CP-2a populates the CP-1 ledger from gold-labelled candidate readings and reports
per-pattern reliability — the measurement the cue-precision thesis rests on. Plus
the function-word unit filter, whose value this measurement makes concrete (clean
unit_shape labelling).
What landed (all sealed; serving 3/47/0 byte-identical):
- generate/cue_precision/trainer.py — train_from_cases(cases, enumerators): folds
gold-labelled candidate chains into the ledger via record_case. Decoupled (the
candidate enumerators are injected, so the package still imports nothing from
search). candidates_for dedupes a reading shared by two enumerators.
- generate/derivation/multistep.py — extracted the enumeration half of search_chain
into public candidate_chains(problem_text); search_chain now delegates (verified
byte-identical: ms3 tests + practice counts unchanged). CP-2 needs the readings
the search weighs, not just the one it resolves.
- generate/derivation/extract.py — function-word unit filter (_NON_UNIT_WORDS):
blanks spurious function-word units ($0.75 each -> "", 3/4 of -> "") that
corrupt same-unit detection and unit_shape. Closed lexeme set, ADR-0165-safe.
- evals/gsm8k_math/practice/v1/cue_precision_report.py — trains over 200 sealed
cases (50 train_sample + 150 ADR-0163-F additive) with the real enumerators and
prints the per-pattern reliability table.
- tests/test_adr_0177_cp2a_training.py — trainer obligations (credit/dedupe/
determinism/empty) via synthetic enumerators; real-measurement well-formedness;
search_chain parity.
Load-bearing finding (recorded in ADR-0177): over 200 cases EVERY (cue,op,unit_shape)
pattern floors at ~0.0 reliability (best: for-multiply-cross_unit 0.0116 at 2/34).
The blunt product/sum-of-all readings are almost always wrong vs gold, so the
conservative floor correctly trusts nothing. => CP-2b (trust reliable cues) is
blocked on candidate GENERATION, not the ledger: candidate readings must get less
crude (clause/referent structure, ADR-0178 GB-3b) before any cue earns reliability.
Cue-precision and compositional structure are coupled; structure comes first.
Verification: 107 targeted tests green (CP-2a/CP-1/extract/ms3/GB-1/2/3/MS-1/2) +
architectural invariants; serving CLAIMS.md sha unchanged; practice 4/1/45 and
0/1/149 unchanged. Inert: trains/reports only, consulted by no search/gate.
Track C of docs/handoff/PARALLEL-WORK-PLAN-2026-05-29.md asked for a tight
EX-3 multi-word-unit redo satisfying (a) "12 jumping jacks." -> "jumping
jacks", (b) "6 apples and 4 apples." -> two apples, (c) all GB-1/2/3 tests
green. The cleanest tight rule that satisfies all three —
(?<![\w.])(\d+(?:\.\d+)?)\s+([a-z]+\s+[a-z]+)(?=\s*[.?!,]|\s*$)
— was implemented and passed the four pinned test files. Full-suite
verification then surfaced a second trap the audit at
docs/handoff/AUDIT-ADR-0179-EX-RECONCILE.md did not anticipate:
postmodifier-adjective tails. "25 years old?" fires the tight rule and
produces unit "years old" rather than "years", regressing
test_adr_0176_ms1_question_target.py::TestQuestionQuantities::
test_extracts_quantity_stated_in_question and the "X years old" pattern
in tests/test_adr_0176_ms2_chain.py. The pattern is endemic in GSM8K
(cases 0006 and 0033 both use "X years old"); closing it would need a
second closed lexeme set ({old, tall, long, wide, deep, away, ago, ...})
which the brief judged too open-ended to enumerate responsibly.
Per the brief's escape hatch ("If no rule satisfies all of (a)-(c) without
a grammar template, write a note and ship no code — a refusal is fine")
this commit:
* updates extract.py's module docstring to name BOTH known traps
(connective-crossing AND postmodifier-adjective tails);
* adds tests/test_adr_0179_extract.py::TestEX3StillDeferred with two pins
asserting the postmodifier-adjective shape stays at unit "years" alone,
so no future redo silently re-introduces the regression;
* ships NO extractor code change — the regex remains exactly as on main.
Scope/safety:
* Files touched are within Track C's allowed set (extract.py + its test).
* Zero functional change: extract_quantities byte-identical to main.
* Serving lane untouched (chat/ does not import this module).
* Safe alongside GB-3b on compose.py / clauses.py.
CP-1 of ADR-0177: the per-(cue, op, unit_shape) reliability ledger + credit
assignment mechanism. Mirrors the ADR-0175 per-class ledger discipline
(core/reliability_gate/ledger.py): counts-only integers, reliability via the
pinned conservative_floor, refusals never counted as commitments.
- generate/cue_precision/ledger.py
- CuePattern: (cue, op, unit_shape) key; op in VALID_OPS, unit_shape closed-set.
- pattern_for_step / patterns_in_chain: per-step extraction. unit_shape compares
the operand unit to the model's running (primary/start) unit; a dimensionless
comparative scalar scales within the dimension -> same_unit.
- PatternTally: counts-only (correct/wrong, no refused axis); reliability =
conservative_floor(correct, committed); 0.0 while cold/below N_MIN.
- CuePrecisionLedger: immutable pattern->tally map (canonical sorted tuple);
record_chain / record_case credit candidate chains by gold label, independent
of whether the search resolved or refused.
Inert substrate: not wired into the gate, any scorer, or the search (CP-2/CP-3).
Imported by nothing outside its own tests (asserted by a source-tree scan).
Tests (tests/test_adr_0177_cp1_ledger.py, 27 passing): pattern validation;
unit_shape classification; cold ledger -> 0 reliability; credit assignment;
refusals-not-counted; reliability earned by volume; determinism/replay;
immutability; inertness scan. Smoke suite green (67 passed).
The mandated lookback review before GB-3 (CLAUDE.md §Lookback Review Discipline)
confirmed the audit's hazards H1/H2/H3 were LIVE: compose_sequential summed
same-unit quantities from the whole problem, merging unrelated referents/scopes
and admitting wrong structures whose value happened to ground:
H1 (second actor's apples) -> 6+4+2 = 12
H2 (comparative on other actor) -> (6+4)*2 = 20
H3 (later depletion event) -> 6+4+3 = 13
Root cause is the audit's G1/D2 drift: GB-2a re-extracts from the whole text and
ignores GB-1's clause structure. The fix is the GB-3 increment — make the composer
clause-scoped (consume segment_clauses), refusing when the licensed structure spans
clauses, because this slice cannot model referents:
- quantities must live in exactly ONE clause (0 or >1 -> refuse);
- a comparative outside that clause -> refuse (unmodelled referent binding).
All three hazards now refuse; all 7 GB-2 single-clause structures preserved
(list-sum, three-item, sum-then-scale, and the mixed-unit/disagreement/too-few
refusals). tests/test_adr_0178_gb3_referent_guard.py would fail against the
pre-guard code (12/20/13), so the obligation is proven, not decorative.
Scope/safety:
- compose_sequential is sealed substrate, not wired to a scorer -> serving
byte-identical 3/47/0 (lane-SHA 8/8, generate_claims --check OK); practice
unchanged 4/1/45. No new test failures (2 pre-existing on main).
- ADR-0178 amended: GB-2 relabelled GB-2a (list slice, drift G1 recorded);
GB-3 split into GB-3a (this referent guard, landed) and GB-3b (constructive
cross-clause chaining, next).
Reconciles ChatGPT's four independently-branched extraction PRs (#451/#452/
#453/#454) into one coherent generate/derivation/extract.py. They each rewrote
the same file + same new test off main, so they conflicted pairwise and needed
integration, not a merge.
Integrated (span-tracked, most-specific-pass-first so numbers are never double
counted):
- EX-1 word-numbers (#452): reuses WORD_NUMBERS; tens-one hyphen compounds;
factor-bearing half/third/quarter excluded.
- EX-4 list-unit inheritance (#451): bare numeric list with one trailing unit.
- EX-5 sentence-final numbers (#454): bare final number with empty unit.
Deferred: EX-3 multi-word units (#453). Its greedy lowercase span reads
"6 apples and 4 apples" as unit "apples and", regressing GB-2's
test_same_unit_list_sums, and still can't recover real multi-word units from
0024-class text ("jumping jacks on"). Needs a tighter rule; see
docs/handoff/AUDIT-ADR-0179-EX-RECONCILE.md.
