics were measured over. - pub n: u32, + /// `|{rows ⊇ antecedent ∪ consequent}|` — the co-occurrence count, and the + /// NARS evidential mass `m`. + pub cooccur: u32, + /// `|{rows ⊇ antecedent}|` — the denominator of confidence. + pub antecedent_count: u32, + /// Window size `n` the counts were measured over. + pub window: u32, } impl CandidateRule { - /// Evidential mass: `support × n`, the count of rows supporting the rule. - /// Drives the NARS confidence term `c = m / (m + k)`. + /// Evidential mass `m = cooccur` — the count of rows supporting the rule. + /// Drives the NARS confidence term `c = m / (m + k)`. Integer. + #[must_use] + pub fn evidence(&self) -> u32 { + self.cooccur + } + + /// Support in parts-per-million: `cooccur · 1e6 / window`. Integer. #[must_use] - pub fn evidence(&self) -> f32 { - self.support * self.n as f32 + pub fn support_ppm(&self) -> u32 { + if self.window == 0 { + return 0; + } + ((self.cooccur as u64 * PPM) / self.window as u64) as u32 } - /// A rule survives the classical ARM gate iff it clears both the minimum - /// support and minimum confidence floors. - /// - /// **Not implemented yet:** the Jirak-bound significance floor - /// (`I-NOISE-FLOOR-JIRAK`, deliverable D-ARM-7) is the *mandatory* Stage-A - /// gate per the plan, but it does **not** exist in this crate today - /// (`jirak` appears nowhere; D-ARM-7 is Queued). Until it lands, `passes()` - /// is the ONLY gate, and this proposer MUST NOT be wired to a live - /// `SpoStore`: the classical floor alone leaks thin-but-frequent rules past - /// the substrate noise floor (plan §11.1 — "the substrate calcifies on - /// noise"). Tracked as ISSUE ARM-JIRAK-FLOOR. + /// Confidence in parts-per-million: `cooccur · 1e6 / antecedent_count`. + /// Integer. Returns 0 when the antecedent never occurs. #[must_use] - pub fn passes(&self, min_support: f32, min_confidence: f32) -> bool { - self.support >= min_support && self.confidence >= min_confidence + pub fn confidence_ppm(&self) -> u32 { + if self.antecedent_count == 0 { + return 0; + } + ((self.cooccur as u64 * PPM) / self.antecedent_count as u64) as u32 + } + + /// The classical ARM gate, compared entirely in integers (ppm). + /// The Jirak-bound significance floor (`I-NOISE-FLOOR-JIRAK`, D-ARM-7) is a + /// separate, stricter, still-unimplemented gate — see ISSUE + /// ARM-JIRAK-FLOOR; do not wire this proposer to a live `SpoStore` until + /// D-ARM-7 lands. + #[must_use] + pub fn passes(&self, min_support_ppm: u32, min_confidence_ppm: u32) -> bool { + self.support_ppm() >= min_support_ppm && self.confidence_ppm() >= min_confidence_ppm + } + + /// `f32` support — **edge convenience only**, for the downstream `f32` + /// `TruthValue`/`Triple` contracts. Not used in any decision. + #[must_use] + pub fn support_f32(&self) -> f32 { + self.support_ppm() as f32 / PPM as f32 + } + + /// `f32` confidence — edge convenience only (see [`Self::support_f32`]). + #[must_use] + pub fn confidence_f32(&self) -> f32 { + self.confidence_ppm() as f32 / PPM as f32 } } /// A Stage-A proposer: something that emits a batch of candidate rules. -/// -/// Mirrors the planned `lance-graph-contract::Proposer` (D-ARM-2) so the -/// discovery crate is dependency-injectable: the hypothesis-test stage takes -/// `&mut dyn Proposer` and does not care whether the rules came from the -/// deterministic pair-stats trunk or the Aerial+ fan-in. +/// Mirrors the planned `lance-graph-contract::Proposer` (D-ARM-2). pub trait Proposer { - /// Produce the next batch of candidate rules. Returning an empty `Vec` - /// signals the proposer is exhausted for now (e.g. the window closed and - /// no more rules cleared the gate). + /// Produce the next batch of candidate rules. An empty `Vec` signals the + /// proposer is exhausted for now. fn next_batch(&mut self) -> Vec; } @@ -106,39 +133,47 @@ pub trait Proposer { mod tests { use super::*; - #[test] - fn evidence_is_support_times_n() { - let r = CandidateRule { + fn rule(cooccur: u32, antecedent_count: u32, window: u32) -> CandidateRule { + CandidateRule { antecedent: vec![Item::new(0, 1)], consequent: vec![Item::new(1, 0)], - support: 0.25, - confidence: 0.9, - n: 1000, - }; - assert!((r.evidence() - 250.0).abs() < 1e-3); + cooccur, + antecedent_count, + window, + } + } + + #[test] + fn evidence_is_the_cooccur_count() { + assert_eq!(rule(250, 300, 1000).evidence(), 250); + } + + #[test] + fn support_and_confidence_are_integer_ppm() { + let r = rule(250, 300, 1000); + assert_eq!(r.support_ppm(), 250_000); // 25% + assert_eq!(r.confidence_ppm(), 833_333); // 250/300 ≈ 0.8333 + } + + #[test] + fn passes_gates_on_both_floors_in_ppm() { + let r = rule(100, 125, 2000); // support 5%, confidence 80% + assert!(r.passes(10_000, 500_000)); + assert!(!r.passes(100_000, 500_000), "support floor not met"); + assert!(!r.passes(10_000, 900_000), "confidence floor not met"); } #[test] - fn passes_gates_on_both_floors() { - let r = CandidateRule { - antecedent: vec![Item::new(0, 0)], - consequent: vec![Item::new(1, 1)], - support: 0.05, - confidence: 0.8, - n: 200, - }; - assert!(r.passes(0.01, 0.5)); - assert!(!r.passes(0.10, 0.5), "support floor not met"); - assert!(!r.passes(0.01, 0.9), "confidence floor not met"); + fn zero_antecedent_is_zero_confidence_not_a_panic() { + let r = rule(0, 0, 1000); + assert_eq!(r.confidence_ppm(), 0); + assert!(!r.passes(0, 1)); } #[test] fn item_ordering_is_feature_then_category() { let mut items = vec![Item::new(1, 0), Item::new(0, 2), Item::new(0, 1)]; items.sort(); - assert_eq!( - items, - vec![Item::new(0, 1), Item::new(0, 2), Item::new(1, 0)] - ); + assert_eq!(items, vec![Item::new(0, 1), Item::new(0, 2), Item::new(1, 0)]); } } diff --git a/crates/lance-graph-arm-discovery/src/translator.rs b/crates/lance-graph-arm-discovery/src/translator.rs index 289251a7..ce5fc35b 100644 --- a/crates/lance-graph-arm-discovery/src/translator.rs +++ b/crates/lance-graph-arm-discovery/src/translator.rs @@ -1,33 +1,59 @@ // SPDX-License-Identifier: Apache-2.0 // SPDX-FileCopyrightText: Copyright The Lance Authors -//! Stage B — translate an ARM [`CandidateRule`] into NARS truth + an SPO -//! triple carrier. +//! Stage B — translate a [`CandidateRule`]'s integer evidence into NARS truth +//! + an SPO triple carrier. //! -//! The mapping is verbatim from Aerial+ §2 / §3.3 and the plan's FINDING: +//! The mapping is verbatim from Aerial+ §2/§3.3, computed in **integers**: //! -//! - ARM **confidence** = `P(Y|X)` → NARS **frequency** `f` -//! - ARM **support × n** (evidential mass `m`) → NARS **confidence** -//! `c = m / (m + k)`, NAL-9 personality constant `k` (default `1.0`) +//! - ARM **confidence** = `cooccur / antecedent_count` → NARS **frequency** +//! - ARM **evidence** `m = cooccur` → NARS **confidence** `c = m / (m + k)` //! -//! The resulting `(f, c)` is the exact pair consumed by -//! `lance_graph::graph::spo::TruthValue::new(f, c)` and carried by -//! `ruff_spo_triplet::Triple { f, c }`. That is the whole point: a rule mined -//! from runtime data and a triple extracted from static source land in the -//! SPO store on the *same* truth scale. +//! The canonical truth is [`TruthU8`] — `frequency`/`confidence` as `u8`, +//! which is exactly the `CausalEdge64` wire (`confidence_u8` + i4 mantissa), +//! float-free. [`NarsTruth`] / [`arm_to_nars`] are a thin `f32` **edge** that +//! exists only because the downstream `spo::truth::TruthValue` and +//! `ruff_spo_triplet::Triple` are themselves `f32`; nothing in the discovery +//! path consumes the `f32` form. use crate::rule::{CandidateRule, Item}; -/// NAL-9 default personality constant for the support → confidence mapping. -/// Larger `k` ⇒ more evidence needed before confidence approaches 1. -pub const NARS_PERSONALITY_K: f32 = 1.0; +/// NAL-9 default personality constant (integer). Larger `k` ⇒ more evidence +/// needed before confidence approaches saturation. +pub const NARS_PERSONALITY_K: u32 = 1; + +/// Quantised NARS truth — the canonical, float-free wire form (mirrors the +/// `CausalEdge64` `confidence_u8` + i4 mantissa fields). `255` = 1.0. +#[derive(Debug, Clone, Copy, PartialEq, Eq)] +pub struct TruthU8 { + /// NARS frequency, `0..=255` (255 = 1.0). + pub frequency: u8, + /// NARS confidence, `0..