build_refusal_taxonomy_cases._STATEMENT_RE only matched the old 'no admissible
candidate for ...' shape, so post-#359 'recognizer matched but produced no
injection ... (category=X)' refusals were silently dropped (44 refusals -> 12
extracted). Extend it to both shapes (same gap fixed in rescan_v4 before that
layer was retired).
The lane mirrored 50 cases from the all-refused era; the reader now admits 6,
so it covers the 44 refused. Regenerated the cases fixture + committed
report.json and updated the count pins (50 -> 44).
Removed the perverse categorized_rate >= 0.95 floor: the exact histogram is
already pinned by test_committed_report_matches_categorizer, and the rate
drifts DOWN as the reader graduates categorized refusals — it fought reader
progress. Replaced with a sanity floor.
adr_0126: the unparseable 'contemplates' input still refuses (wrong=0); only
the reason wording changed (#359). Accept either non-admission phrasing.
Phase B round 2. Categorizing the post-#304 GSM8K train_sample's
still-refused 47 set surfaced three coherent sub-shapes in the previously
UNCATEGORIZED tail plus five ratified-but-narrowness-blocked temporal
cases; this PR ships the operator-authored exemplar seeds + Phase A
categorizer extension that prove the corridor scales beyond round 1.
Exemplar corpora (70 new exemplars across 4 files):
- discrete_count_statement_v1.jsonl (20)
- multiplicative_aggregation_v1.jsonl (20)
- currency_amount_v1.jsonl (20)
- temporal_aggregation_v2.jsonl (10, widening)
Each corpus carries ≥3 verbatim train-sample citations, ≥12 (≥5 for v2)
novel operator-authored statements, and ≥1–3 edge cases. Statements are
disjoint across all 7 round-1 + round-2 corpora; tests enforce.
Phase A categorizer (evals/refusal_taxonomy/shape_categories.py)
extends ShapeCategory with three new members and inserts their rule
predicates AFTER the existing more-specific categories:
- rate_with_currency before currency_amount
- multiplicative_aggregation before discrete_count_statement
Each new rule predicate cites ≥3 train_sample case_ids in its docstring
(ADR-0163 §Risks). No LLM, no embedding, no learned classifier.
Refusal-taxonomy histogram empirical signal (public 50 sample):
- pre-round-2: 14 UNCATEGORIZED (categorized_rate 0.72)
- post-round-2: 1 UNCATEGORIZED (categorized_rate 0.98)
The single residual is case 0044 ("10% simple interest" — percentage
without change verb), an honest tail outside the three round-2 shapes.
wrong=0 holds on capability axes G1..G5 + S1; no runtime code shipped.
Smoke suite green (67/67).
Cross-refs: ADR-0163, #297 (Phase A), #298 (Phase B round 1),
#301 (Phase C), #302 (Phase D), #304 (round-1 ratify), #305 (session
recap).
Co-authored-by: Claude Opus 4.7 <noreply@anthropic.com>
ADR-0163 Phase A measurement. Reads the GSM8K train-sample refusal report
(50 cases, all refused on candidate-graph admissibility) and emits a
histogram of statement shapes. Read-only: no corpus, pack, or proposal
mutation; the categorizer is rules-only with no LLM, embedding, or
learned model.
Lane: evals/refusal_taxonomy/ (auto-discovered by evals.framework)
- shape_categories.py — ShapeCategory enum + deterministic categorizer
(9 ADR-mandated baseline categories + UNCATEGORIZED, first-match-wins)
- runner.py — pure run_lane(cases) -> LaneReport
- contract.md — purpose, doctrine, schema, ADR compatibility
- public/v1/cases.jsonl — 50 refused statements (sorted by case_id)
- v1/report.json — first run output (categorized_rate=72%)
CLI: core teaching refusal-taxonomy [--input PATH] [--json] [--save]
Accepts a cases JSONL or a raw GSM8K eval report.json directly.
Helper: scripts/build_refusal_taxonomy_cases.py rebuilds the v1 case set
from the GSM8K train-sample report deterministically.
Tests: tests/test_refusal_taxonomy_lane.py (21 passing) cover schema
integrity, lane auto-discovery, enum exhaustiveness, categorizer
determinism + purity + no-ML-imports, histogram correctness, replay
byte-identity, committed report match, helper extraction, and a
read-only invariant snapshot over teaching/, packs/, language_packs/data/.
v1 histogram (50-case sample):
17 descriptive_setup_no_quantity
14 uncategorized
4 temporal_aggregation
3 rate_with_currency
3 fractional_rate_of_change
3 indefinite_quantity
3 comparative_with_unit
2 nested_question_target
1 unit_partition
0 conditional_quantity
total=50 categorized_rate=72% uncategorized=28% (below 50% target)
Top three by count (Phase B candidates):
1. descriptive_setup_no_quantity (17)
2. temporal_aggregation (4)
3. tie at 3 — operator selects from {rate_with_currency,
fractional_rate_of_change, indefinite_quantity, comparative_with_unit}
Phase B is not started in this PR — the ADR explicitly requires the
operator to ratify the top-N selection before any exemplar corpus is
authored.
Invariants verified:
- tests/test_adr_0131_*.py: 224 passed, 0 wrong on G1..G5 + S1
- core test --suite smoke -q: 67 passed
- The refusal_taxonomy/__init__.py and runner do not import openai,
anthropic, transformers, torch, sklearn, sentence_transformers,
requests, or httpx — verified by test_categorizer_no_llm_or_ml_imports.
Cross-references: ADR-0163 (parent), ADR-0114a (capability obligations),
ADR-0149 (recognizer pipeline substrate that Phases C–E build on).
Refs: [[thesis-decoding-not-generating]] — the rules-only categorizer
honors the doctrine: the engine learns to find better shapes; this PR
does not stuff it with another found pattern.