Verification (sealed lane only; chat/ does not import this module):
- Serving frozen: lane-SHA 8/8 match, generate_claims --check OK -> 3/47/0
byte-identical, wrong=0 held.
- Sealed practice improved 4/2/44 -> 4/1/45: case 0025 flips wrong->refused.
EX-1 reads "three", so completeness sees a quantity the 6x50 chain omits and
refuses the spurious 300 (gold 1200) instead of committing it.
- No new test failures (3 pre-existing on main).
Also fixes stale test drift from EX-2 (#447): TestDecimalGroundingGapIsDeferred
asserted decimals still refuse, but #447 made $0.75-class resolve to 864.
Renamed to TestDecimalGroundingResolves and updated to assert the flip.
Honest scope note: EX-4 does NOT unblock real case 0024 (its PR test used a
fabricated bare-list paraphrase). TestRealCase0024StillBlocked pins the true
boundary.
EX-2 — the ONE shared-primitive (serving-path) touch of extraction richness.
_value_grounds already grounds the symbol form $N.NN (currency) and N/M (fraction);
a decimal written WITHOUT a symbol ('0.75') is never a single token (the tokenizer
splits on '.') so it failed to ground — refusing correct products like 0003
(48*24*0.75=864). Now a bare decimal 'N.M' grounds when both digit-runs appear,
symmetric with the $N.NN and N/M branches. Only returns True on a match; non-matching
decimals fall through (refuse).
wrong=0 (the load-bearing obligation, this being a shared/serving-path change):
- serving 3/47/0 BYTE-IDENTICAL (verified).
- round-trip + candidate-graph tests 51/51.
- lane-SHA gate (pinned eval lanes unchanged) — verified before merge.
Payoff: 0003 unblocked in sealed practice (search_chain -> 864 = gold; +1 flip).
Decimal cases that now ground but mis-compose become sealed eliminations (learning
signal); serving untouched. 6 EX-2 tests (decimal grounds/refuses, integer
unchanged, serving byte-identical, 0003 resolves).
GB-2 first increment (ADR-0178). compose_sequential() adds the structure the blunt
MS-3 shapes couldn't reach: a same-unit quantity LIST sums (additive cue), and any
stated comparative scales the sum (sum-then-scale, 0024-family). Op-per-step from
text structure (list => add; comparative => scale); operands are text quantities
(grounded) + comparative steps (cue-grounded) on the flat left-fold — no derived-
intermediate model needed (running value is the intermediate).
Deliberately narrow: same-unit lists only. A stated comparative is ALWAYS applied
(no bare-vs-scaled self-disagreement). A product base over the same list is added
WITHOUT a comparative tail purely as a disagreement-safety candidate -> a same-unit
list that also carries a mult cue (ambiguous) REFUSES. Product-of-all/cross-unit
products stay MS-3's job (avoids the product x comparative blowups a blunt all-bases
composer produced: 0024 -> 4.3M).
Clean-case capability proven: 8 tests (list-sum, sum-then-double/triple, mixed-units
refuse, ambiguous-disagreement refuse, determinism). Honest practice result: 3/2/45
— NO new flips (extraction wall: real cases like 0024 extract non-uniform units
'36 on' so they aren't seen as same-unit lists), 2 sealed eliminations (0037/0039:
list-sum was the wrong structure -> learning signal). Coverage gated by extraction
richness + cue precision, as predicted.
Sealed; serving untouched. Full derivation surface 53/53; ruff clean; smoke 67.
Continuation: richer relational ops (per/each->multiply, more/older->add), branch/
DAG (0033), and the extraction richness (uniform-unit extraction) that unblocks this
on real cases.
GB-1 — first slice of the comprehension-guided composer (ADR-0178). Reads the
problem one clause at a time and derives each clause's LOCAL contribution; GB-2
combines them across clauses.
generate/derivation/clauses.py:
- segment_clauses(text): sentence-level orthographic split (ADR-0165; not grammar).
- clause_local_results(text) -> tuple[ClauseResult]: per clause, 0 quantities =
context (hold), 1 = leaf (its value), >=2 = bounded local search (reuses MS-3
search_chain). Refuse-preferring: ambiguous multi-quantity clause -> unresolved
hold, not guessed.
Locality is the guidance that bounds the search + steers grouping. 9 GB-1 tests
(segmentation, leaf/context/local-product, ambiguous-holds, determinism,
per-clause structure of a multi-sentence problem). Full derivation surface 86/86;
ruff clean; smoke 67. Sealed; not wired into serving (ClauseResults ready for
GB-2 sequential combination).
MS-3 composes MS-1 (Target) + MS-2 (comparative chains + completeness) + the gate.
generate/derivation/multistep.py: search_chain(problem_text, target=None).
Shape-based, NOT blind enumeration: enumerates a small principled candidate set
(product-of-all if a multiplicative cue is present; sum-of-all if an aggregation
hint is present; each optionally + comparative scalars), each using all
quantities, routed through select_self_verified (grounding ∧ cue ∧ unit ∧
completeness ∧ uniqueness). Bounded (MAX_QUANTITIES, refuse-on-overflow) +
deterministic. Target supplies the aggregation cue + question quantities; target-
UNIT matching is deferred (answer_unit=start.unit is wrong for cross-unit products
-> a unit gate would over-refuse; documented).
Honest practice measurement (sealed lane): 4 correct / 9 wrong / 37 refused
(baseline 3/0/47). +1 flip is the unambiguous whole-problem product (0021); the 9
wrongs are product-of-all eliminations on multi-step problems (caught by gold,
the learning signal). Whole-problem shapes add no coverage beyond the unambiguous
product WITHOUT cue precision: when product and sum both self-verify they disagree
-> uniqueness refuses (safe-but-low-coverage by design). The lever remains cue
precision (the ADR-0175 learning loop).
Microscope finding: 0003-class flips (48*24*0.75=864=gold) are blocked by a
DECIMAL/currency grounding gap -- '$0.75' tokenizes to 0/75 so '0.75' is not
grounded by the shared round-trip primitive. Not a search bug; deferred
extraction-richness work (won't casually change the serving round-trip primitive).
A test documents the current refusal so the fix is detectable.
wrong=0: serving untouched (sealed); ambiguity + no-licensed-cue refuse; routes
through the proven gate. 8 MS-3 tests; full derivation surface 77/77; ruff clean;
smoke 67.
MS-2 of multi-step composition. Extends the derivation model so a chain mixes
text-quantity operands and COMPARATIVE-scalar operands (twice->x2, 'N times'->xN,
half->x0.5), self-verifying the whole chain with completeness over body+question
and question-target matching.
- model.py: Step gains comparative flag.
- comparatives.py: ComparativeScalar gains number_token (the '<N> times' number,
so completeness counts the consumed body quantity); comparative_step(cs) bridges
a scalar into a Step (operand grounded by cue, not a text value token).
- verify.py: self_verifies exempts comparative operands from value-grounding
(clause 1) — they are cue-grounded (clause 2); completeness (Counter) counts a
digit comparative's number_token as consuming the body quantity. Adds target_units
to select_self_verified: a chain whose answer_unit isn't the asked unit is dropped
(question-target match; empty target_units imposes no constraint).
Proves the multi-step shapes from the gold structures: 0024 (text sum then 'three
times' scale -> 438), 0033 father-chain (digit-comparative '7 times' + fixed 'half'
+ text add -> 47). Full 0033 DAG (quantity reuse + the question's 25) deferred.
25 MS-2 tests; full derivation surface 69/69 (3a/3b/comparatives/ms1/ms2); ruff
clean; smoke 67. Not wired into serving (model ready for MS-3 target-guided search).
MS-1 of multi-step composition. Turns the question into a Target = what the
problem asks for, the search's pruning signal + stopping criterion (MS-3).
Lexeme-level only (ADR-0165): the existing question parser returns nothing on
these GSM8K questions, and 0165 forbids new question-shape grammar regex. Three
robust signals:
- quantities: numbers stated IN the question (0033's 'when she is 25') via the
body's lexeme extractor — they participate in the derivation.
- aggregation: presence of an aggregation lexeme (total/altogether/combined/sum/
'in all'/'in total') — soft hint the final step is a sum.
- units: asked units resolved by INTERSECTION with the body's known units
(precise lexeme match, e.g. 'jumping'). Superordinates (weight<->pounds) are
NOT faked — deferred to a curated superordinate-units pack; until then the unit
signal is precise-but-incomplete and the search leans on completeness.
Refuse-preferring: empty target field is not an error, just a weaker prune.
generate/derivation/target.py: Target + extract_target(question, known_units=()).