=255`. + pub confidence: u8, +} + +/// Translate a candidate rule's integer evidence into quantised NARS truth. +/// `k` is the NARS personality constant ([`NARS_PERSONALITY_K`] default). +#[must_use] +pub fn arm_to_truth_u8(rule: &CandidateRule, k: u32) -> TruthU8 { + // frequency = P(Y|X) = cooccur / antecedent_count + let frequency = if rule.antecedent_count == 0 { + 128 // unknown ≈ 0.5 + } else { + ((rule.cooccur as u64 * 255) / rule.antecedent_count as u64).min(255) as u8 + }; + // confidence = m / (m + k), m = cooccur (integer evidential mass) + let m = rule.cooccur as u64; + let denom = (m + k as u64).max(1); + let confidence = ((m * 255) / denom) as u8; + TruthU8 { + frequency, + confidence, + } +} -/// A NARS truth value `(frequency, confidence)` — the translator's output. -/// -/// Deliberately *not* a re-implementation of the SPO store's `TruthValue`: -/// it is the `(f, c)` pair you feed to `TruthValue::new(f, c)`. Keeping it a -/// distinct, tiny carrier honours `E-SOA-IS-THE-ONLY` (no parallel truth -/// store) while keeping this crate zero-dep. +/// An `f32` NARS truth — **edge convenience only** (see module docs). Derived +/// from [`TruthU8`]; never read inside the discovery path. #[derive(Debug, Clone, Copy, PartialEq)] pub struct NarsTruth { /// NARS frequency `f ∈ [0, 1]`. @@ -38,35 +64,25 @@ pub struct NarsTruth { impl NarsTruth { /// NARS expectation `e = c·(f − 0.5) + 0.5` — matches - /// `lance_graph::graph::spo::TruthValue::expectation` exactly. + /// `spo::truth::TruthValue::expectation`. #[must_use] pub fn expectation(&self) -> f32 { self.confidence * (self.frequency - 0.5) + 0.5 } } -/// Translate a candidate rule's ARM statistics into NARS truth. -/// -/// `k` is the NARS personality constant ([`NARS_PERSONALITY_K`] by default, -/// per-feed configurable per OQ-ARM-3). +/// `f32` edge of [`arm_to_truth_u8`], for the downstream `f32` `TruthValue` / +/// `Triple` contracts. #[must_use] -pub fn arm_to_nars(rule: &CandidateRule, k: f32) -> NarsTruth { - // ARM confidence is P(Y|X) — directly the NARS frequency. - let frequency = rule.confidence.clamp(0.0, 1.0); - // Evidential mass m = support × n; c = m / (m + k) → 1 as evidence grows. - let m = rule.evidence().max(0.0); - let confidence = m / (m + k); +pub fn arm_to_nars(rule: &CandidateRule, k: u32) -> NarsTruth { + let t = arm_to_truth_u8(rule, k); NarsTruth { - frequency, - confidence, + frequency: t.frequency as f32 / 255.0, + confidence: t.confidence as f32 / 255.0, } } /// Projects rule items into SPO IRIs for a particular feed/domain. -/// -/// An Odoo feed projects `(model, predicate, value)`; an MedCare feed -/// projects differently. The projector is the only domain-specific seam in -/// Stage B — everything else is feed-agnostic. pub trait FeedProjector { /// IRI for the subject built from the antecedent items. fn subject(&self, antecedent: &[Item]) -> String; @@ -79,12 +95,9 @@ pub trait FeedProjector { /// One SPO triple with NARS truth — shape-compatible with /// `ruff_spo_triplet::Triple` and the SPO store's ndjson loader (same /// `{s,p,o,f,c}` fields; see [`crate::ndjson`] for the predicate-vocabulary -/// caveat that gates the `ruff` loader on D-ARM-SYN-1). -/// -/// `(s, p, o)` is the identity; `(f, c)` is the data-derived truth. The -/// `origin` byte records that this triple came from the ARM-discovery -/// proposer (bits per `discovery_origin` in the plan §7) — it is metadata, -/// dropped on the ndjson wire, not part of the triple identity. +/// caveat that gates the `ruff` loader on D-ARM-SYN-1). The `f`/`c` floats are +/// the *serialization format* of that downstream contract, derived from the +/// canonical integer [`TruthU8`] at the wire edge. #[derive(Debug, Clone, PartialEq)] pub struct CandidateTriple { /// Subject IRI. @@ -93,20 +106,16 @@ pub struct CandidateTriple { pub p: String, /// Object IRI. pub o: String, - /// NARS frequency. + /// NARS frequency (serialization edge). pub f: f32, - /// NARS confidence. + /// NARS confidence (serialization edge). pub c: f32, } impl CandidateTriple { /// Build a triple from a candidate rule, a projector, and a NARS `k`. #[must_use] - pub fn from_rule( - rule: &CandidateRule, - projector: &dyn FeedProjector, - k: f32, - ) -> Self { + pub fn from_rule(rule: &CandidateRule, projector: &dyn FeedProjector, k: u32) -> Self { let truth = arm_to_nars(rule, k); Self { s: projector.subject(&rule.antecedent), @@ -118,9 +127,7 @@ impl CandidateTriple { } } -/// A minimal projector that renders items as `feat=cat` and joins an -/// antecedent with `&`. Useful for tests and ndjson smoke checks; real feeds -/// supply a domain projector that emits proper namespaced IRIs. +/// A minimal projector rendering items as `feat=cat`, joined with `&`. #[derive(Debug, Clone)] pub struct DebugProjector { /// Predicate IRI to stamp on every emitted implication. @@ -130,9 +137,9 @@ pub struct DebugProjector { impl Default for DebugProjector { fn default() -> Self { Self { - // Not in `ruff_spo_triplet`'s *current* closed vocabulary — see the - // synergy doc: flowing ARM rules through that loader needs an - // `implies` predicate added there first. + // Not in `ruff_spo_triplet`'s current closed vocabulary — see the + // synergy doc: the `ruff` loader needs an `implies` predicate added + // (D-ARM-SYN-1) before ARM rules flow through it. predicate: "implies".to_string(), } } @@ -164,58 +171,62 @@ impl FeedProjector for DebugProjector { mod tests { use super::*; - fn rule(support: f32, confidence: f32, n: u32) -> CandidateRule { + fn rule(cooccur: u32, antecedent_count: u32, window: u32) -> CandidateRule { CandidateRule { antecedent: vec![Item::new(0, 1)], consequent: vec![Item::new(1, 0)], - support, - confidence, - n, + cooccur, + antecedent_count, + window, } } #[test] - fn frequency_is_arm_confidence() { - let t = arm_to_nars(&rule(0.2, 0.83, 1000), NARS_PERSONALITY_K); - assert!((t.frequency - 0.83).abs() < 1e-6); + fn frequency_is_quantised_arm_confidence() { + // confidence = 249/300 ≈ 0.83 → 0.83×255 ≈ 211 + let t = arm_to_truth_u8(&rule(249, 300, 1000), NARS_PERSONALITY_K); + assert_eq!(t.frequency, ((249u64 * 255) / 300) as u8); } #[test] - fn confidence_grows_with_evidence() { - // m = support × n = 0.2 × 1000 = 200; c = 200/201 ≈ 0.995 - let strong = arm_to_nars(&rule(0.2, 0.83, 1000), NARS_PERSONALITY_K); - // m = 0.2 × 10 = 2; c = 2/3 ≈ 0.667 - let weak = arm_to_nars(&rule(0.2, 0.83, 10), NARS_PERSONALITY_K); + fn confidence_grows_with_evidence_integer() { + // m=200, k=1 → 200·255/201 = 253 + let strong = arm_to_truth_u8(&rule(200, 240, 1000), NARS_PERSONALITY_K); + // m=2, k=1 → 2·255/3 = 170 + let weak = arm_to_truth_u8(&rule(2, 3, 10), NARS_PERSONALITY_K); assert!(strong.confidence > weak.confidence); - assert!((strong.confidence - 200.0 / 201.0).abs() < 1e-4); - assert!((weak.confidence - 2.0 / 3.0).abs() < 1e-4); + assert_eq!(strong.confidence, ((200u64 * 255) / 201) as u8); + assert_eq!(weak.confidence, ((2u64 * 255) / 3) as u8); + } + + #[test] + fn larger_k_demands_more_evidence_integer() { + let k1 = arm_to_truth_u8(&rule(5, 10, 100), 1).confidence; + let k10 = arm_to_truth_u8(&rule(5, 10, 100), 10).confidence; + assert!(k10 < k1); } #[test] - fn larger_k_demands_more_evidence() { - let k1 = arm_to_nars(&rule(0.1, 0.7, 50), 1.0).confidence; - let k10 = arm_to_nars(&rule(0.1, 0.7, 50), 10.0).confidence; - assert!(k10 < k1, "larger k lowers confidence for the same evidence"); + fn zero_antecedent_is_unknown_frequency() { + let t = arm_to_truth_u8(&rule(0, 0, 1000), NARS_PERSONALITY_K); + assert_eq!(t.frequency, 128); + assert_eq!(t.confidence, 0); } #[test] - fn expectation_matches_spo_formula() { - let t = NarsTruth { - frequency: 0.9, - confidence: 0.8, - }; - // 0.8 * (0.9 - 0.5) + 0.5 = 0.82 - assert!((t.expectation() - 0.82).abs() < 1e-6); + fn f32_edge_is_derived_from_u8() { + let r = rule(100, 125, 400); // m=100; conf_u8 = 100·255/101 = 252 + let t8 = arm_to_truth_u8(&r, NARS_PERSONALITY_K); + let tf = arm_to_nars(&r, NARS_PERSONALITY_K); + assert!((tf.confidence - t8.confidence as f32 / 255.0).abs() < 1e-6); + assert!((tf.frequency - t8.frequency as f32 / 255.0).abs() < 1e-6); } #[test] - fn triple_projection_and_truth() { - let r = rule(0.25, 0.9, 400); // m = 100; c = 100/101 - let t = CandidateTriple::from_rule(&r, &DebugProjector::default(), NARS_PERSONALITY_K); + fn triple_projection() { + let t = CandidateTriple::from_rule(&rule(90, 100, 400), &DebugProjector::default(), NARS_PERSONALITY_K); assert_eq!(t.s, "arm:feat0=cat1"); assert_eq!(t.p, "implies"); assert_eq!(t.o, "arm:feat1=cat0"); - assert!((t.f - 0.9).abs() < 1e-6); - assert!((t.c - 100.0 / 101.0).abs() < 1e-4); } } From 3a52974615c1e5aef34b86743cc3e154bf42b445 Mon Sep 17 00:00:00 2001 From: Claude Date: Sat, 30 May 2026 19:33:43 +0000 Subject: [PATCH 5/7] perf(arm-discovery): bitset SoA + ndarray::simd::U64x8 for the AND+popcount count loop MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit The data-confirmation count loop (rows satisfying a conjunction of items) is the faiss-homology "SIMD batch-AND over the SoA facet column" workload. count_matching on the AoS Dataset is an O(rows x items) rescan, called once per candidate. - bitset.rs: RowMasks transposes the window once into one u64 bitset per (feature, category) item; every candidate count is then AND + popcount over &[u64]. 