#000049: Phase-2 candidate-NLI bench — first run (§7 #18); both candidates 18/18 catch, 0/18 FP on synthetic set; harness in ~/git/arborist-nli-bench

docs/TICKETS.md

@@ -103,7 +103,7 @@ Newest first. Update on every open/close.
 |----------|------------------------------------------------|-----------------------|------------|-----------|
 | #000051  | Federated vecpack distribution (gossip the embedding backfill) | open · awaiting go/no-go · doc-only scaffold. Makes `chunk_vecs` a distributable artifact: backfill once on any CPU box (cloud / Prometheus-Σ sweep — #000037 §3.1), publish a **vecpack** `(shard_root, vec_backend_version, [(leaf_hash, embedding_blob)…])` over the mesh wire layer, every peer pulls + bulk-loads (sub-ms/chunk on the receiver — the laptop never runs the transformer). Keyed on `leaf_hash` (portable) not `chunk_id` (shard-local). Vecpacks are **soft data** — embeddings are `UNGROUNDED`, never proof path — so a cheap structural sanity gate (chunk exists locally w/ matching leaf_hash, right blob length for (dim,quant), finite norm, backend_version matches) suffices, no Merkle-proof-grade verification needed. Supplies #000050's prereq #1 ("a vecpack exists & is imported on the bench box", not "fox embedded the corpus locally"). GPU producer (the fast path): bge-small-en-v1.5 batched on a CUDA box (4090) ≈ 10³–10⁴ chunks/s → full 6.24M-chunk corpus in *minutes*, not days — drop a CUDA `Embedder` into `default_embedder()`; CUDA stack lives only on the producer box, never in arborist's `python+sqlite3` core. The mechanism behind whitepaper §1's "the embedding pass runs off the device". #000039 / #000050 sibling | 2026-05-12 | —         |
 | #000050  | Vec RRF hybrid fusion (#000039 Phase 2) | open · awaiting go/no-go · doc-only scaffold; design in #000039 §4.2 (RRF) + §8 (the gate). Wire `VecBackend` as a 5th retrieval route in `query.py`, RRF-merged (route provenance carried) with the 4 FTS5 routes; UNGROUNDED hits, additive not replacement. Phase-2 sub-items now explicit: **accept-path-5** in `_filter_by_title_relevance` (low-title-overlap vec hits survive only via a stronger span-level warrant, never similarity-score alone — else the title gate drops exactly the semantic candidates vec exists for & the bench shows no lift); **six** vec config fields fold into `governance_policy_hash` (recipe-named quant `int8sym`) **+ a cache-write guard** blocking `providence_cache` persistence for vec/hybrid runs until that's wired; **run-DAG records the vec stage** (backend version, six fields, top_k, query-embedding hash, candidate chunk_ids+distances). **Gated** on (a) a corpus backfill **distributed via #000051** AND (b) a **four-condition** recall bench (A FTS5-only / B vec-only / C RRF hybrid / D candidate-union-no-RRF) clearing the 5pp floor incl. C-beats-D, on semantic-allusion + curated + **adversarial-semantic-neighbor** fixtures (else park, vec stays opt-in `--backend vec`; if C≈D ship the union, drop RRF). #000039 follow-up | 2026-05-12 | —         |
-| #000049  | Attribution-aware grounding check (the recombination boundary) | open · boundary accepted · production no-go · shadow-path approved (de novo review 2026-05-13 — ticket §7) · doc-only; the home for #000048's deferred §2.3 — closing the 2 recombination over-grounds in `falsification-hard` (hard-003 Mercury / hard-005 Einstein) needs an attribution / dependency-parse or mini-NLI check, which is *not lexical* (#000048 §5). Discipline question answered: a small fixed purpose-built NLI/entailment *model* may influence `audit_mode` only as an opt-in, hash-pinned, governance-hashed, **demotion-only contradiction veto** after shadow-mode evidence (never promotes — `MODEL_ASSISTED_DEMOTION`, never `MODEL_ASSISTED_PROMOTION`). Production verifier unchanged; `falsification-hard` stays 10/12 as an honest boundary marker. Roadmap: Phase 0 (this amendment) → Phase 1 (shadow design: NLI manifest, fetch/verify, `nli_pair@v1` canonicalization, recombination-risk trigger) → Phase 2 (bench-only shadow impl, `[nli]` extra, `make fetch-nli`) → Phase 3 (demotion-only runtime, gated) → Phase 4 (mesh blob sync); §7 #12 six-condition bench gate required before Phases 2–4; if NLI ever affects `audit_mode`, `nli_policy_hash` folds into `governance_policy_hash`. #000048 follow-up | 2026-05-12 | —         |