12 MS-1 tests (question-quantity, aggregation, body-unit intersection,
superordinate-not-faked, determinism, frozen). Verified: derivation suite 57/57;
ruff clean; smoke 67. Not wired into serving (Target ready for MS-2/MS-3).
The curated, irreducible world-fact primitives multi-step composition needs
(ADR-0175 section 10: the engine can't derive 'twice = 2' from arithmetic). The
microscope flagged these via the 0015/0025/0024/0033 wrongs.
language_packs/data/en_core_comparatives_v1/: 9 closed-set multiplicative
comparatives (twice/double/triple/quadruple/half/quarter + inflections) -> scalar
ops. manifest.json with sha256 of the bytes on disk (CLAUDE.md pack rule).
Refusal-preferring: non-terminating/ambiguous comparatives (a third, several)
deliberately excluded; expansion via HITL corridor.
generate/derivation/comparatives.py: extract_comparative_scalars() ->
ComparativeScalar(op, scalar, span, cue). Fixed lexemes + the '<number> times'
pattern (digit or word-number via WORD_NUMBERS). Lexeme-level (ADR-0165);
deterministic (text-order); supplies only the SCALAR primitive — referent
binding is the multi-step search's job (ADR-0176).
14 tests incl. refusal-preferring discipline + pack integrity (manifest checksum
matches bytes on disk). Verified: derivation suite 45/45; ruff clean; smoke 67;
packs 141. Not wired into serving (data + extractor ready for ADR-0176 MS phases).
Self-verification strengthening (microscope-driven). The Phase 3b measurement
showed self-verification was necessary-but-not-sufficient: 9/13 self-verified
attempts were wrong. Inspecting them deterministically revealed most were
correct FIRST STEPS of multi-step problems that ignored numbers stated elsewhere.
Adds clause 5 to self_verifies: a derivation must account for every quantity the
problem states (problem quantities subset of used). Refuse-preferring: unused
quantities -> not self-verified. This catches the multi-step-incomplete attempts
the grounding/cue/unit clauses cannot (their operands ARE grounded).
Practice measurement: wrongs 9 -> 2 (4 correct / 2 wrong / 44 refused). The 2
survivors (0015, 0025) are COMPLETE but wrong due to missed WORD-quantities
('twice', 'her friends') -> the microscope points the next change at extraction.
Updated the disagreement test to use two complete derivations; added an
incomplete-refusal test. 32 tests pass; smoke green; serving untouched (sealed).
ADR-0175 Phase 3b — the first live attempt generator. Runs only in the sealed
practice lane, only on cases the engine refused; every proposal is gated by the
Phase 3a self-verification gate.
generate/derivation/:
- extract.py: extract_quantities() — lexeme-level (number + unit word; ADR-0165).
- search.py: search_multiplicative() — one in-clause product candidate per
sentence with >=2 quantities + a present multiplicative cue; gated by
select_self_verified. Per-sentence scope + multi-candidate disagreement give
the uniqueness gate real teeth (two qualifying sentences -> refuse). The cue
set {each,every,for,per,times} is an explicit PROVISIONAL hypothesis the
practice loop refines, not a claimed-correct grammar.
evals/gsm8k_math/practice/v1/search_runner.py: search_augmented_scorer +
build_search_report — base scorer, then a practice-only attempt on refusals.
MEASUREMENT (the deliverable, per the breadth-of-impact test):
practice with search: correct=4 wrong=9 refused=37 (baseline 3/0/47)
- Flips +1 (0021, the clean in-clause aggregate) and its renumbered/reworded
variants (ADR-0114a perturbation guard) -> a real capability, not memorisation.
- 9 wrong attempts -> elimination records (§9), the learning signal. The naive
full-product cue model over-attempts; the eliminations are exactly the signal
that refines it.
HONEST FINDING: self-verification (grounding ∧ cue ∧ unit ∧ uniqueness) is
NECESSARY but NOT SUFFICIENT — 9/13 self-verified attempts were wrong vs gold.
The gap is cue PRECISION / which-quantities-compose (the knowledge axis), not
'can we multiply' (skill). This is why the search runs sealed: gold catches the
9, and case 0050 (canary) attempted-and-failed IN PRACTICE without touching
serving -> validates the seal.
Invariants: #1 seal (serving still 3/47/0; 0050 refuses in serving; no
generate/chat import of the lane), #3 determinism. Serving wrong=0 untouched.
Verified: 3a+3b 31/31; ruff clean; serving lane 4/4; smoke 67/67.
ADR-0175 Phase 3 splits wrong=0-first: build the gate (3a) and PROVE invariant #2
before the bounded search (3b) that could exploit gaps.
generate/derivation/:
- model.py: Quantity / Step / GroundedDerivation. A derivation is a left-fold over
text-sourced quantities; each Step carries its licensing cue (the lexeme the
search claims licenses the op).
- verify.py: self_verifies() — grounded operands ∧ grounded operation cues ∧ unit
consistency ∧ no divide-by-zero. Grounding REUSES the canonical primitives from
math_roundtrip (_tokens/_token_in/_value_grounds) so the gate cannot drift from
the round-trip contract. select_self_verified() adds the uniqueness rule:
unique self-verifying answer resolves; zero or disagreeing refuse (wrong=0).
INVARIANT #2 proven (TestInvariant2_NoSpuriousSelfVerification): the gate refuses
to self-verify a derivation that is not grounded+unit-consistent+unique even when
its value coincides with gold — the 20/5==4 class:
- invented operand not in text -> refused
- operation cue not in text -> refused (division not licensed by any present cue)
- value coincidence (20/5=4) with ungrounded op -> still refused
- add across units (pounds + reps) -> refused
- divide-by-zero -> refused
Plus uniqueness: disagreeing grounded derivations -> refuse; agreeing -> resolve.
Phase 3a is inert (nothing wires generate.derivation into serving). 3b is the
bounded search that produces derivations for this gate + measures the flip-curve
in the practice lane under perturbation.
Verified: 16/16; ruff clean; smoke 67/67; no serving import.
The recognizer/candidate-graph path is the single canonical reader.
Retires the flag-gated incremental-reader dispatch that admitted 0/50 on
train_sample and only added a dead fall-through:
- remove _try_comprehension_reader, _try_reader_for_question, _tokenize_sentence
and both dispatch blocks from generate/math_candidate_graph.py
- delete generate/comprehension/lifecycle_runtime_adapter.py (402 LOC,
used only by the question-reader dispatch)
- drop the comprehension_reader_questions config flag and the parse_and_solve
/ _score_one_candidate_graph config threading
- remove the --use-reader runner plumbing + flag-ON/OFF delta report from
the train_sample runner; refresh report.json (drops stale use_reader field
and a stale refusal-reason; verdicts unchanged at 3/47/0)
- remove the now-dead use_reader field from teaching/coverage.py
CoverageReport + the core teaching coverage CLI flag
- delete tests/test_reader_coexistence.py (flag-ON/OFF premise dissolved);
fix 3 ADR-0174 build_report calls and 2 subprocess invocations
lifecycle.py and audit.py are KEPT — they are load-bearing for the ADR-0172
math-contemplation teaching corridor (audit_problem -> teaching/math_*),
which a pre-deletion trace surfaced. The parent ADR's plan to delete
lifecycle.py was wrong; only its GSM8K scoring dispatch was inert.
Net -1,038 LOC (code + tests). Behavior-preserving:
- train_sample 3/47/0, byte-identical verdicts to pre-5a baseline
- determinism holds; smoke/packs/runtime/cognition/teaching lanes green
- contemplation corridor + lifecycle/audit tests pass
Pre-existing (NOT introduced here; reproduce on base with changes stashed):
5 out-of-curated-lane stale committed-artifact / stale-assertion failures
(test_math_evidence_e2e, test_adr_0126_runner_wiring, G3/coverage_probe
report-match, test_refusal_taxonomy_lane rebuild).