1- and 2-item conjunctions (the probe hot path) allocate nothing. - simd.rs: popcount / and_popcount. Scalar u64::count_ones by default (crate stays std-only and independently verifiable); the `ndarray-simd` feature routes through ndarray::simd::U64x8 (from_slice / & / popcnt / to_array) per ndarray-vertical-simd-alien-magic.md — zero raw intrinsics, zero cfg(target_arch) in this crate. - extract.rs: the probe counts via RowMasks instead of rescanning the dataset. Verified: default (scalar, zero-dep) 33/33 + clippy -D warnings clean; --features ndarray-simd 33/33 + clippy clean (ndarray builds as a path dep with default-features=false + std; ndarray-rand is not pulled). target-cpu: the U64x8 path is correct on any target but only emits the real vector kernels (AVX-512 VPOPCNTQ, AMX) under -C target-cpu=native or -C target-cpu=x86-64-v4; otherwise it is ndarray's scalar fused-loop fallback. https://claude.ai/code/session_012SorR8UbtEvYmbX8cXftj7 --- .claude/board/AGENT_LOG.md | 10 ++ crates/lance-graph-arm-discovery/Cargo.lock | 163 ++++++++++++++++++ crates/lance-graph-arm-discovery/Cargo.toml | 35 +++- crates/lance-graph-arm-discovery/README.md | 14 ++ .../src/aerial/extract.rs | 15 +- .../lance-graph-arm-discovery/src/bitset.rs | 150 ++++++++++++++++ crates/lance-graph-arm-discovery/src/lib.rs | 14 ++ crates/lance-graph-arm-discovery/src/simd.rs | 98 +++++++++++ 8 files changed, 487 insertions(+), 12 deletions(-) create mode 100644 crates/lance-graph-arm-discovery/src/bitset.rs create mode 100644 crates/lance-graph-arm-discovery/src/simd.rs diff --git a/.claude/board/AGENT_LOG.md b/.claude/board/AGENT_LOG.md index 91baae92..10b5bd58 100644 --- a/.claude/board/AGENT_LOG.md +++ b/.claude/board/AGENT_LOG.md @@ -1,3 +1,13 @@ +## [Main thread / Opus] D-ARM-13 SIMD seam — bitset SoA + ndarray::simd::U64x8 (AND+popcount) + +**Branch:** claude/jolly-cori-clnf9 | **Files:** `crates/lance-graph-arm-discovery/` — ADDED `bitset.rs` (`RowMasks` row-bitset SoA), `simd.rs` (`popcount`/`and_popcount`, scalar default + `ndarray-simd` feature); rewired `aerial/extract.rs` (probe counts via `RowMasks`, not AoS rescan); `lib.rs`, `Cargo.toml` (`ndarray-simd` feature + optional ndarray path dep, `default-features=false` + `std`), `README.md`. + +**Cargo:** DEFAULT (scalar, zero-dep) → **33/33**, clippy `-D warnings` clean. `--features ndarray-simd` → **33/33**, clippy clean (ndarray builds here as a path dep with `std`; `ndarray-rand` NOT pulled). + +**Outcome:** DONE. User directive: "use ndarray crate::simd::*". The data-confirmation count loop is the `faiss-homology` "SIMD batch-AND over the SoA facet column" workload. Transposed the window into one `u64` bitset per item (`RowMasks`), so every candidate count is `AND` + popcount over `&[u64]`. Per `ndarray-vertical-simd-alien-magic.md` (MANDATORY), the primitive routes through `ndarray::simd::U64x8` (`from_slice`/`&`/`popcnt`/`to_array`) — zero raw intrinsics, zero `cfg(target_arch)` in this crate; scalar `u64::count_ones` is the default so the crate stays std-only/verifiable. **target-cpu caveat** (per user): the real AVX-512 VPOPCNTQ / AMX kernels need `-C target-cpu=native` or `x86-64-v4`; otherwise it is ndarray's correct-but-scalar polyfill. The palette256 `CodebookDistance` oracle is SIMD on the consumer side (`bgz17::batch_palette_distance` / BLASGraph splat top-k). PR #436 updated. + +--- + ## [Main thread / Opus] D-ARM-13 de-float — autoencoder → deterministic codebook-probe (palette256) **Branch:** claude/jolly-cori-clnf9 | **Files:** diff --git a/crates/lance-graph-arm-discovery/Cargo.lock b/crates/lance-graph-arm-discovery/Cargo.lock index a6274758..1bcdfbbd 100644 --- a/crates/lance-graph-arm-discovery/Cargo.lock +++ b/crates/lance-graph-arm-discovery/Cargo.lock @@ -2,6 +2,169 @@ # It is not intended for manual editing. version = 4 +[[package]] +name = "arrayref" +version = "0.3.9" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "76a2e8124351fda1ef8aaaa3bbd7ebbcb486bbcd4225aca0aa0d84bb2db8fecb" + +[[package]] +name = "arrayvec" +version = "0.7.6" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "7c02d123df017efcdfbd739ef81735b36c5ba83ec3c59c80a9d7ecc718f92e50" + +[[package]] +name = "autocfg" +version = "1.5.1" +source = "registry+https://github.com/rust-lang/crates.io-index" 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"2.0.1" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "f8fadd59c855ef2080decdef8ff161eb6661b86933c9d82e5ba29dc602a55aba" diff --git a/crates/lance-graph-arm-discovery/Cargo.toml b/crates/lance-graph-arm-discovery/Cargo.toml index bd8fae30..6937fbd9 100644 --- a/crates/lance-graph-arm-discovery/Cargo.toml +++ b/crates/lance-graph-arm-discovery/Cargo.toml @@ -8,19 +8,38 @@ edition = "2021" description = "Deterministic codebook-probe association-rule discovery (Aerial+ transcode) → NARS-truth SPO candidates" license = "Apache-2.0" -# Standalone, zero-dependency crate — EXCLUDED from the workspace (see root -# Cargo.toml `exclude`), like bgz17 / deepnsm / bgz-tensor. Verified with: +# Standalone, zero-dependency crate by default — EXCLUDED from the workspace +# (see root Cargo.toml `exclude`), like bgz17 / deepnsm / bgz-tensor. Verified +# with the DEFAULT (scalar, std-only) feature set: # # cargo test --manifest-path crates/lance-graph-arm-discovery/Cargo.toml # # The discovery path is float-free and bitwise-deterministic (integer codebook -# distance, integer evidence counts). The similarity oracle is injected through -# the zero-dep `aerial::CodebookDistance` trait, so the real distance table -# (`bgz17::PaletteDistanceTable`, the BLASGraph splat top-k, or an HDR-popcount -# Hamming primitive) lives on the consumer side and this crate stays standalone. +# distance, integer evidence counts, AND+popcount over row bitsets). -[dependencies] -# none — std only. +[features] +default = [] +# Route the bitset AND+popcount primitive (`simd.rs`) through +# `ndarray::simd::U64x8` per `ndarray-vertical-simd-alien-magic.md`. Verified +# here: `cargo test/clippy --features ndarray-simd` → 33/33, clippy clean +# (ndarray builds as a path dep with default-features=false + std; the +# `ndarray-rand` workspace member is NOT pulled). +# +# TARGET-CPU: the U64x8 polyfill is CORRECT on any target but only emits the +# real vector kernels (AVX-512 VPOPCNTQ for popcnt, AMX for the matmul/VNNI +# paths) when built for native silicon. Build with +# RUSTFLAGS="-C target-cpu=native" (this machine), or +# RUSTFLAGS="-C target-cpu=x86-64-v4" (AVX-512 baseline) +# Without it the popcnt path is the scalar fused-loop fallback — same result, +# no SIMD speedup. The default (scalar) feature does not pull ndarray at all. +ndarray-simd = ["dep:ndarray"] + +[dependencies.ndarray] +path = "../../../ndarray" +optional = true +default-features = false +# `std` gates `ndarray::simd` (the `U64x8` polyfill); that is all we need. +features = ["std"] [lib] doctest = true diff --git a/crates/lance-graph-arm-discovery/README.md b/crates/lance-graph-arm-discovery/README.md index a50b11d6..534812ac 100644 --- a/crates/lance-graph-arm-discovery/README.md +++ b/crates/lance-graph-arm-discovery/README.md @@ -71,10 +71,24 @@ The Python-IPC isolation rationale (D-ARM-9) is fully moot. ## Build & test ```bash +# Default: scalar, std-only, zero-dep (independently verifiable) cargo test --manifest-path crates/lance-graph-arm-discovery/Cargo.toml cargo clippy --manifest-path crates/lance-graph-arm-discovery/Cargo.toml --all-targets -- -D warnings + +# SIMD: route the bitset AND+popcount through ndarray::simd::U64x8 +cargo test --manifest-path crates/lance-graph-arm-discovery/Cargo.toml --features ndarray-simd ``` +The count loop transposes the window into row bitsets (`bitset::RowMasks`) so +every candidate count is an `AND` + popcount over `&[u64]` (`simd.rs`). The +default path is scalar `u64::count_ones`; the `ndarray-simd` feature swaps in +`ndarray::simd::U64x8` (zero raw intrinsics here — the polyfill owns dispatch). + +**target-cpu:** the `U64x8` path is correct everywhere but only emits the real +vector kernels (AVX-512 VPOPCNTQ, AMX) when built for native silicon — +`RUSTFLAGS="-C target-cpu=native"` or `-C target-cpu=x86-64-v4`. Otherwise it is +ndarray's scalar fused-loop fallback (same result, no speedup). + ## Status Implements the Aerial+ leg of the plan as a deterministic codebook backend diff --git a/crates/lance-graph-arm-discovery/src/aerial/extract.rs b/crates/lance-graph-arm-discovery/src/aerial/extract.rs index db1572b8..0177a538 100644 --- a/crates/lance-graph-arm-discovery/src/aerial/extract.rs +++ b/crates/lance-graph-arm-discovery/src/aerial/extract.rs @@ -22,6 +22,7 @@ //! `P(Y|X) ≈ P(Y)` for independent `Y`. No softmax, no seed, no `f32`. use crate::aerial::codebook::{antecedent_distance, CodebookDistance}; +use crate::bitset::RowMasks; use crate::encode::Dataset; use crate::rule::{CandidateRule, Item}; @@ -66,13 +67,17 @@ pub fn extract_rules( return Vec::new(); } + // Transpose the window into row bitsets once; every count below is then an + // AND + popcount (the `ndarray::simd` seam), not an AoS rescan. + let masks = RowMasks::build(data); + // Frequent single items (support floor) — the apriori prune that bounds // the antecedent search, grouped by feature. Integer ppm. let mut frequent_by_feature: Vec> = vec![Vec::new(); spec.num_features()]; for (f, bucket) in frequent_by_feature.iter_mut().enumerate() { for cat in 0..spec.cardinality(f) { let item = Item::new(f as u32, cat); - let count = data.count_matching(&[item]); + let count = masks.support_count(item); let support_ppm = ((count as u64 * crate::rule::PPM) / n as u64) as u32; if support_ppm >= params.min_support_ppm { bucket.push(item); @@ -88,7 +93,7 @@ pub fn extract_rules( for size in 1..