+| #000049  | Attribution-aware grounding check (the recombination boundary) | open · boundary accepted · production no-go · shadow-path approved (de novo review 2026-05-13 — ticket §7) · doc-only; the home for #000048's deferred §2.3 — closing the 2 recombination over-grounds in `falsification-hard` (hard-003 Mercury / hard-005 Einstein) needs an attribution / dependency-parse or mini-NLI check, which is *not lexical* (#000048 §5). Discipline question answered: a small fixed purpose-built NLI/entailment *model* may influence `audit_mode` only as an opt-in, hash-pinned, governance-hashed, **demotion-only contradiction veto** after shadow-mode evidence (never promotes — `MODEL_ASSISTED_DEMOTION`, never `MODEL_ASSISTED_PROMOTION`). Production verifier unchanged; `falsification-hard` stays 10/12 as an honest boundary marker. Roadmap: Phase 0 (this amendment) → Phase 1 (shadow design: NLI manifest, fetch/verify, `nli_pair@v1` canonicalization, recombination-risk trigger) → Phase 2 (bench-only shadow impl, `[nli]` extra, `make fetch-nli`) → Phase 3 (demotion-only runtime, gated) → Phase 4 (mesh blob sync); §7 #12 six-condition bench gate required before Phases 2–4; if NLI ever affects `audit_mode`, `nli_policy_hash` folds into `governance_policy_hash`. **Phase-2 candidate bench started 2026-05-12** (`~/git/arborist-nli-bench/`, commit `829f9a4`; ticket §7 #18): `deberta-v3-base-mnli-fever-anli` (184M, 251ms p50 CPU) and `nli-MiniLM2-L6-H768` (82M, 28ms) both catch 18/18 synthesized recombination cases (incl. the 2 fixtures) with 0/18 false positives on true cross-sentence summaries, contra-score gap ~0.93–0.97, both deterministic — picture: recombination is an *easy* task for any modern NLI checkpoint, so the small MiniLM is the cost-pick; open risk = real-traffic FP rate, which only a shadow run on actual `bench-qa` measures (gate item 4, TODO). #000048 follow-up | 2026-05-12 | —         |
 | #000048  | Verifier upgrade — recombination-aware grounding + clause segmentation | **closed · 2026-05-12** — steps 2.1 + 2.4 landed 2026-05-11 (12 of 16 residual items: 4 HYBRID_ENTITY over-grounds + 8 Formulate mis-segments → `formulate-hard` 12/12, `falsification-hard` 10/12; each bench-gated, no STRICT-rate regression — 2.1's gate fired on 0 QA answers, 2.4's segmenter touched 7 of 450 lattice cells both verdict changes correct). Step 2.2 (single-clause-containment paraphrase check) attempted + reverted — catches the 2 recombination fixtures but also rejects legit cross-sentence summaries with no threshold separating the two; recombination-vs-summary isn't lexical (§5 "What we learned"). The attribution-aware path moved to **#000049** (fox 2026-05-12). 2 live-pack `expected_reason` updated HYBRID_ENTITY→UNGROUNDED; 12+ tests; `make bench-5f-falsification-hard` / `bench-5f-formulate-hard` / `bench-fork-baseline-hard`. #000046 follow-up; #000047 closed | 2026-05-11 | —         |
 | #000047  | ForkScore `_delta_*` aggregator (mean vs max vs sum) | **closed · 2026-05-11** — Option D: `WeightSet.delta_aggregator` ∈ {`mean`,`max`,`sum`} (default `mean` unchanged → no `ESTIMATOR_VERSION` bump), `fork_score._delta_5{s,t,f}` dispatch via `_aggregate`, recorded in `ScoredFork.weights`, per-sub `HARD_REGRESSION_FLOOR` flags aggregator-independent; bench data behind keeping `mean` in `5f-threshold-calibration-2026-05-11.md` §5; 8+1 tests. #000012-revision / #000025 §10.14 follow-up | 2026-05-11 | —         |
 | #000046  | Harder 5S/5T/5F fixture tier (below-ceiling baselines) | **closed · 2026-05-11** — Phase 1 `falsification-hard-v1.jsonl` (12 near-misses) + Phase 2 `formulate-hard-v1.jsonl` (12 mis-segments, rate 4/12) + Phase 3 `verify_quotes` paraphrase numeric-agreement gate (`_numeric_signature`; demotes a token-covering span asserting a digit-number the source lacks modulo thousands-comma) → falsification-hard rate 4/12 → 6/12 on a real change; bench-gated (`make bench-qa` n=3×75×3 before/after — no STRICT-rate regression on legit answers; only gate-caused QA shift was correctly demoting a fictional-year claim STRICT→HYBRID); `fork_score` γ·Δ5f went positive on it. Headroom now down to 2 falsification-hard over-grounds (#000048 step 2.1 closed the 4 entity over-grounds; step 2.4 closed the 8 Formulate mis-segments → that pack 12/12; step 2.2 attempted + reverted — the last 2 recombination fixtures need an attribution-aware verifier, now tracked as **#000049**, and stand as documented residue). `make bench-5f-falsification-hard` / `bench-5f-formulate-hard` / `bench-fork-baseline-hard`; 7+ tests. #000025 §10.14 follow-up; #000047 closed; #000048 closed | 2026-05-11 | —         |