ADR-0174 Phase 3b — emit N anchors for compound-clause discrete-count
sentences sharing one subject + one verb. Architectural substrate;
score on train_sample preserved at 3/47/0 (compound cases like 0027
admit past the recognizer-injection refusal but the rest of the
problem still has downstream complexity — fractions, percent — that
needs Phase 4 + solver work).
generate/comprehension/state.py:
HYPOTHESIS_CAP raised 4 → 8. Case 0040 emits 5 anchors; cap=8
gives headroom (7-item lists) without becoming permissive.
generate/recognizer_match.py:
_try_extract_compound_discrete_count_anchors() — new extractor
emitting tuple of anchors for compound sentences. Refusal-
preferring on:
- no conjunctive separator (single-anchor path)
- multiplicative/percent/fraction markers
- head verb not in whitelist
- any tail clause without grounded (count, observed_noun) pair
- exceeding HYPOTHESIS_CAP
- unaccounted digit in tail (wrong=0 hazard defense surfaced by
2026-05-28 implementation review: bogusnoun would silently fail
to produce anchor while leaving the digit unaccounted, admitting
partial state)
Wired into _match_discrete_count_statement dispatch as fallback when
single-anchor extraction fails.
tests/test_adr_0174_phase3b_compound_clause.py:
11 acceptance tests passing — pure conjunctive lists (proper-noun
+ pronoun-subject + single-actor antecedent), refusal-preferring
discipline (mixed-verb, multiplicative-tail, non-whitelisted-head,
partial-grounding all-or-nothing), HYPOTHESIS_CAP enforcement,
multi-actor pronoun defense preserved on compound, wrong=0 +
case-0050 canary.
tests/test_adr_0174_phase1_held_hypothesis_state.py:
Updated test_hypothesis_cap_is_four → test_hypothesis_cap_is_eight
with rationale for the raise.
Phase 3b implementation lookback review (per CLAUDE.md doctrine):
- Surfaced silent-partial-admission hazard in tail extraction;
fixed with digit-accounting check before commit
- Surfaced LATENT regex-path multi-actor pronoun hazard (not
introduced by Phase 3b; documented in test docstring with
cross-reference to project-adr-0174-multi-actor-pronoun-hazard
memory for follow-up)
- case 0040 ('He now has...') remains refused — 'now' adverb between
subject and verb defeats the existing canonical regex. Adverb-
stripping is separate scope (not Phase 3b).
Acceptance:
- 258/258 ADR-0174 + math_problem_graph tests pass
- Smoke 67/67, packs 141/141
- train_sample 3/47/0 preserved (wrong=0 held)
- Case 0027 'Malcolm has 240 followers on Instagram and 500 followers
on Facebook' now admits via the compound extractor — verified by
refusal moving to the next sentence (which has 'half' fraction)
All findings from the 2026-05-28 Phase 1-3a lookback review addressed
in one commit on the Phase 3a branch:
Wrong=0 hazard defense (the load-bearing fix):
- generate/math_candidate_graph.py: Phase 3a wiring now collects the
set of distinct proper-noun subjects seen in prior context. When
more than one exists, refuses with no_antecedent_ambiguous trace
event rather than guessing the most-recent (which was gender-blind
single-binding — wrong attribution in multi-actor problems).
- Refusals from the statement loop now preserve _statement_trace via
reader_trace in CandidateGraphResult (pre-existing latent issue:
Phase 2/3 trace events were dropped on early statement refusal).
- New tests assert: ambiguous case refuses with correct trace; single-
actor case still resolves normally.
Test coverage backfills (closes the 13 untested predicate-name gaps):
- TestCheckConstraintsInitialPredicateNames — 3 tests asserting the
exact predicate name on initial.value_grounds / initial.unit_grounds
/ initial.entity_grounds failure paths.
- TestCheckConstraintsOperationPredicateNames — 3 tests asserting
operation.verb_grounds / operation.value_grounds / operation.unit_grounds
failure-predicate-name parity.
- TestCheckConstraintsComposedInitialPath — 4 tests for the RAT-1
composed_initial path which was entirely untested in Phase 2
(parity manually verified during lookback review; now automated).
ADR amendment (honest doc vs impl drift):
- docs/decisions/ADR-0174-held-hypothesis-comprehension.md: appended
'Implementation Notes' section documenting:
- reevaluate signature differs from spec text (shipped is more
composable; treat as amended)
- Phase 2 wires per-candidate, not per-token (per-token is Phase 5)
- Lookback recompute is candidate-level, not token-level
- Hypothesis.constraint_state is never populated by Phase 2
- Multi-actor pronoun hazard defense rationale
- Honest LOC accounting: Phases 1-3a net +1,500 lines (Phase 5
delivers the projected net removal)
- Test coverage backfill summary
Cosmetic:
- lookback.py:297 unreachable raise — added # type: ignore[unreachable]
with comment explaining defensive future-proofing for Phase 3b.
Acceptance verified:
- 124/124 Phase 1+2+3a + reader tests pass (was 95/95 before backfills)
- Smoke 67/67, packs 141/141
- train_sample 3/47/0 preserved (wrong=0 invariant held)
- Multi-actor hazard live-tested: parse_and_solve refuses the
Alice/Bob/She case with no_antecedent_ambiguous trace event
See CLAUDE.md §Lookback Review Discipline and memory
feedback-lookback-review-discipline for the doctrine that surfaced
all of these issues at the right time.
ADR-0174 Phase 3a — substrate for held-hypothesis lookback.
Score unchanged at 3/47/0 (this PR is correctly-engineered
infrastructure; eval impact gated on ADR-0163.x recognizer expansion
documented in the follow-up brief).
Adds generate/comprehension/lookback.py:
- VALID_REFINEMENT_KINDS, VALID_UNRESOLVED_SLOTS — closed sets
contracted with reader_trace consumer
- PronounResolution refinement dataclass (pronoun + resolved_to +
evidence_source, all validated)
- Refinement Union (Phase 3b will widen with CompoundClauseExpansion)
- ReevaluateResult dataclass with admit/eliminate consistency
- reevaluate(hypothesis, refinement) operator — applies refinement,
re-runs check_constraints, returns refined Hypothesis or None.
- _rebuild_candidate_with_resolved_actor — rebuilds
CandidateOperation / CandidateInitial replacing the semantic actor
field (op.actor / initial.entity) while preserving matched_actor_token
/ matched_entity_token as the pronoun (so grounding still passes
against the held statement's source span).
Modifies generate/recognizer_match.py:
- _try_extract_discrete_count_anchor: pronoun-subject statements now
emit anchors with subject_role=<pronoun> + requires_pronoun_resolution
marker, rather than refusing at the _REFUSED_SUBJECT_TOKENS check.
The other narrowness layers (clause split, verb whitelist) still
refuse; only the pronoun layer changes.
Modifies generate/math_candidate_graph.py:
- After inject_from_match, when any parsed_anchor carries
requires_pronoun_resolution, the candidates are held as Hypothesis
objects with unresolved=('actor_pronoun',). The lookback path then
resolves via the existing _discourse_prior_subjects map and runs
PronounResolution refinements through reevaluate. Resolved
hypotheses flow into per_sentence_choices as if the regex parser
had produced them; unresolved hypotheses drop cleanly (refusal-
preferring). Emits 'lookback' JSON trace events with
outcome ∈ {admitted, eliminated, no_antecedent}.
Tests:
- tests/test_adr_0174_phase3_lookback.py — 17 acceptance tests
covering operator semantics on Operation/Initial, dataclass
invariants, closed-set constants, end-to-end wiring on synthetic
problems, and wrong=0 preservation on train_sample.
Phase 3.1 follow-up brief:
- docs/handoff/PHASE-3.1-FOLLOWUP-RECOGNIZER-EXPANSION.md documents
the empirical finding that the train_sample bottleneck is
verb-coverage (recognizer scope, ADR-0163.x) not lookback
(ADR-0174 scope). 11 verbs identified for HITL contemplation pass.
Recommends sequencing: Phase 3a now (substrate), ADR-0163.x verb
expansion next, Phase 3b after coverage matures.
Acceptance verified:
- 17/17 Phase 3a tests pass
- 95/95 existing tests pass (Phase 1 + Phase 2 + brief_11 + reader_phase2)
- Smoke 67/67, packs 141/141, lanes 8/8
- wrong=0 preserved, score unchanged 3/47/0 (intentional per brief)
Stacks on Phase 2 (PR #420). Rebases onto main after #416 + #420 land.
ADR-0174 Phase 2 — hoist _initial_admissible / roundtrip_admissible into
hypothesis-based constraint checks with structured elimination tracing.
Admission semantics are byte-equivalent to today; the change is structural.
Adds generate/comprehension/constraint_propagation.py:
- VALID_PREDICATE_NAMES: closed set of 17 sub-check names spanning
initial / composed_initial / operation admissibility predicates.
Adding new names requires an ADR amendment (structural contract with
reader_trace consumer).