=max_ant { for feature_combo in feature_combinations(&candidate_features, size) { for antecedent in item_product(&feature_combo, &frequent_by_feature) { - probe(oracle, data, &antecedent, &feature_combo, params, &mut rules); + probe(oracle, data, &masks, &antecedent, &feature_combo, params, &mut rules); } } } @@ -105,9 +110,11 @@ pub fn extract_rules( /// Probe one antecedent: for each other feature, take the codebook-nearest /// category; if within `theta`, confirm on data and emit if it clears the /// ARM floors. +#[allow(clippy::too_many_arguments)] fn probe( oracle: &dyn CodebookDistance, data: &Dataset, + masks: &RowMasks, antecedent: &[Item], antecedent_features: &[usize], params: &ExtractParams, @@ -115,7 +122,7 @@ fn probe( ) { let spec = &data.spec; let n = data.len() as u32; - let antecedent_count = data.count_matching(antecedent); + let antecedent_count = masks.and_count(antecedent); if antecedent_count == 0 { return; } @@ -140,7 +147,7 @@ fn probe( let consequent = vec![Item::new(g as u32, best_cat)]; let mut both = antecedent.to_vec(); both.extend_from_slice(&consequent); - let cooccur = data.count_matching(&both); + let cooccur = masks.and_count(&both); let rule = CandidateRule { antecedent: antecedent.to_vec(), diff --git a/crates/lance-graph-arm-discovery/src/bitset.rs b/crates/lance-graph-arm-discovery/src/bitset.rs new file mode 100644 index 00000000..43c61bdb --- /dev/null +++ b/crates/lance-graph-arm-discovery/src/bitset.rs @@ -0,0 +1,150 @@ +// SPDX-License-Identifier: Apache-2.0 +// SPDX-FileCopyrightText: Copyright The Lance Authors + +//! Row-bitset SoA — the columnar form the codebook probe counts over. +//! +//! `count_matching` on the AoS `Dataset` is an `O(rows × items)` scan. The +//! probe calls it once per candidate, so the window is rescanned thousands of +//! times. [`RowMasks`] transposes the window once into one `u64` bitset per +//! `(feature, category)` item (bit `r` set iff row `r` has that category); +//! then every candidate's support/co-occurrence count is an `AND` + popcount +//! over `&[u64]` — the [`crate::simd`] primitive that routes through +//! `ndarray::simd::U64x8` under the `ndarray-simd` feature. + +use crate::encode::Dataset; +use crate::rule::Item; +use crate::simd::{and_popcount, popcount}; + +/// One `u64` bitset per item slot, over the window's rows. +#[derive(Debug, Clone)] +pub struct RowMasks { + /// Rows in the window. + n: usize, + /// `u64` words per mask = `ceil(n / 64)`. + words: usize, + /// Per-feature code offset (`offsets[feature] + category` = item slot). + offsets: Vec, + /// Flat `dim × words` storage; mask of slot `s` is `[s·words .. (s+1)·words)`. + masks: Vec, +} + +impl RowMasks { + /// Transpose a dataset into row bitsets (one pass over the window). + #[must_use] + pub fn build(data: &Dataset) -> Self { + let spec = &data.spec; + let dim = spec.dim(); + let n = data.len(); + let words = n.div_ceil(64).max(1); + let offsets: Vec = (0..spec.num_features()).map(|f| spec.block(f).0).collect(); + let mut masks = vec![0u64; dim * words]; + for (r, row) in data.rows.iter().enumerate() { + let w = r / 64; + let bit = 1u64 << (r % 64); + for (f, &cat) in row.iter().enumerate() { + let slot = offsets[f] + cat as usize; + masks[slot * words + w] |= bit; + } + } + Self { + n, + words, + offsets, + masks, + } + } + + /// Window size `n`. + #[must_use] + pub fn len(&self) -> usize { + self.n + } + + /// Whether the window is empty. + #[must_use] + pub fn is_empty(&self) -> bool { + self.n == 0 + } + + /// The bitset of an item. + #[must_use] + fn mask(&self, item: Item) -> &[u64] { + let slot = self.offsets[item.feature as usize] + item.category as usize; + &self.masks[slot * self.words..(slot + 1) * self.words] + } + + /// `|{rows ⊇ {item}}|` — single-item support count. + #[must_use] + pub fn support_count(&self, item: Item) -> u32 { + popcount(self.mask(item)) + } + + /// `|{rows ⊇ items}|` — conjunction count (the evidence primitive). + /// The 1- and 2-item cases (the probe's hot path: a 1–2 item antecedent + /// plus one consequent) avoid any allocation. + #[must_use] + pub fn and_count(&self, items: &[Item]) -> u32 { + match items { + [] => self.n as u32, + [a] => self.support_count(*a), + [a, b] => and_popcount(self.mask(*a), self.mask(*b)), + [a, rest @ ..] => { + let mut acc = self.mask(*a).to_vec(); + for &it in rest { + for (x, &y) in acc.iter_mut().zip(self.mask(it)) { + *x &= y; + } + } + popcount(&acc) + } + } + } +} + +#[cfg(test)] +mod tests { + use super::*; + use crate::encode::FeatureSpec; + + fn data() -> Dataset { + // rows: (0,0),(0,0),(0,1),(1,1) + let spec = FeatureSpec::new(vec![2, 2]); + Dataset::new(spec, vec![vec![0, 0], vec![0, 0], vec![0, 1], vec![1, 1]]) + } + + #[test] + fn masks_match_aos_count() { + let d = data(); + let m = RowMasks::build(&d); + // single-item: feature0=0 in 3 rows + assert_eq!(m.support_count(Item::new(0, 0)), 3); + assert_eq!(m.support_count(Item::new(0, 0)), d.count_matching(&[Item::new(0, 0)])); + // conjunction f0=0 ∧ f1=0 → 2 rows + let both = [Item::new(0, 0), Item::new(1, 0)]; + assert_eq!(m.and_count(&both), 2); + assert_eq!(m.and_count(&both), d.count_matching(&both)); + // empty conjunction = n + assert_eq!(m.and_count(&[]), 4); + } + + #[test] + fn three_item_conjunction_folds() { + let spec = FeatureSpec::new(vec![2, 2, 2]); + let d = Dataset::new(spec, vec![vec![0, 0, 0], vec![0, 0, 1], vec![0, 0, 0]]); + let m = RowMasks::build(&d); + let items = [Item::new(0, 0), Item::new(1, 0), Item::new(2, 0)]; + assert_eq!(m.and_count(&items), 2); + assert_eq!(m.and_count(&items), d.count_matching(&items)); + } + + #[test] + fn spans_multiple_words() { + // 130 rows → 3 u64 words per mask; every row has feature0=0. + let spec = FeatureSpec::new(vec![1, 2]); + let rows: Vec> = (0..130).map(|i| vec![0, (i % 2) as u32]).collect(); + let d = Dataset::new(spec, rows); + let m = RowMasks::build(&d); + assert_eq!(m.support_count(Item::new(0, 0)), 130); + assert_eq!(m.support_count(Item::new(1, 0)), 65); + } +} diff --git a/crates/lance-graph-arm-discovery/src/lib.rs b/crates/lance-graph-arm-discovery/src/lib.rs index 68359d51..96f8545f 100644 --- a/crates/lance-graph-arm-discovery/src/lib.rs +++ b/crates/lance-graph-arm-discovery/src/lib.rs @@ -46,18 +46,32 @@ //! pair-stats (D-ARM-3). The ratification council still governs *promotion to //! the SPO store*, but no longer because of any nondeterminism here. +//! # SIMD seam +//! +//! The data-confirmation count loop transposes the window into row bitsets +//! ([`bitset::RowMasks`]) so every candidate's count is an `AND` + popcount +//! over `&[u64]` ([`simd`]). The default path is scalar (`u64::count_ones`), +//! keeping the crate std-only and independently verifiable; the optional +//! `ndarray-simd` feature routes that primitive through `ndarray::simd::U64x8` +//! per `.claude/knowledge/ndarray-vertical-simd-alien-magic.md` (zero raw +//! intrinsics in this crate). The palette256 [`aerial::CodebookDistance`] +//! oracle is similarly SIMD on the consumer side (`bgz17::batch_palette_distance`). + #![forbid(unsafe_code)] pub mod aerial; +pub mod bitset; pub mod encode; pub mod ndjson; pub mod rule; +pub mod simd; pub mod translator; pub use aerial::{ antecedent_distance, extract_rules, AerialParams, AerialProposer, CodebookDistance, ExtractParams, MatrixDistance, }; +pub use bitset::RowMasks; pub use encode::{Dataset, FeatureSpec}; pub use rule::{CandidateRule, Item, Proposer, PPM}; pub use translator::{ diff --git a/crates/lance-graph-arm-discovery/src/simd.rs b/crates/lance-graph-arm-discovery/src/simd.rs new file mode 100644 index 00000000..445d7a55 --- /dev/null +++ b/crates/lance-graph-arm-discovery/src/simd.rs @@ -0,0 +1,98 @@ +// SPDX-License-Identifier: Apache-2.0 +// SPDX-FileCopyrightText: Copyright The Lance Authors + +//! Bitset population-count primitives — the SIMD seam. +//! +//! The data-confirmation hot loop (counting rows that satisfy a conjunction of +//! items) is the `faiss-homology` "SIMD batch-AND over the SoA facet column" +//! workload. With the row-bitset SoA ([`crate::bitset::RowMasks`]) it reduces +//! to `AND` + popcount over `&[u64]`. +//! +//! Per `.claude/knowledge/ndarray-vertical-simd-alien-magic.md` (MANDATORY for +//! consumer SIMD), this routes through **`ndarray::simd::U64x8`** — zero raw +//! intrinsics, zero `cfg(target_arch)`, zero feature detection in this crate; +//! the polyfill owns dispatch. The scalar path is the default (so the crate +//! stays std-only and independently verifiable); the `ndarray-simd` feature +//! swaps in the vectorised path. + +/// Population count of a bitset (`Σ count_ones`). +#[cfg(not(feature = "ndarray-simd"))] +#[must_use] +pub fn popcount(words: &[u64]) -> u32 { + words.iter().map(|w| w.count_ones()).sum() +} + +/// Population count of `a AND b` (the co-occurrence count of two item masks). +#[cfg(not(feature = "ndarray-simd"))] +#[must_use] +pub fn and_popcount(a: &[u64], b: &[u64]) -> u32 { + a.iter().zip(b).map(|(x, y)| (x & y).count_ones()).sum() +} + +// ── ndarray::simd vectorised path (feature `ndarray-simd`) ────────────────── +// +// Uses ONLY `ndarray::simd::U64x8` (`from_slice` + `&` (BitAnd) + `popcnt` + +// `to_array`) — the canonical W1b consumer surface, zero raw intrinsics. +// Verified: `cargo test/clippy --features ndarray-simd` → 33/33, clippy clean. +// The real AVX-512 VPOPCNTQ / AMX kernels need `-C target-cpu=native` or +// `x86-64-v4`; otherwise this is ndarray's scalar polyfill (correct, not +// accelerated). + +/// Population count of a bitset, via `ndarray::simd::U64x8::popcnt`. +#[cfg(feature = "ndarray-simd")] +#[must_use] +pub fn popcount(words: &[u64]) -> u32 { + use ndarray::simd::U64x8; + let chunks = words.len() / 8; + let mut acc = 0u64; + for i in 0..chunks { + acc += U64x8::from_slice(&words[i * 8..i * 8 + 8]) + .popcnt() + .to_array() + .iter() + .sum::(); + } + for &w in &words[chunks * 8..] { + acc += u64::from(w.count_ones()); + } + acc as u32 +} + +/// Population count of `a AND b`, via `ndarray::simd::U64x8`. +#[cfg(feature = "ndarray-simd")] +#[must_use] +pub fn and_popcount(a: &[u64], b: &[u64]) -> u32 { + use ndarray::simd::U64x8; + let chunks = a.len() / 8; + let mut acc = 0u64; + for i in 0..chunks { + let va = U64x8::from_slice(&a[i * 8..i * 8 + 8]); + let vb = U64x8::from_slice(&b[i * 8..i * 8 + 8]); + acc += (va & vb).popcnt().to_array().iter().sum::(); + } + for i in (chunks * 8)..a.len() { + acc += u64::from((a[i] & b[i]).count_ones()); + } + acc as u32 +} + +#[cfg(test)] +mod tests { + use super::*; + + #[test] + fn popcount_counts_set_bits() { + assert_eq!