docs/tickets/ticket-000049-attribution-aware-grounding-check.md

@@ -1,7 +1,10 @@
 # Ticket #000049 — Attribution-aware grounding check (the recombination boundary)
 
 **Status:** open · boundary accepted · production no-go · shadow-path
-approved (de novo review 2026-05-13 — see §7)
+approved (de novo review 2026-05-13 — see §7) · Phase-2 candidate bench
+done (§7 #18 — `~/git/arborist-nli-bench/`; 2/4 candidates, both
+18/18 catch · 0/18 FP on a synthetic 36-case set; real-QA shadow FP
+rate still TODO)
 **Opened:** 2026-05-12
 **Scope:** Decide whether — and if so how — to add a verifier check
 that catches a *recombination*: a claim whose content tokens are all
@@ -557,3 +560,46 @@ checkpoint/policy change invalidates prior cache identity; the output
 is bench-gated and auditable) while respecting the danger (a model in
 the verifier path is still a model in the verifier path — a
 controlled exception, not a casual dependency).
+
+**18. Phase-2 candidate bench — first run (2026-05-12).** Harness:
+`~/git/arborist-nli-bench/` (sibling repo — keeps `transformers`/
+`torch` out of arborist's `python+sqlite3` core; commit `829f9a4`).
+Runs the §7 #5 clause-level algorithm checkpoint-agnostically (reads
+`id2label` from each model's config) over 18 synthesized recombination
+cases (incl. the two `5f-fal-hard-003/-005` fixtures — the veto
+*should* fire) + 18 true cross-sentence summaries (the veto must
+*not* fire). First two candidates:
+
+| candidate | params | p50 / p95 latency (CPU) | catch / FP | min(contra\|recomb) − max(contra\|legit) |
+|---|---|---|---|---|
+| `MoritzLaurer/DeBERTa-v3-base-mnli-fever-anli` (Apache-2.0) | 184M | 251 / 465 ms | **18/18 · 0/18** | 0.977 − 0.005 = **0.97** |
+| `cross-encoder/nli-MiniLM2-L6-H768` (Apache-2.0) | 82M | **28 / 42 ms** | **18/18 · 0/18** | 0.981 − 0.046 = **0.93** |
+
+Both deterministic across re-runs. *Every* θ_c in roughly [0.1, 0.95]
+separates the two classes perfectly (best operating point reported as
+θ_c = 0.5, θ_e = 0.9 — the θ_e entailment-guard barely matters on this
+set, only `recomb-004` Mauna Kea has high `max_entail`). Null baseline
+(current lexical verifier, no NLI) is 0/2 catch · 0 FP — so on *this*
+eval set both candidates beat it net (full catch, FP still 0). The
+differentiator is **cost, not capability**: contradiction recombination
+is an *easy* task for any modern sentence-pair NLI checkpoint, so the
+82M MiniLM (9× faster, half the size) is the natural pick for a check
+that runs only on a small unresolved subset.
+
+**Caveats (why this is not yet the §7 #12 gate, only gate items 1–3 +
+6).** (a) The eval set is *synthetic and clean* — short single-sentence
+sources, textbook-contradiction shapes; real Wikipedia QA has long
+messy contexts, partial overlaps, hedged claims. Gate item 4
+(shadow-mode FP rate on a real `bench-qa` sample) is the actual test
+and is *not* done here. (b) n = 36 total — better than n = 2, still
+small; needs the harder, more-realistic recombination/summary cases
+before it's load-bearing. (c) Only 2 of 4 manifest candidates run
+(the deberta-large + bart-large reference points pending) — but the
+picture is already clear enough that "which checkpoint" is a
+cost question, not an accuracy one. **None of Phases 2 (full shadow
+implementation in arborist) / 3 / 4 are unblocked by this run** — it
+unblocks the *decision to build a shadow mode at all* with real
+numbers instead of a contrived 2/12, and the answer it points to is
+"yes, a shadow mode is worth building; use a small cross-encoder NLI;
+the open risk is real-traffic false positives, which only a shadow
+run on actual QA can measure."