- ConstraintResult dataclass: admitted bool + predicates_run trace +
elimination_reason. Validates admitted-vs-reason consistency.
- Elimination dataclass: confidence_rank + predicate + reason for one
hypothesis being eliminated. Serialisable as a reader_trace event.
- hypothesis_from_initial / hypothesis_from_operation: adapters wrapping
CandidateInitial / CandidateOperation as Phase-1 Hypothesis objects
with caller-supplied confidence_rank.
- _check_initial / _check_composed_initial / _check_operation:
decomposed sub-check implementations of the existing admissibility
predicates with first-failure short-circuit (matches current
semantics). Each sub-check populates predicates_run with (name, ok|
fail|skip) so the consumer sees exactly which predicate decided.
- check_constraints: dispatches on candidate type.
- eliminate_violating: bulk filter; returns (survivors, eliminations);
survivors are re-densified to satisfy ProblemReadingState's
open_hypotheses post_init invariant (dense-from-0 ranks);
eliminations carry the original confidence_rank for trace fidelity.
Wires into generate/math_candidate_graph.py at the recognizer
injection site (line 825+): replaces inline _initial_admissible /
roundtrip_admissible dispatch with eliminate_violating. Elimination
events become JSON entries in reader_trace with layer=
'constraint_propagation', phase=2, predicate, reason, sentence_index.
Phase 2 acceptance verified:
- 24/24 ADR-0174 Phase 2 tests pass (emission, parity with existing
predicates on 9 admit/reject cases, redensification, dataclass
invariants, integration).
- 71/71 existing reader + Phase 1 tests still pass.
- Smoke 67/67, packs 141/141, lanes 8/8.
- train_sample/v1 byte-identical across two runs with use_reader=True.
- Score preserved: correct=3 refused=47 wrong=0 — semantics identical
because the decomposed sub-checks short-circuit on the same predicates
the inline checks would have caught.
Trace-event behavior: today's injectors are conservative enough that
zero eliminations occur on train_sample/v1 (no false positives, no
mid-pipeline failures). The wiring is exercised by
test_phase2_event_shape_when_synthesized which proves the trace shape
on a synthetic CandidateInitial that fails initial.unit_grounds. When
Phase 3 begins emitting partial hypotheses from apply_word, the
elimination path will fire on real candidates and the trace will
populate.
Stacks on Phase 1 (feat/adr-0174-phase1-held-hypothesis-state, PR
#416). Merges cleanly into main after PR #416 lands.
MathProblemGraph.__post_init__ now raises MathGraphError when two
InitialPossession entries share the same (entity, unit) key but
declare different quantity values.
Pre-fix behavior surfaced by 2026-05-28 ADR-0174 Phase 3 post-merge
diagnostic: math_solver.solve() line 207 used last-write-wins dict
assignment when consolidating initial state. Two contradictory
inputs would silently overwrite without trace:
'Sam has 5 marbles. Sam has 3 marbles. How many marbles does Sam have?'
→ returned 3.0 (wrong=0 violation: definite answer from
contradictory input)
Post-fix: same input refuses with 'no branch produced a solvable
graph' — refusal-preferring discipline as wrong=0 doctrine requires.
Identical duplicates (same value) are admitted as redundant (no
contradiction). Different units for same actor admitted. Different
actors for same unit admitted. Single-value cases (the dominant
real-world pattern) unchanged.
This is an extraction-layer hazard discovered while investigating
Phase 3b scope: Phase 3b compound-clause held hypotheses would
emit multiple CandidateInitial entries per sentence, exercising
exactly this consolidation path. Fixing the silent overwrite NOW
ensures Phase 3b admission doesn't silently produce wrong answers.
Acceptance:
- 4 new tests in TestContradictoryInitialPossessionsRefuse
- 165/165 test_math_problem_graph tests pass (was 161/161)
- Smoke 67/67, packs 141/141 unchanged
- train_sample 3/47/0 unchanged (no real case exercised the
overwrite — but the hazard was latent)
References: CLAUDE.md §Lookback Review Discipline (the doctrine
that surfaced this), CLAUDE.md §Non-Negotiable Field Invariant
(make illegal states difficult to represent).
ADR-0174 Phase 1 — substrate only, no admission behavior change.
Adds to generate/comprehension/state.py:
- HYPOTHESIS_CAP (=4, structural assertion per ADR-0174 §Constraints)
- VALID_HYPOTHESIS_CONFIDENCE_RANKS (closed set, no probabilistic ranking)
- Hypothesis dataclass (frozen, slots) — candidate, category_assignments,
constraint_state, confidence_rank, unresolved. The 'candidate' field is
typed as object to avoid circular import on math_roundtrip /
math_candidate_graph candidate types; Phase 2 will pin canonical_bytes
contract over real candidates.
- UnknownHeld dataclass — token, position, narrowed_categories (frozenset).
Substrate for Phase 3 'hold instead of refuse' on unknown words; Phase 1
introduces only the type.
- ProblemReadingState.open_hypotheses + unknown_held fields, both default
to () (empty tuple). Defaults preserve today's single-committed behavior
exactly. Confidence-rank uniqueness + density-from-0 enforced at
__post_init__ as structural invariants.
- Canonical-bytes serializer extended to handle frozenset (sorted list).
Phase 1 acceptance verified:
- 29/29 ADR-0174 Phase 1 tests pass (construction, validation, cap
enforcement, canonical-bytes determinism, frozenset stability).
- 42/42 existing reader tests pass (test_brief_11_audit +
test_reader_phase2) — default-empty fields preserve byte-identity.
- Smoke 67/67, packs 141/141.
- train_sample/v1 byte-identical across two runs with use_reader=True.
- wrong=0 invariant held: 3/47/0 unchanged.
No apply_word body changes. The 'thread the hypothesis set' requirement
at Phase 1 is satisfied by field defaults that propagate through every
ProblemReadingState construction site in lifecycle.py without code edits.
Phase 2 (continuous constraint propagation) and Phase 3 (lookback
re-evaluation) will populate these fields with real hypothesis data and
wire the EMIT / ELIMINATE / HOLD operators.
_unit_grounds() previously refused multi-word units like 'Pokemon cards'
even when both component words appeared as tokens in the source span.
The function checked unit_token against the haystack as a single key,
but the tokenizer splits source into per-word tokens — 'Pokemon cards'
was never going to match.
Fix is conjunctive by design: every component word must appear in the
haystack. A missing component refuses, preserving wrong=0.
Truth-test: case 0023 (Nicole/Pokemon cards) previously refused with
'recognizer matched but produced no injection' on its first sentence.
After this fix, sentence 1 passes injection cleanly; the case now
refuses on sentence 2 (Cindy/Rex compositional clause) — a more
honest refusal reason that reflects the actual remaining gap.
Score unchanged at 3/47/0 (no overall lift; correctness win).
smoke 67/67, packs 141/141, lanes 8/8 all green.
C1: delete generate/math_versor_arithmetic.py and its 3 tests (ADR-0139
add-only arithmetic spike; no runtime consumers, no pipeline wiring,
follow-on lift paused per module docstring).
C3: gitignore engine_state runtime artifacts (manifest.json,
recognizers.jsonl, discovery_candidates.jsonl). Module code
(engine_state/__init__.py) remains tracked; generated checkpoint
files should not be.
C5: document reader zero-delta root cause in train_sample/v1/README.md.
Both Phase 2 (whole-problem) and Phase 1 (question-only) reader paths are
called but inert because all 47 refusals are statement-level NO_INJECTOR
gaps, not question-sentence gaps. Reader unblocks when injector coverage
expands (C2 work). report.json use_reader flag corrected to reflect last run.
C6: add deprecation header to generate/math_parser.py pointing at
generate.math_candidate_graph.parse_and_solve as the live path.
C2/C4 briefs: docs/handoff/CLEANUP-C2-run-lane-migration.md and
docs/handoff/CLEANUP-C4-compositions-compile.md added as operator
dispatch docs for the medium-scope wiring tasks.
Addresses 5 of 47 train_sample "recognizer matched but produced no
injection" refusals (the largest single failure-mode bucket
identified in RAT-1's audit).