(popcount(&[0u64]), 0); + assert_eq!(popcount(&[u64::MAX]), 64); + assert_eq!(popcount(&[0b1011, 0b11]), 5); // 3 + 2 set bits + // exercise the 8-word vector chunk + tail + assert_eq!(popcount(&[u64::MAX; 9]), 64 * 9); + } + + #[test] + fn and_popcount_is_conjunction_count() { + assert_eq!(and_popcount(&[0b1100], &[0b1010]), 1); // only bit 3 shared + assert_eq!(and_popcount(&[u64::MAX; 9], &[u64::MAX; 9]), 64 * 9); + assert_eq!(and_popcount(&[u64::MAX; 9], &[0u64; 9]), 0); + } +} From fd809c69557d9768f39f30495af0adac28858617 Mon Sep 17 00:00:00 2001 From: Claude Date: Sat, 30 May 2026 19:41:08 +0000 Subject: [PATCH 6/7] =?UTF-8?q?docs(arm-discovery):=20map=20the=20splat-co?= =?UTF-8?q?debook=20=E2=86=92=20aerial=20=E2=86=92=20Wikidata=20wiring;=20?= =?UTF-8?q?jc=20resolves=20both=20aerial=20seams?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Architecture synthesis (no new code — the seam already exists). Grounds the user's direction (wire the 10000^2 Gaussian-splat BLASGraph top-k as the aerial oracle for OWL/DOLCE+ SPO HHTL class/basin discovery -> deterministic Wikidata compression, adjacent to the jc EWA-sandwich splat) against real workspace artifacts. Finding (E-ARM-JC-RESOLVES-BOTH-SEAMS): aerial's two open seams both resolve to crates/jc (Jirak-Cartan): - CodebookDistance oracle <- jc builds + certifies the frozen [u32;dim^2] table offline: ewa_sandwich{,_3d} (splat Sigma-push-forward for ndarray::hpc::splat3d), sigma_codebook_probe (the rho=0.9973 source: 256-codebook R^2>=0.99 viability), pflug (CAM-PQ/HHTL Le-faithful). - D-ARM-7 Jirak floor <- jc::jirak (weak-dependence Berry-Esseen rate n^(p/2-1)). Float boundary = CAM-PQ doctrine end-to-end: float lives ONLY in jc's offline build+certify; it emits a frozen integer artifact; aerial's online path stays integer. The "deterministically compress Wikidata" claim is float-free at runtime. No new aerial dependency — the certified table feeds the existing MatrixDistance /CodebookDistance seam. - knowledge/splat-codebook-aerial-wikidata-compression.md: the full wiring map - EPIPHANIES: E-ARM-JC-RESOLVES-BOTH-SEAMS - STATUS_BOARD: D-ARM-7 engine pointer (jc::jirak), D-ARM-13 -> 33/33 + SIMD clause, new D-ARM-14 (splat-codebook oracle + Wikidata skeleton discovery) - aerial/codebook.rs: oracle doc names jc + the offline-float/online-integer boundary https://claude.ai/code/session_012SorR8UbtEvYmbX8cXftj7 --- .claude/board/AGENT_LOG.md | 8 ++ .claude/board/EPIPHANIES.md | 13 ++ .claude/board/STATUS_BOARD.md | 5 +- ...at-codebook-aerial-wikidata-compression.md | 111 ++++++++++++++++++ .../src/aerial/codebook.rs | 13 +- 5 files changed, 144 insertions(+), 6 deletions(-) create mode 100644 .claude/knowledge/splat-codebook-aerial-wikidata-compression.md diff --git a/.claude/board/AGENT_LOG.md b/.claude/board/AGENT_LOG.md index 10b5bd58..bc595147 100644 --- a/.claude/board/AGENT_LOG.md +++ b/.claude/board/AGENT_LOG.md @@ -1,3 +1,11 @@ +## [Main thread / Opus] Splat-codebook ↔ aerial ↔ Wikidata wiring — jc resolves both aerial seams + +**Branch:** claude/jolly-cori-clnf9 | **Files:** ADDED `.claude/knowledge/splat-codebook-aerial-wikidata-compression.md`; EPIPHANIES (E-ARM-JC-RESOLVES-BOTH-SEAMS); STATUS_BOARD (D-ARM-7 engine pointer, D-ARM-13 → 33/33 + SIMD clause, new D-ARM-14); `aerial/codebook.rs` oracle doc names jc + the float boundary; AGENT_LOG. + +**Outcome:** ANALYSIS + doc (no new code — the seam already exists). User direction: wire the 10000² Gaussian-splat BLASGraph top-k as the aerial oracle for OWL/DOLCE+ SPO HHTL class/basin discovery → deterministic Wikidata compression, adjacent to jc (Jirak-Cartan) EWA-sandwich splat. Grounded it against real workspace artifacts (consult-before-guess): `crates/jc` is "Jirak-Cartan" ("candar"≈Cartan), holding `ewa_sandwich{,_3d}` (Pillars 9/9b: splat Σ-push-forward for `ndarray::hpc::splat3d`), `sigma_codebook_probe` (**the ρ=0.9973 source** — 256-codebook R²≥0.99 viability), `pflug` (Pillar 10: CAM-PQ/HHTL Lε-faithful), `jirak` (Pillar 5: weak-dep Berry-Esseen). FINDING (E-ARM-JC-RESOLVES-BOTH-SEAMS): aerial's two open seams — the production `CodebookDistance` oracle AND the D-ARM-7 Jirak floor — **both resolve to jc**. jc PROVES the codebook (builds + certifies the frozen `[u32;dim²]` table offline, float OK); aerial USES it online (integer) to discover the `wikidata-hhtl-load.md` skeleton (P279/P31 DAG + basins, DOLCE as axis template). Float boundary = CAM-PQ doctrine end-to-end: build offline (float), address online (integer); the runtime stays float-free. No new aerial dependency — pass the table through the existing `MatrixDistance` seam. Confirmed user's target-cpu point (AVX-512/AMX need native/x86-64-v4) — already in the SIMD commit. PR #436 updated. + +--- + ## [Main thread / Opus] D-ARM-13 SIMD seam — bitset SoA + ndarray::simd::U64x8 (AND+popcount) **Branch:** claude/jolly-cori-clnf9 | **Files:** `crates/lance-graph-arm-discovery/` — ADDED `bitset.rs` (`RowMasks` row-bitset SoA), `simd.rs` (`popcount`/`and_popcount`, scalar default + `ndarray-simd` feature); rewired `aerial/extract.rs` (probe counts via `RowMasks`, not AoS rescan); `lib.rs`, `Cargo.toml` (`ndarray-simd` feature + optional ndarray path dep, `default-features=false` + `std`), `README.md`. diff --git a/.claude/board/EPIPHANIES.md b/.claude/board/EPIPHANIES.md index 0a6ac034..8426d570 100644 --- a/.claude/board/EPIPHANIES.md +++ b/.claude/board/EPIPHANIES.md @@ -1,3 +1,16 @@ +## 2026-05-30 — E-ARM-JC-RESOLVES-BOTH-SEAMS — aerial's two open seams (the distance oracle AND the D-ARM-7 Jirak floor) both resolve to `crates/jc`; jc PROVES the splat codebook, aerial USES it to discover the DOLCE skeleton that compresses Wikidata + +**Status:** FINDING (architecture; seams concrete, end-to-end pipeline is CONJECTURE). User framing: "gaussian-splat spatial blasgraph top-k 10000×10000 … for OWL/DOLCE+ SPO HHTL classes and basin via aerial+ to deterministically compress Wikidata … adjacent to JC Jirak[-Cartan] with EWA-sandwich gaussian splat." + +The de-float (E-ARM-PROBE-IS-CODEBOOK-TOPK) left aerial with two open seams: a production `CodebookDistance` oracle, and the unimplemented D-ARM-7 Jirak significance floor (ISSUE ARM-JIRAK-FLOOR). **Both resolve to the same crate — `crates/jc` (Jirak-Cartan)** — and the grounding is already in the workspace, not invented: + +- **Oracle ← jc.** `jc::ewa_sandwich{,_3d}` (Pillars 9/9b) certify the Gaussian-splat Σ-push-forward `J·W·Σ·Wᵀ·Jᵀ` for `ndarray::hpc::splat3d` — i.e. the 10000² BLASGraph spatial top-k that *builds* the codebook is correct. `jc::sigma_codebook_probe` is **literally where ρ=0.9973 comes from**: it measures a 256-codebook capturing the Σ-distribution at R²≥0.99 in log-Euclidean space. `jc::pflug` (Pillar 10) certifies the CAM-PQ/HHTL tree quantization is Lε-faithful (compression is faithful, not lossy-by-surprise). The frozen `[u32; dim²]` table feeds aerial's existing `MatrixDistance`/`CodebookDistance` seam — no new aerial dependency. +- **Jirak floor ← jc.** `jc::jirak` (Pillar 5) is the weak-dependence Berry-Esseen engine: classical IID is wrong here (I-NOISE-FLOOR-JIRAK), the correct rate is `n^(p/2-1)`. D-ARM-7's significance gate derives its threshold from it. + +**The float boundary is the punchline.** float lives ONLY in jc's offline build+certify (k-means, SPD/EWA math, Berry-Esseen sup-error); it runs once, emits a frozen integer artifact, and is never on aerial's online path. That is the CAM-PQ doctrine end-to-end — *build the codebook offline (float OK), address it online with integer codes* — and it makes the whole "deterministically compress Wikidata" claim float-free at runtime. Downstream: aerial discovers the OWL/DOLCE+ SPO HHTL classes + basins (the `specs/wikidata-hhtl-load.md` skeleton: P279/P31 DAG + basin assignment, DOLCE as the axis template), the Jirak floor decides which rules are significant enough to persist, and codebook-HHTL is the 16ⁿ bucket router. Full map: `.claude/knowledge/splat-codebook-aerial-wikidata-compression.md`. Cross-ref: jc pillars 5/9/9b/10, `wikidata-hhtl-load.md`, `ogit-owl-dolce-ontology-compartments.md`, `3DGS-HHTL-datalake-traversal-plan.md`, E-ARM-PROBE-IS-CODEBOOK-TOPK. + +--- + ## 2026-05-30 — E-ARM-PROBE-IS-CODEBOOK-TOPK — Aerial+'s reconstruction probe IS a codebook top-k; the autoencoder was a float approximation of a lookup the substrate already does exactly; de-floating it dissolves the determinism firewall **Status:** FINDING (de-float shipped on `claude/jolly-cori-clnf9`, 28/28 tests, zero f32 in the discovery path). User-directed: "neither cam_pq nor any other crate uses (or should) float … all is deterministic [a,b] codebook distance, ρ=0.9973 spearman." diff --git a/.claude/board/STATUS_BOARD.md b/.claude/board/STATUS_BOARD.md index f12a45e3..3954f13e 100644 --- a/.claude/board/STATUS_BOARD.md +++ b/.claude/board/STATUS_BOARD.md @@ -627,13 +627,14 @@ The missing UPSTREAM discovery leg. Today's proposers (curated L-docs + AST-extr | D-ARM-4 | ARM-truth → NARS-truth translator + Odoo `FeedProjector` impl | lance-graph-arm-discovery::translator | 200 | HIGH | **Partially shipped (branch)** | The translator substance landed early inside D-ARM-13: `translator::{arm_to_nars, NarsTruth, CandidateTriple, FeedProjector}` (verbatim paper §2/§3.3 mapping, 35/35 tests). REMAINING: the real Odoo `FeedProjector` (currently a `DebugProjector` stub emitting `implies`) + contract homing on D-ARM-1/2. Depends on D-ARM-1/2. | | D-ARM-5 | Hypothesis test: SpoStore round-trip, NARS revision, contradiction commit per The Click | lance-graph-arm-discovery::hypothesis | 350 | MED | **Queued** | depends on D-ARM-4; verifies `spo::truth::Contradiction` primitive exists | | D-ARM-6 | `RatificationQueue` ring buffer + corrections-to-#434 spec PR (`discovery_arc D=8`, `discovery_origin u8`) | lance-graph-arm-discovery::queue + #434 spec follow-up | 200 + spec | MED | **Queued** | depends on PR #434 D-MBX-A3 landing | -| D-ARM-7 | Jirak-2016 weak-dependence significance thresholds (mandatory Stage A floor) | lance-graph-arm-discovery::jirak | 150 | HIGH | **Queued — HARD PREREQUISITE** | blocks D-ARM-3; cites I-NOISE-FLOOR-JIRAK. **ISSUE ARM-JIRAK-FLOOR (2026-05-30, 3-savant review):** D-ARM-13 ships the Aerial proposer with NO Jirak floor (classical `min_support`/`min_confidence` only). MUST land before D-ARM-5 wires the proposer to a live `SpoStore`, else the substrate calcifies on thin-but-frequent noise (plan §11.1). | +| D-ARM-7 | Jirak-2016 weak-dependence significance thresholds (mandatory Stage A floor) | lance-graph-arm-discovery::jirak | 150 | HIGH | **Queued — HARD PREREQUISITE** | blocks D-ARM-3; cites I-NOISE-FLOOR-JIRAK. **ISSUE ARM-JIRAK-FLOOR (2026-05-30, 3-savant review):** D-ARM-13 ships the Aerial proposer with NO Jirak floor (classical `min_support`/`min_confidence` only). MUST land before D-ARM-5 wires the proposer to a live `SpoStore`, else the substrate calcifies on thin-but-frequent noise (plan §11.1). **ENGINE EXISTS:** `jc::jirak` (Jirak-Cartan Pillar 5) is the weak-dependence Berry-Esseen rate (`n^(p/2-1)`); this deliverable is the *gate function* (rule → significant?) that derives its threshold from it — NOT a from-scratch Jirak impl. See E-ARM-JC-RESOLVES-BOTH-SEAMS + `splat-codebook-aerial-wikidata-compression.md`. | | D-ARM-8 | `Feed` + `FeedProjector` + window-size config + Odoo `account.move` projector example | lance-graph-arm-discovery::feed | 250 | MED | **Queued** | depends on D-ARM-2 | | D-ARM-9 | Aerial+ IPC client (feature-gated `arm-aerial`, NDJSON over Unix socket) | lance-graph-arm-discovery::proposer::aerial_ipc | 200 | MED | **Superseded by D-ARM-13** | The native in-process Aerial+ transcode (D-ARM-13, branch `claude/jolly-cori-clnf9`) replaces the need for the Python IPC client. The determinism-boundary rationale the IPC was designed for (keep the nondeterministic autoencoder out of the Rust path) is now met in-process via seed (`aerial::Rng`) + `aerial` feature gate + workspace `exclude`. Keep this row ONLY if a Python-only Aerial variant is later required; otherwise close as Abandoned-by-replacement. | | D-ARM-10 | `op_emitter::bucket_corpus` ratification filter (`confidence ≥ Ratified`) + 2 tests | lance-graph-ontology::op_emitter | 30 | HIGH | **Queued** | depends on D-ARM-1 | | D-ARM-11 | `style_recipe.rs` rule 8 — ArmDiscovered backing adds `DAtom::Compute` weight 2 (provisional) | lance-graph-ontology::style_recipe | 80 | MED | **Queued** | depends on D-ARM-1 | | D-ARM-12 | End-to-end pipeline test + bench (synthetic Odoo feed → all 5 stages → council micro-batch) | lance-graph-arm-discovery::tests + benches | 400 | MED | **Queued** | depends on Waves 1-6; informs OQ-ARM-2 + OQ-ARM-7 | -| D-ARM-13 | **Aerial+ Rust transcode — deterministic codebook-probe backend** (float-free). The paper's `f32` denoising autoencoder is REPLACED by an integer `CodebookDistance` oracle (palette256 distance, ρ=0.9973 vs cosine): the reconstruction probe is a codebook top-k, not a softmax over float weights. Integer evidence counts + ppm gates + `TruthU8` (= CausalEdge64 wire). `AerialProposer` impl of `Proposer`. | lance-graph-arm-discovery::aerial | ~1.0K | HIGH | **Shipped (branch)** | branch `claude/jolly-cori-clnf9`; standalone zero-dep crate (excluded); 28/28 tests, clippy `-D warnings` clean; **zero f32 in the discovery path** (audit), float only at the `TruthValue`/`Triple` serialization edge. Bitwise-deterministic ⇒ joins the trunk; the nondeterminism firewall + D-ARM-9 IPC rationale are moot. v1 (autoencoder) superseded per the user's no-float directive. | +| D-ARM-13 | **Aerial+ Rust transcode — deterministic codebook-probe backend** (float-free). The paper's `f32` denoising autoencoder is REPLACED by an integer `CodebookDistance` oracle (palette256 distance, ρ=0.9973 vs cosine): the reconstruction probe is a codebook top-k, not a softmax over float weights. Integer evidence counts + ppm gates + `TruthU8` (= CausalEdge64 wire). `AerialProposer` impl of `Proposer`. Count loop is a row-bitset SoA (`RowMasks`) → AND+popcount, routed through `ndarray::simd::U64x8` under the `ndarray-simd` feature. | lance-graph-arm-discovery::aerial | ~1.1K | HIGH | **Shipped (branch)** | branch `claude/jolly-cori-clnf9`; standalone zero-dep crate (excluded); **33/33** tests + clippy `-D warnings` clean on BOTH default (scalar) and `--features ndarray-simd`; **zero f32 in the discovery path** (audit), float only at the `TruthValue`/`Triple` serialization edge. Bitwise-deterministic ⇒ joins the trunk; the nondeterminism firewall + D-ARM-9 IPC rationale are moot. SIMD target-cpu caveat: real AVX-512/AMX kernels need `-C target-cpu=native`/`x86-64-v4`. v1 (autoencoder) superseded per the user's no-float directive. | +| D-ARM-14 | **Splat-codebook oracle + Wikidata skeleton discovery** — wire the certified jc splat codebook into aerial as the `CodebookDistance` oracle, discover OWL/DOLCE+ SPO HHTL classes + basins, drive the `wikidata-hhtl-load.md` deterministic compression (skeleton + basins + CAM-dedup + thin rows). | lance-graph-arm-discovery::aerial + crates/jc + wikidata loader | ~? | MED | **Queued (architecture)** | CONJECTURE pipeline; seams concrete (`CodebookDistance` ← jc `[u32;dim²]` table built by `ewa_sandwich`+`sigma_codebook_probe` ρ=0.9973+`pflug` Lε; Jirak floor ← `jc::jirak`). Float lives OFFLINE in jc only; aerial online path integer. No new aerial dep (pass the frozen table in). Gated on D-ARM-7. Full map: `splat-codebook-aerial-wikidata-compression.md`; finding E-ARM-JC-RESOLVES-BOTH-SEAMS. | | D-ARM-SYN-1 | Add `Implies`/`CoOccursWith` to `ruff_spo_triplet::Predicate` closed vocabulary (+ `Provenance` tier) so ARM rules load through the same `parse_triples` ndjson path as the static extractor | ruff/ruff_spo_triplet | 40 | MED | **Queued** | council-gated (deliberate ontology change); blocks SYN-2; see `.claude/knowledge/aerial-arm-ruff-spo-codegen-synergies.md` §1 | | D-ARM-SYN-2 | `CandidateRule → ruff_spo_triplet::ModelGraph` adapter so the Aerial runtime-data leg joins the `ruff_python_dto_check` static-AST leg in one graph before `expand()` | lance-graph-arm-discovery + ruff_spo_triplet | 120 | MED | **Queued** | depends on SYN-1; synergy doc §2 | | D-ARM-SYN-3 | Calibrate `ProvenanceTier::ArmDiscovered` `(f,c)` below the `op_emitter` ratification gate + below static `Inferred (0.85,0.75)` so un-ratified ARM truth is council-visible but codegen-filtered | lance-graph-contract + lance-graph-ontology::op_emitter | 30 | MED | **Queued** | depends on D-ARM-1 + SYN-1; synergy doc §3/§4 | diff --git a/.claude/knowledge/splat-codebook-aerial-wikidata-compression.md b/.claude/knowledge/splat-codebook-aerial-wikidata-compression.md new file mode 100644 index 00000000..f97cb521 --- /dev/null +++ b/.claude/knowledge/splat-codebook-aerial-wikidata-compression.md @@ -0,0 +1,111 @@ + + +# KNOWLEDGE: Splat codebook → aerial discovery → deterministic Wikidata compression + +## READ BY: +- Any worker wiring `lance-graph-arm-discovery::aerial::CodebookDistance` to a production oracle +- Any worker touching `crates/jc` (Jirak-Cartan) pillars 5/9/9b/10 in an ARM/compression context +- Any worker on the Wikidata-HHTL load (`specs/wikidata-hhtl-load.md`) skeleton/basin pass +- `truth-architect`, `integration-lead`, `palette-engineer` + +## P0 TRIGGERS: +- About to give `aerial` a real distance oracle → the oracle is jc's certified palette256 table, read this +- About to implement the D-ARM-7 Jirak significance floor → it derives from `jc::jirak`, read this +- About to add a float similarity to the ARM/discovery path → STOP, the float lives OFFLINE in jc, not here + +--- + +## The convergence + +`lance-graph-arm-discovery` (the Aerial+ transcode) shipped with two open seams: + +1. a **production `CodebookDistance` oracle** (the de-float replaced the autoencoder with an injected integer distance — E-ARM-PROBE-IS-CODEBOOK-TOPK), and +2. the **D-ARM-7 Jirak significance floor** (the still-unimplemented gate above the classical ARM support/confidence floors — ISSUE ARM-JIRAK-FLOOR). + +**Both resolve to the same crate: `crates/jc` (Jirak-Cartan).