Modules
-------
- generate/recognizer_match.py:
- _MULT_AGG_EACH_WEIGHING_RE — regex for "<Subject> <bake-verb>
<M> <outer-noun>, each <weigh-verb>ing <N> <unit>" pattern
- _try_extract_each_weighing_anchor — extracts M, N, subject,
inner unit; emits pre-composed CandidateInitial(value=M*N) with
composition_evidence so RAT-1's _composed_initial_admissible
gate verifies INPUT tokens ground (preserves wrong=0)
- _match_multiplicative_aggregation dispatches to the value
extractor when spec carries extract_values=True; specs without
that flag get the existing detection-only return path
(byte-identical legacy behavior)
- generate/recognizer_anchor_inject.py:
- inject_multiplicative_aggregation — new per-category injector;
narrow by anchor.kind so ME-3/ME-4 additive/subtractive anchors
(which share the same matcher entry point) continue to flow
through composition_registry consult instead of WAVE-A's direct
path
- registered in _INJECTORS dict (2nd entry after DCS)
- core/cli.py:
- seed-recognizer CLI gains --extract-values flag to opt the
canonical_pattern into the value-extracting matcher path
Seeded artifacts
----------------
- proposals.jsonl: rat1-seed-4dc30608fb783bc7 — multiplicative_
aggregation recognizer with anchor_kind=multiplicative_aggregate,
extract_values=True, observed_units covering ounces/strawberries/
questions/etc.
Live result on train_sample
---------------------------
- wrong == 0 preserved (3/47/0 baseline)
- Case 0050 hazard pin held
- public 150/150 preserved
- packs suite: 127 → 131 (+4 new WAVE-A tests, all green)
- teaching suite 93 unchanged
- runtime suite 20 unchanged
End-to-end synthetic solve (FIRST WAVE-A admission):
"Lilibeth fills 6 baskets where each basket holds 50 strawberries.
How many strawberries does Lilibeth have?" → answer=300
Cases that moved (statement now admits; refusal shifted downstream):
- Case 0025 (Lilibeth): statement admits via WAVE-A; refusal moved
to question parser ("If three of Lilibeth's friends pick the same
amount, how many strawberries do Lilibeth and her friends pick in
all?")
- Case 0047 (John bakes 12 macaroons): statement 1 admits; refusal
moved to statement 2
Eval correct count unchanged because the QUESTION parser (and
multi-statement cross-sentence reasoning) is the next bottleneck.
RAT-1's audit identified that gap; WAVE-A closes the injector half.
The remaining 3 multiplicative_aggregation refusals (0006, 0013,
0045) have different shape patterns the WAVE-A regex does not yet
cover; they're follow-up matcher extensions in the same architecture.
Tests
-----
- tests/test_wave_a_multiplicative_aggregation_injector.py (10
tests): each-weighing + each-basket-holds admission shapes,
detection-only path preserved when extract_values absent,
unobserved unit / pronoun / zero count refusals, end-to-end
inject_from_match dispatch, the Lilibeth canary solve,
wrong=0 preserved, case 0050 hazard pin
Stacks on PR #406 (RAT-1).
The user's question — "shouldn't we be running it multiple times so
it can learn? or is that part broken?" — exposed that the math
teaching loop's `ratify → admit` closure had been structurally
broken at the connector between operator ratification and runtime
visibility. The handlers wrote source files (compositions/, frames/)
that the runtime loader never read because no compile step
regenerated the runtime artifacts.
This PR fixes the gap end-to-end AND fires the first live composition
admission on the canonical pack.
Modules
-------
- language_packs/compile_pack.py — unified compile step that
regenerates frames.jsonl + compositions.jsonl + updates
manifest.{frame,composition}_checksum atomically. Idempotent.
- teaching/math_composition_ratification.py — apply_composition_claim
now calls compile_pack at end of successful ratification. Closes
the source-file→runtime-artifact gap.
- teaching/math_frame_ratification.py — same auto-compile wire for
apply_frame_claim.
- generate/math_candidate_parser.py — CandidateInitial gains optional
composition_evidence Mapping field. When populated, signals the
candidate was produced by a registry-gated composition (ADR-0169);
the value/unit/entity are DERIVED arithmetic over grounded inputs.
- generate/math_candidate_graph.py — new _composed_initial_admissible
predicate that branches on composition_evidence. Wrong=0 preserved
by requiring each composition INPUT token (count, amount) to ground
in source_span literally; the derived value is admitted because the
arithmetic over grounded inputs is deterministic.
- generate/math_candidate_graph.py — discourse-level prior_subject
tracking: capture proper-noun subjects from ALL statement sentences
(including ADR-0136.S.0 context-filler sentences that get filtered
out before the candidate loop). Without this, "John adopts a dog"
(no numbers) is dropped and the cross-sentence subject resolver for
case 0019 sees prior_subject=None.
- generate/recognizer_match.py — all four composition matchers
(ME-1 currency-per-unit same-sentence, ME-2 cross-sentence, ME-3
additive, ME-4 subtractive) now populate composition_evidence in
CandidateInitial. Also added standalone " each " / " apiece " to
_PER_UNIT_TOKENS so currency_amount detection-only matcher refuses
per-item costs instead of swallowing them.
CLIs
----
- core teaching compile-pack — explicit operator surface for
regenerating runtime artifacts. JSON output for CI integration.
- core teaching seed-recognizer — operator surface for seeding a
RatifiedRecognizer entry in the proposal log for a given
(shape_category, anchor_kind). Writes created + transition(accepted)
events directly via ProposalLog._append.
Seeded artifacts (the actual loop closure)
------------------------------------------
- proposals.jsonl: new rat1-seed-48dd2673d6ad673d RatifiedRecognizer
entry for shape_category=rate_with_currency,
anchor_kind=currency_per_unit_composition.
- compositions/multiplicative_composition.jsonl: ratified
"bound(count) × bound(unit_cost)" affirms entry sourced from
case 0019 evidence.
- compositions.jsonl + manifest.composition_checksum: compiled
runtime artifact + manifest pin (RAT-1 auto-compile).
Live result on train_sample
---------------------------
- wrong == 0 preserved (3 correct / 47 refused / 0 wrong)
- Case 0050 hazard pin holds (refused)
- public split 150/150 preserved
- Case 0019 sentence 1 ("requires 3 vet appointments, which cost
$400 each") NOW ADMITS via composition. Previously refused with
"recognizer matched but produced no injection". The refusal moved
downstream to sentence 2 (a different currency_amount detection
bottleneck that is its own follow-up).
This is the first time a composition ratification on the canonical
pack actually reaches the runtime. The flywheel turned one
revolution.
Tests
-----
- tests/test_rat1_end_to_end_admission.py — 4 new live tests:
composition statement admits on isolated synthetic problem, case
0019 cross-sentence admission, wrong=0 preserved on train_sample,
case 0050 hazard pin.
- tests/test_consumption_empty_registry_no_op.py — refactored to use
isolated synthetic packs (the canonical pack may now carry ratified
entries).
- tests/test_math_{frame,composition}_ratification.py — updated
"manifest checksum unchanged" tests to "lexicon checksum
preserved" semantics: RAT-1 auto-compile may add the new optional
checksum fields; pre-existing lexicon checksum stays untouched.
Suite results: teaching 93, packs 131 (+4), runtime 20. All green.
Extends _match_multiplicative_aggregation with a new branch keyed on
anchor_kind="additive_quantity_composition". When a statement carries
"<Subject> <verb> <N> <unit> and <M> <unit>" (same unit) shape, emits
a pre-composed CandidateInitial(N+M, unit) and publishes
composition_shape="bound(qty_a) + bound(qty_b)".
Subject binding under Option A (refuse on pronoun / determiner / no
proper-noun head). Cross-sentence subject support (mirroring ME-2)
is deferred — not needed for the v1 ME-3 canaries.
Verb whitelist: lost / gained / earned / saved / made / paid / spent /
bought / sold / added / removed / received. Verbs that route through
CandidateInitial.matched_anchor's existing post-init whitelist;
unmapped verbs fall back to "had".
Unit normalization: rstrip 's' for plural matching (pounds vs pound).
Cross-unit composition refused — no conversion table in v1.
Tests (15 new, all green):
- same-unit admission with sum
- pronoun subject refuses
- determiner subject refuses
- cross-unit refuses
- unobserved unit refuses
- zero count refuses
- plural normalization
- unknown verb refuses
- multiplicative_aggregate detection path unaffected
- wrong anchor_kind refuses
- anchor audit fields complete
- source_span substring invariant
- no match returns None
- end-to-end admission via composition_registry
- end-to-end falsifies suppresses
Registered in core/cli.py "packs" suite. core test --suite packs -q →
106 passed (91 existing + 15 new).
Anti-regression invariants preserved:
- wrong == 0 on gsm8k_math public 150/150
- Case 0050 hazard pin holds
- ADR-0166 — no new eval lanes
- ADR-0167 partition — no cognition imports
- Original multiplicative_aggregate detection path byte-identical
- ME-1 currency-per-unit path unaffected
- ME-2 cross-sentence path unaffected
- engine_state/* not committed
Live train_sample admission requires the same operator workflow as
ME-2: a RatifiedRecognizer for the new anchor_kind + composition_registry
entry for "bound(qty_a) + bound(qty_b)" under additive_composition.
Without those, the wiring is correctly positioned but dormant — no
regression in the live eval.
Stacks on PR #401 (base: feat/matcher-extension-cross-sentence-subject).
Admits case 0019's composition sentence via prior_subject resolved
from upstream sentences. Stacks on PR #400 (ME-1).
Modules
-------
- generate/recognizer_match.py:
- _CROSS_SENTENCE_COMPOSITION_RE — regex for "requires N noun, which
cost(s) $X each" (no subject prefix)
- try_extract_cross_sentence_composition_anchor(statement, spec,
prior_subject) — refuses on None / empty / pronoun prior_subject;
publishes the same composition_shape + composed_initial payload as
ME-1, sourced via prior_subject
- extract_proper_noun_subject(statement) — head proper-noun extractor
used by callers to track running prior_subject; rejects determiners,
sentence-initial connectors (After/How/Every/...), and pronouns
- match() dispatcher gains keyword-only prior_subject parameter;
when a per-category matcher returns None for a RATE_WITH_CURRENCY
recognizer with currency_per_unit_composition anchor_kind AND
prior_subject is supplied, the cross-sentence helper is tried as
a fallback
- generate/math_candidate_graph.py:
- tracks _prior_subject across statement_sentences iteration
- passes prior_subject to recognizer_match.match()
- updates _prior_subject from each sentence's head proper-noun
Tests (19 new, all green)
-------------------------
- test_me2_cross_sentence_subject.py (15 tests)
- subject extraction narrowness (proper noun / determiner / connector
/ pronoun / non-string)
- cross-sentence helper happy path + refusals (None, empty, pronoun,
unobserved currency / per_unit, wrong anchor_kind, zero count,
multi-match)
- source_span substring invariant
- kind label "currency_per_unit_composition_cross_sentence"
- test_me2_case_0019_admits.py (4 tests)
- case_0019_admits_with_prior_subject_john — the truth test
- case_0019_refuses_without_prior_subject — ME-1 Option A still holds
- case_0019_refuses_with_pronoun_prior — refusal-preferring
- maria_same_sentence_unaffected_by_prior_subject — ME-1 path intact
Registered in core/cli.py "packs" suite.
Suite results
-------------
core test --suite packs -q → 91 passed (existing + ME-1's 21 + 19 new)
core test --suite runtime -q → 20 passed
core eval gsm8k_math --split public → 150/150, wrong=0
Scope boundary
--------------
The wiring is load-bearing AND tested end-to-end via synthetic
recognizer registry (test_case_0019_admits_with_prior_subject_john
proves the full chain match → inject → admit).
For the LIVE train_sample case 0019 admission, two ratifications must
also be seeded (operator workflow outside this PR's code scope):
1. A RatifiedRecognizer in the proposal log with shape_category=
RATE_WITH_CURRENCY and canonical_pattern carrying
anchor_kind="currency_per_unit_composition"
2. A composition_registry entry for "bound(count) × bound(unit_cost)"
under multiplicative_composition with polarity=affirms
With both ratifications in place, case 0019 admits via the wiring
this PR ships. Without them, the live train_sample run remains at
the 3/47 baseline (preserved; no regression).
Anti-regression invariants preserved
------------------------------------
- wrong == 0 on gsm8k_math public
- Case 0050 hazard pin holds (no _COMPOSITION_SUBJECT_BUY_RE or
_CROSS_SENTENCE_COMPOSITION_RE match on case 0050's sentences)
- ADR-0166 — no new eval lanes
- ADR-0167 partition — no cognition imports
- ME-1 Maria same-sentence path byte-identical (test pins)
- Existing currency_per_unit_rate path unaffected (test pins)
- prior_subject is keyword-only on match() (additive; old callers
unaffected)
- engine_state/* not committed
Stacks on PR #400 (base: feat/matcher-extension-currency-per-unit-composition).
Closes the consumption-half of the math teaching loop for two of three
sub-types per docs/handoff/CONSUMPTION-WIRING-DISPATCH-PACK.md (PR #397).
Companion to the doctrinal brief in PR #396.
Modules
-------
- language_packs/compile_frames.py — byte-deterministic compile of
frames/*.jsonl → frames.jsonl (sorted by (frame_category, surface_form))
- language_packs/compile_compositions.py — same shape for
compositions/*.jsonl → compositions.jsonl
- generate/comprehension/frame_registry.py — load_frame_registry()
mirroring load_lexicon: cache by (path, mtime, sha256), manifest
checksum verification (optional frame_checksum field), polarity
validation, conflict detection, empty-registry no-op
- generate/comprehension/composition_registry.py — same shape PLUS:
* SAFE_COMPOSITION_CATEGORIES enforced at LOAD (defense in depth;
raises WrongCompositionCategory on any unsafe category — protects
against pack edits that bypass the handler)
* polarity "falsifies" exposed via is_falsified() (consumer must
suppress; not silently treated as affirms)
- language_packs/compiler.py — manifest verification extended for
frame_checksum + composition_checksum, mirroring the proven
glosses_checksum pattern (optional fields; backward-compatible)
- generate/recognizer_anchor_inject.py — inject_from_match consults
composition_registry when the per-category injector returns empty
AND the matcher publishes ``composition_shape`` in parsed_anchors.
Registry is a gate (admissibility) not an arithmetic primitive
(ADR-0169 §"Mutation boundary").
Tests (38 new, all green)
-------------------------
tests/test_frame_registry_load.py (11 tests)
tests/test_composition_registry_load.py (11 tests)
tests/test_composition_consult_in_injector.py ( 6 tests)
tests/test_consumption_case_0050_hazard_pin.py( 3 tests, parametrized
over allowlist)
tests/test_consumption_empty_registry_no_op.py( 4 tests)
tests/test_consumption_partition.py ( 3 tests)
Registered in core/cli.py "packs" suite.
Suite results
-------------
core test --suite teaching -q → 93 passed
core test --suite runtime -q → 20 passed
core test --suite packs -q → 51 passed
core eval gsm8k_math --split public → 150/150, wrong=0
Truth-test rows (6-row binding table in dispatch pack):
#1 Case 0019 admits ............. PARTIAL — see Scope Boundary below
#2 Case 0050 stays refused ....... PASS
#3 train_sample 3/47 → ≥4/46 ..... PARTIAL — same as #1#4 wrong == 0 preserved .......... PASS
#5 public split 150/150 .......... PASS
#6 Empty-registry no-op .......... PASS
Scope Boundary (honest finding)
-------------------------------
Rows #1 and #3 (case 0019 admission) require a matcher extension that
publishes ``composition_shape`` + a pre-composed CandidateInitial in
parsed_anchors. The existing currency_amount / multiplicative_aggregation
matchers in generate/recognizer_match.py are detection-only (return
empty parsed_anchors). This PR ships the consumption infrastructure
correctly but the runtime path remains dormant until a follow-up PR
extends the matcher. The dispatch pack's truth test #1/#3 cannot fire
without that extension.
The wiring is positioned correctly: inject_from_match → consult
composition_registry → admit on affirms-with-payload, suppress on
falsifies, refuse on absence. A synthetic recognizer match with
populated composition_shape + composed_initial DOES admit through the
new path (covered by 6 tests in test_composition_consult_in_injector.py).
A follow-up brief naming the matcher-extension work is the
recommended next step.
Anti-regression invariants verified
-----------------------------------
- wrong == 0 on core eval gsm8k_math (public 150/150)
- case 0050 stays refused (parametrized over allowlist categories)
- ADR-0166 — no new eval lanes
- ADR-0167 partition — no cognition imports in any new module
- Empty-registry runtime byte-identical to today (no-op test)
- SAFE_COMPOSITION_CATEGORIES enforced at write AND load
- polarity semantics (affirms vs falsifies) honored
- engine_state/* never committed
Second implementation PR of the ADR-0170 wave. Extends the DCS injector
to emit ``CandidateOperation(kind='add')`` for acquisition verbs
alongside the existing ``CandidateInitial`` emission for possession
verbs. Proves the W1 type-widening with real emission of both union
members.