** jc *proves the +codebook is sound*; aerial *uses it to discover the ontology skeleton* that +drives deterministic Wikidata compression. The user's framing — +"gaussian-splat spatial blasgraph top-k 10000×10000 … for OWL/DOLCE+ SPO HHTL +classes and basin via aerial+ to deterministically compress Wikidata … adjacent +to JC Jirak[-Cartan] with EWA-sandwich gaussian splat" — names this end to end. + +```text + OFFLINE (build + certify the codebook — f64 is allowed here) + ─────────────────────────────────────────────────────────────────────── + ndarray::hpc::splat3d jc::ewa_sandwich{,_3d} (Pillars 9/9b) + Gaussian-splat spatial top-k → Σ-push-forward J·W·Σ·Wᵀ·Jᵀ certified + (10000×10000 BLASGraph; jc::sigma_codebook_probe: 256-codebook + jc splat_* graph algos: captures the Σ-distribution at + Louvain / triangle / LPA / ρ=0.9973 (R² ≥ 0.99, log-Euclidean) + Jaccard-Adamic-Adar) jc::pflug (Pillar 10): CAM-PQ tree + │ quantization is Lε-faithful ⇒ the + ▼ HHTL cascade preserves FreeEnergy + 256×256 integer [a,b] distance table ──────────────────────────────┐ + │ + ONLINE (use the frozen table — integer ONLY) │ + ───────────────────────────────────────────────────────────────────│─── + aerial::CodebookDistance ◄── MatrixDistance::new(spec, table) ◄─────┘ + │ (the codebook probe: nearest consequents within θ) + ▼ + aerial discovers OWL/DOLCE+ SPO HHTL classes + basins from Wikidata triples + │ gated by: classical support/confidence (ppm) + │ AND D-ARM-7 Jirak floor ◄── derives its rate from jc::jirak + ▼ (n^(p/2-1), weak dependence) + skeleton (P279/P31 class DAG + basin assignment) → codebook-HHTL compression + (specs/wikidata-hhtl-load.md: skeleton + basins + CAM-dedup + thin rows) +``` + +## The two seams, precisely + +| Seam | aerial side | jc side | +|---|---|---| +| Similarity oracle | `aerial::CodebookDistance::distance(a,b) -> u32` (integer, frozen). `MatrixDistance` already consumes a `[u32; dim²]` table. | jc builds + certifies that table: `ewa_sandwich` (splat Σ-push-forward correct) + `sigma_codebook_probe` (256-codebook viable at ρ=0.9973). | +| Significance floor (D-ARM-7) | `CandidateRule::passes()` gates classical support/confidence; the Jirak floor is the stricter, unimplemented gate above it. | `jc::jirak` supplies the correct rate (`n^(p/2-1)` for weakly-dependent data) — classical IID Berry-Esseen is wrong here (I-NOISE-FLOOR-JIRAK). The floor threshold derives from this rate. | + +## The float boundary (why this is doctrine-clean) + +The de-float removed float from aerial's **online** path. This pipeline keeps +that invariant: **float lives only in jc's OFFLINE certification** — k-means in +log-Euclidean space (`sigma_codebook_probe`), the SPD/covariance EWA math +(`ewa_sandwich`), the Berry-Esseen sup-error (`jirak`). All of that runs once, +produces a frozen integer artifact (the 256×256 `[a,b]` table + a derived +significance threshold), and is never on aerial's hot path. This is exactly the +CAM-PQ doctrine (`faiss-homology-cam-pq.md`, I-VSA-IDENTITIES): **build the +codebook offline (float OK), address it online with integer codes.** jc is the +"build + prove"; aerial is the "use". + +## jc pillar → role in this pipeline + +| jc module | Pillar | Role here | +|---|---|---| +| `ewa_sandwich`, `ewa_sandwich_3d` | 9, 9b | Certifies the Gaussian-splat Σ-push-forward (`ndarray::hpc::splat3d`) — the splat top-k that *builds* the codebook is correct. | +| `sigma_codebook_probe` | (probe) | The ρ=0.9973 / R²≥0.99 viability measurement — *why* a 256-codebook may replace a float similarity. | +| `pflug` | 10 | Pflug-Pichler nested-distance Lipschitz — the CAM-PQ/HHTL tree quantization preserves FreeEnergy within Lε, so codebook-HHTL compression is faithful, not lossy-by-surprise. | +| `jirak` | 5 | The weak-dependence Berry-Esseen rate — the engine the D-ARM-7 significance floor derives from. | + +## Wikidata / DOLCE mapping (specs/wikidata-hhtl-load.md, ogit-owl-dolce) + +- **Skeleton (Pass 1):** P279 (subClassOf) DAG + P31 (instance-of) → classes + basins (HHTL levels). aerial's discovered `(X → Y)` rules over the class/property items ARE candidate skeleton edges; the Jirak floor decides which are significant enough to persist. +- **DOLCE as axis template:** Endurant/Perdurant/Quality/Abstract (≈ Object/Process/Quality/Region) define WHICH HHTL axes exist; Wikidata properties fill WHAT occurs. aerial discovers the fill against the DOLCE scaffold. +- **Compression is structural:** classes + masks + refs + CAM-deduped shapes, not a gzip trick (~120GB → ~38GB). aerial supplies the class/basin structure; codebook-HHTL supplies the bucket router (16ⁿ nibble addressing). + +## Status & open questions + +- **CONJECTURE** (architecture): the splat→aerial→Wikidata wiring as a whole. The *seams* are concrete (the `CodebookDistance` trait + `jc::jirak`); the end-to-end pipeline is not yet built. +- **Hard prerequisite:** D-ARM-7 (the Jirak floor) must land before aerial promotes any rule to a live skeleton — ISSUE ARM-JIRAK-FLOOR. jc::jirak is the engine; the *gate function* (rule → significant?) is the deliverable. +- **No new aerial dependency needed:** jc emits a frozen integer table; aerial consumes it through the existing `MatrixDistance`/`CodebookDistance` seam. Do NOT make aerial depend on jc — keep the float-free standalone posture; pass the certified table in. +- **Open (from wikidata-hhtl-load):** P279 fan-out is wildly uneven (2…4000 children) — whether it rebalances onto a clean 16ⁿ tree or forces adaptive fan-out is MEASURABLE; measure on a real P279 subtree before fixing the HHTL base. +- **target-cpu:** the splat/SIMD kernels (AVX-512 VPOPCNTQ, Intel AMX) need `-C target-cpu=native` or `x86-64-v4`; otherwise ndarray's correct-but-scalar polyfill runs. + +## Cross-references + +- `crates/lance-graph-arm-discovery/` — the aerial consumer; `aerial::CodebookDistance` is the seam. +- `crates/jc/src/{ewa_sandwich,ewa_sandwich_3d,sigma_codebook_probe,pflug,jirak}.rs` — the certifier. +- `.claude/specs/wikidata-hhtl-load.md` — the compression pipeline. +- `.claude/knowledge/ogit-owl-dolce-ontology-compartments.md` — the DOLCE scaffold. +- `.claude/plans/3DGS-HHTL-datalake-traversal-plan.md` — the splat-HHTL traversal. +- `.claude/knowledge/faiss-homology-cam-pq.md`, CLAUDE.md `I-VSA-IDENTITIES`, `I-NOISE-FLOOR-JIRAK` — the float/codebook + Jirak iron rules. +- `EPIPHANIES.md` E-ARM-PROBE-IS-CODEBOOK-TOPK (the de-float) + the entry that points here. diff --git a/crates/lance-graph-arm-discovery/src/aerial/codebook.rs b/crates/lance-graph-arm-discovery/src/aerial/codebook.rs index 09732e22..bdf3841c 100644 --- a/crates/lance-graph-arm-discovery/src/aerial/codebook.rs +++ b/crates/lance-graph-arm-discovery/src/aerial/codebook.rs @@ -12,10 +12,15 @@ //! never by a softmax over float weights. //! //! The oracle is a **trait**, not a concrete table, so this crate stays -//! zero-dep and standalone: the real implementation is -//! `bgz17::PaletteDistanceTable` (or the BLASGraph Gaussian-splat top-k, or an -//! HDR-popcount Hamming primitive) wired in by the consumer. [`MatrixDistance`] -//! is the in-crate reference impl used by tests. +//! zero-dep and standalone. The production table is **built and certified +//! offline by `crates/jc` (Jirak-Cartan)** — `ewa_sandwich` certifies the +//! Gaussian-splat Σ-push-forward (the 10000² BLASGraph spatial top-k that +//! constructs it), and `sigma_codebook_probe` measures the 256-codebook viable +//! at ρ=0.9973. That offline build may use float (k-means, SPD math); the +//! frozen `[u32; dim²]` table it emits is consumed here as integer through +//! [`MatrixDistance`]. Float to BUILD the codebook, integer to USE it — the +//! CAM-PQ doctrine. See `.claude/knowledge/splat-codebook-aerial-wikidata-compression.md`. +//! [`MatrixDistance`] is also the in-crate reference impl used by tests. //! //! Invariant (per `faiss-homology-cam-pq.md`): this distance is **discovery / //! shape-family only**, never identity addressing. Identity is the exact CAM From bca868e203bdd73487a48349e1260d4df1156320 Mon Sep 17 00:00:00 2001 From: Claude Date: Sat, 30 May 2026 20:11:29 +0000 Subject: [PATCH 7/7] fix(arm-discovery): harden public APIs per PR #436 review (CodeRabbit) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Verified all 11 review comments against current (post de-float + SIMD) code. 5 still valid, 6 outdated/resolved. Fixed the 5, each with a regression test: - aerial/codebook.rs: MatrixDistance::code validates (feature, category) bounds so an invalid Item fails fast instead of aliasing another feature's block and returning a real-but-wrong distance. - aerial/extract.rs: extract_rules honors max_antecedent == 0 (returns no rules) instead of forcing 1-item antecedents via .max(1). - translator.rs: arm_to_truth_u8 asserts k > 0. k=0 made any cooccur>0 quantise to confidence=255 (1.0) — dogmatic truth at the SPO edge. - encode.rs: Dataset::new rejects rows with category >= cardinality (previously accepted, then silently undercounted since no Item could ever match). - knowledge/aerial-arm-ruff-spo-codegen-synergies.md: add `text` lang tag to a fenced block (MD040). Skipped (with reason): autoencoder forward/mean_loss/train guards + mod.rs hidden_dim invariant + Cargo.toml bgz-tensor comment all target code deleted or rewritten by the de-float (8681cdf); STATUS_BOARD "Shipped (branch)" is an established board convention used by every D-ARM row. Verified: 37/37 tests (33 + 4 regression) + clippy -D warnings clean on both default and --features ndarray-simd. https://claude.ai/code/session_012SorR8UbtEvYmbX8cXftj7 --- .claude/board/AGENT_LOG.md | 10 +++++++ .../aerial-arm-ruff-spo-codegen-synergies.md | 2 +- .../src/aerial/codebook.rs | 26 +++++++++++++++++-- .../src/aerial/extract.rs | 18 +++++++++++-- .../lance-graph-arm-discovery/src/encode.rs | 18 ++++++++++++- .../src/translator.rs | 15 ++++++++--- 6 files changed, 80 insertions(+), 9 deletions(-) diff --git a/.claude/board/AGENT_LOG.md b/.claude/board/AGENT_LOG.md index bc595147..a0150f69 100644 --- a/.claude/board/AGENT_LOG.md +++ b/.claude/board/AGENT_LOG.md @@ -1,3 +1,13 @@ +## [Main thread / Opus] PR #436 review response — 5 CodeRabbit findings (public-API hardening) + +**Branch:** claude/jolly-cori-clnf9 | **Files:** `crates/lance-graph-arm-discovery/src/{aerial/codebook.rs, aerial/extract.rs, translator.rs, encode.rs}` + `.claude/knowledge/aerial-arm-ruff-spo-codegen-synergies.md`. + +**Cargo:** **37/37** (33 + 4 regression tests) + clippy `-D warnings` clean on BOTH default and `--features ndarray-simd`. + +**Outcome:** DONE. PR #436 had 11 CodeRabbit review comments across 2 rounds. Verified each against CURRENT code (post de-float + SIMD); 5 still valid, 6 outdated/resolved. **Fixed (all quick-win public-API guards + a `#[should_panic]`/behavior test each):** (1) `MatrixDistance::code` validates `(feature,category)` bounds so an invalid item fails fast instead of aliasing another feature's block; (2) `extract_rules` honors `max_antecedent==0` (returns no rules) instead of forcing singletons; (3) `arm_to_truth_u8` asserts `k>0` (k=0 made any cooccur>0 dogmatically `confidence=1.0`); (4) `Dataset::new` rejects rows with `category ≥ cardinality` (was silently undercounted); (5) added `text` language tag to a fenced block (MD040). **Skipped (with reason):** autoencoder `forward/mean_loss/train` guards + `mod.rs` hidden_dim invariant + Cargo.toml bgz-tensor comment → all target code DELETED/rewritten by the de-float (8681cdf); STATUS_BOARD "Shipped (branch)" taxonomy nit → established convention used by every D-ARM row (changing one row would be inconsistent). No PR replies posted (frugal); fixes self-document via the commit. PR #436 updated. + +--- + ## [Main thread / Opus] Splat-codebook ↔ aerial ↔ Wikidata wiring — jc resolves both aerial seams **Branch:** claude/jolly-cori-clnf9 | **Files:** ADDED `.claude/knowledge/splat-codebook-aerial-wikidata-compression.md`; EPIPHANIES (E-ARM-JC-RESOLVES-BOTH-SEAMS); STATUS_BOARD (D-ARM-7 engine pointer, D-ARM-13 → 33/33 + SIMD clause, new D-ARM-14); `aerial/codebook.rs` oracle doc names jc + the float boundary; AGENT_LOG. diff --git a/.claude/knowledge/aerial-arm-ruff-spo-codegen-synergies.md b/.claude/knowledge/aerial-arm-ruff-spo-codegen-synergies.md index 6aba3bb2..d5378ed5 100644 --- a/.claude/knowledge/aerial-arm-ruff-spo-codegen-synergies.md +++ b/.claude/knowledge/aerial-arm-ruff-spo-codegen-synergies.md @@ -22,7 +22,7 @@ bracket whose substrate and codegen legs **already exist in the `ruff` fork** so Aerial's job is to emit into contracts the ruff crates already define, not to invent new ones. -``` +```text PROPOSER FRONTENDS SUBSTRATE CODEGEN LEGS ┌───────────────────────┐ ┌────────────────────┐ │ ruff_python_dto_check │ static AST ┐ │ op_emitter.rs │ Rust diff --git a/crates/lance-graph-arm-discovery/src/aerial/codebook.rs b/crates/lance-graph-arm-discovery/src/aerial/codebook.rs index bdf3841c..c5083c79 100644 --- a/crates/lance-graph-arm-discovery/src/aerial/codebook.rs +++ b/crates/lance-graph-arm-discovery/src/aerial/codebook.rs @@ -62,10 +62,23 @@ impl MatrixDistance { Self { dim, offsets, table } } - /// The flat code (slot index) of an item. + /// The flat code (slot index) of an item. Validates bounds so an invalid + /// `(feature, category)` fails fast rather than aliasing another feature's + /// block and returning a real-but-wrong distance. #[must_use] fn code(&self, it: Item) -> usize { - self.offsets[it.feature as usize] + it.category as usize + let feature = it.feature as usize; + let start = *self + .offsets + .get(feature) + .expect("item feature out of range for distance table"); + let end = self.offsets.get(feature + 1).copied().unwrap_or(self.dim); + let category = it.category as usize; + assert!( + category < end - start, + "item category {category} out of range for feature {feature} block" + ); + start + category } } @@ -127,4 +140,13 @@ mod tests { let ant = [Item::new(0, 0), Item::new(0, 1)]; assert_eq!(antecedent_distance(&d, &ant, Item::new(1, 0)), 1); } + + #[test] + #[should_panic(expected = "out of range")] + fn code_rejects_out_of_range_category() { + let spec = FeatureSpec::new(vec![2, 2]); + let d = MatrixDistance::new(&spec, vec![0u32; 16]); + // category 5 ≥ feature-0 block width 2 — must fail, not alias feature 1. + let _ = d.distance(Item::new(0, 5), Item::new(1, 0)); + } } diff --git a/crates/lance-graph-arm-discovery/src/aerial/extract.rs b/crates/lance-graph-arm-discovery/src/aerial/extract.rs index 0177a538..56a629be 100644 --- a/crates/lance-graph-arm-discovery/src/aerial/extract.rs +++ b/crates/lance-graph-arm-discovery/src/aerial/extract.rs @@ -89,8 +89,10 @@ pub fn extract_rules( .collect(); let mut rules: Vec = Vec::new(); - let max_ant = params.max_antecedent.max(1); - for size in 1..=max_ant { + if params.max_antecedent == 0 { + return rules; // caller asked for no antecedents — honor it, don't force singletons + } + for size in 1..=params.max_antecedent { for feature_combo in feature_combinations(&candidate_features, size) { for antecedent in item_product(&feature_combo, &frequent_by_feature) { probe(oracle, data, &masks, &antecedent, &feature_combo, params, &mut rules); @@ -317,4 +319,16 @@ mod tests { let dist = MatrixDistance::new(&spec, vec![0u32; 16]); assert!(extract_rules(&dist, &data, &ExtractParams::default()).is_empty()); } + + #[test] + fn max_antecedent_zero_yields_no_rules() { + let (data, dist) = fixture(0); + let p = ExtractParams { + theta: 2, + max_antecedent: 0, + min_support_ppm: 0, + min_confidence_ppm: 0, + }; + assert!(extract_rules(&dist, &data, &p).is_empty()); + } } diff --git a/crates/lance-graph-arm-discovery/src/encode.rs b/crates/lance-graph-arm-discovery/src/encode.rs index 7af2a5c1..2ba674b0 100644 --- a/crates/lance-graph-arm-discovery/src/encode.rs +++ b/crates/lance-graph-arm-discovery/src/encode.rs @@ -95,8 +95,16 @@ impl Dataset { /// If any row's length differs from the spec's feature count. #[must_use] pub fn new(spec: FeatureSpec, rows: Vec>) -> Self { - for r in &rows { + for (row_idx, r) in rows.iter().enumerate() { assert_eq!(r.len(), spec.num_features(), "row arity mismatch"); + for (feature_idx, &category) in r.iter().enumerate() { + assert!( + category < spec.cardinality(feature_idx), + "row {row_idx} feature {feature_idx} category {category} out of range \ + (cardinality {})", + spec.cardinality(feature_idx) + ); + } } Self { spec, rows } } @@ -159,4 +167,12 @@ mod tests { assert_eq!(data.count_matching(&[Item::new(0, 5)]), 0); // absent category assert_eq!(data.len(), 4); } + + #[test] + #[should_panic(expected = "out of range")] + fn out_of_range_category_is_rejected_at_construction() { + let spec = FeatureSpec::new(vec![2, 2]); + // feature 1 category 2 ≥ cardinality 2 — would silently undercount. + let _ = Dataset::new(spec, vec![vec![0, 2]]); + } } diff --git a/crates/lance-graph-arm-discovery/src/translator.rs b/crates/lance-graph-arm-discovery/src/translator.rs index ce5fc35b..1ffe803d 100644 --- a/crates/lance-graph-arm-discovery/src/translator.rs +++ b/crates/lance-graph-arm-discovery/src/translator.rs @@ -36,16 +36,19 @@ pub struct TruthU8 { /// `k` is the NARS personality constant ([`NARS_PERSONALITY_K`] default). #[must_use] pub fn arm_to_truth_u8(rule: &CandidateRule, k: u32) -> TruthU8 { + assert!( + k > 0, + "NARS personality constant k must be > 0; k=0 makes any cooccur>0 dogmatic (confidence=1.0)" + ); // frequency = P(Y|X) = cooccur / antecedent_count let frequency = if rule.antecedent_count == 0 { 128 // unknown ≈ 0.5 } else { ((rule.cooccur as u64 * 255) / rule.antecedent_count as u64).min(255) as u8 }; - // confidence = m / (m + k), m = cooccur (integer evidential mass) + // confidence = m / (m + k), m = cooccur (integer evidential mass); k>0 ⇒ denom>0 let m = rule.cooccur as u64; - let denom = (m + k as u64).max(1); - let confidence = ((m * 255) / denom) as u8; + let confidence = ((m * 255) / (m + k as u64)) as u8; TruthU8 { frequency, confidence, @@ -229,4 +232,10 @@ mod tests { assert_eq!(t.p, "implies"); assert_eq!(t.o, "arm:feat1=cat0"); } + + #[test] + #[should_panic(expected = "k must be > 0")] + fn k_zero_is_rejected_not_made_dogmatic() { + let _ = arm_to_truth_u8(&rule(5, 10, 100), 0); + } }