## What changes
### `generate/recognizer_match.py`
- New `_ACQUISITION_VERBS` frozenset (12 verbs: collect/get/receive/buy
inflections). Each member is a subset of `ADD_VERBS` so the downstream
CandidateOperation post-init whitelist accepts the matched_verb token.
- Extractor now accepts either possession OR acquisition verbs and
records `anchor_kind` (`"possession"` | `"acquisition"`) plus
`verb_token` in the parsed anchor schema.
### `generate/recognizer_anchor_inject.py`
- `inject_discrete_count_statement` dispatches on `anchor_kind`:
- `"possession"` → `CandidateInitial` (existing behavior unchanged)
- `"acquisition"` → `CandidateOperation(add)` (new)
- New helper `_build_operation_from_discrete_count_acquisition`
constructs the operation. Operand uses `_resolve_count_value`;
matched_verb uses `_locate_token` for round-trip ground check.
- Return type uses `InjectorEmission` from W1.
### Tests
- `tests/test_adr_0170_w2_dcs_acquisition_verbs.py` (new) — 22 tests:
- Verb-set membership pins
- Acquisition ⊂ ADD_VERBS sanity check
- Possession + Acquisition disjoint
- Extractor records anchor_kind correctly
- Injector emits CandidateOperation for acquisition verbs
- Possession path still emits CandidateInitial unchanged
- Deliberate exclusions (gained / donated / saved) still refuse
- Case 0050 hazard pinned (does/contemplates not in either set)
- Determinism + roundtrip_admissible passes
- Updated `tests/test_adr_0163_d2_discrete_count_injection.py` to
reflect new anchor schema fields (anchor_kind, verb_token).
- Updated `tests/test_adr_0170_w1_injector_type_widening.py` —
the DCS injector now legitimately returns
`tuple[InjectorEmission, ...]` (not narrower).
## Deliberate exclusions
These verbs are NOT in `_ACQUISITION_VERBS` and the extractor refuses
them — preserving wrong=0:
- `gained / gains / gain` — delta-of-attribute (weight, age), not
acquisition. Admitting as add-operation would risk wrong>0 on
questions that ask total state.
- `donated / donates / donate` — SUBTRACT semantics (actor gives away).
- `saved / saves / save` — ambiguous (time vs money vs effort).
Widening this set is operator-reviewable per `feedback-wrong-zero-
hazard-case-0050` discipline.
## ADR-0131.G.1 branch-disagreement discipline preserved
The regex parser already emits `CandidateOperation(add)` for
acquisition verbs via `ADD_VERBS` for single-word units. The new DCS
injector path emits the same kind of operation for multi-word units
(where the regex parser fails). Collapsed-tie when both paths emit
identical operations on overlapping shapes; no disagreement.
## Test plan
- tests/test_adr_0170_w2_dcs_acquisition_verbs.py: 22 passed (new)
- tests/test_adr_0163_d2_discrete_count_injection.py: ~30 passed
(existing tests updated for new schema fields)
- tests/test_adr_0170_w1_injector_type_widening.py: 6 passed
- tests/test_recognizer_skip_wrong_zero.py + brief_11b + brief_11 +
candidate_graph_wiring + candidate_domain_partition: passed
- evals/gsm8k_math/train_sample/v1: counts=correct=3 refused=47 wrong=0
unchanged (case 0023 still has S2/S3 downstream blockers; W2's value
is infrastructure, not direct lift)
## Hard invariants
- `wrong == 0` preserved (case 0050 hazard pin + deliberate verb
exclusions + roundtrip_admissible gate)
- ADR-0166: no new eval lanes
- No teaching-store / pack mutation
- ADR-0131.G.1 branch-disagreement discipline preserved (acquisition →
operation, not initial)
- Five-layer wrong=0 safety net (ADR-0163.D.2) intact and extended
## W3 NOT in this PR — honest skip
Initial plan was to bundle W2 + W3 (A1 currency_amount injector).
Inspection of the 4 actual `currency_amount` GSM8K refusals showed
none match A1's narrow form (`<ProperNoun> earns|charges $<amount>`):
| Case | Statement | Reason narrow form doesn't fit |
|---|---|---|
| 0019 | "this requires 3 vet appointments, which cost $400 each" | anaphoric subject + multi-quantity |
| 0026 | "Aaron and his brother Carson each saved up $40" | multi-subject + "each" |
| 0028 | "It cost $100,000 to open initially" | pronoun subject |
| 0043 | "Her mother gave her an additional $4, and her father twice as much" | multi-clause + comparative + transfer |
Shipping W3 as-designed would have re-introduced the dead-code pattern
#373 just cleaned up. Skipped honestly; ADR-0172 Tier 1's decomposer
(the next wave) will surface category-shape mismatches like this
programmatically.
First implementation PR of the ADR-0170 wave. Type-level widening only:
the recognizer-injector dispatch now returns
``tuple[InjectorEmission, ...]`` where
``InjectorEmission = CandidateInitial | CandidateOperation``.
The existing ``inject_discrete_count_statement`` continues to emit only
``CandidateInitial`` — the widening unlocks but does not exercise
operation emission. Subsequent W2-W5 PRs ship the per-injector emission
shapes:
- W2 — DCS-S1 acquisition verbs (CandidateOperation(add))
- W3 — A1 currency_amount (CandidateInitial reimplementation)
- W4 — A3 multiplicative_aggregation (CandidateInitial(product))
- W5 — A4 temporal_aggregation (deferred until apply_rate primitive)
## Changes
### `generate/recognizer_anchor_inject.py`
- New `InjectorEmission = Union[CandidateInitial, CandidateOperation]`
- `inject_from_match` return type widened to
`tuple[InjectorEmission, ...]`
- `__all__` exports `InjectorEmission`
- Documentation comment names ADR-0170 §"Implementation outline"
### `generate/math_candidate_graph.py` (admissibility dispatch)
The per-statement admission loop now dispatches admissibility on the
concrete candidate type:
if isinstance(c, CandidateInitial):
if _initial_admissible(c): admitted.append(c)
elif isinstance(c, CandidateOperation):
if roundtrip_admissible(c): admitted.append(c)
No new admission semantics — each type is gated by the predicate it was
already gated by elsewhere in the codebase. The dispatch unifies the
injector path with the parser path.
### `tests/test_adr_0170_w1_injector_type_widening.py` (new)
- Pin: `InjectorEmission` union members are exactly the two candidate types
- Pin: `inject_from_match` return type is widened
- Pin: `inject_discrete_count_statement` still emits CandidateInitial (W1
is type-level only)
- Hazard pin: case 0050 remains refused
- Hazard pin: unparseable-verb refusal path (#359) unchanged
- Anti-regression: canonical DCS narrow-form extraction still works
## Test plan
- tests/test_adr_0170_w1_injector_type_widening.py: 6 passed (new)
- tests/test_adr_0163_d2_discrete_count_injection.py: 21 passed
(existing D.2 v1 injector regression)
- tests/test_brief_11b_audit_artifact.py + step2_lexicon +
recognizer_skip_wrong_zero + brief_11_audit: 55 passed
- tests/test_candidate_graph_recognizer_wiring.py: 7 passed
- tests/test_candidate_domain_partition.py: 5 passed
- tests/test_adr_0131_G2_comparatives + G4 + G5 + S1_rate_events:
130 passed
- Total: 225 passed
- evals/gsm8k_math/train_sample/v1: counts=correct=3 refused=47 wrong=0
(unchanged; verified no behavioral regression)
## Hard invariants
- `wrong == 0` preserved (admissibility dispatch is type-aware but
semantically identical to the parser path's gating)
- ADR-0166: no new eval lanes
- No teaching-store / pack mutation
- Five-layer wrong=0 safety net (ADR-0163.D.2) intact
- Reader path unchanged
Three concrete cleanup items from the day's work, per the
cleanup-as-you-find memory principle.
## 1. Remove inject_rate_with_currency stub
PR #369 (A2 rate_with_currency) shipped a function that always returns
() with an extensive docstring documenting the Rate-not-in-SentenceChoice
schema gap. The function is dead at runtime — `_INJECTORS.get(category)`
returning None has the same downstream behavior as the function
returning (). The 16 tests pinned the empty-tuple return; the case-0050
hazard pin is duplicated in test_recognizer_skip_wrong_zero.py and
test_brief_11b_step2_lexicon.py.
The schema gap is now properly documented in ADR-0170 (PR #372). A
dispatch-table comment at the removal site retains the at-code pointer
to that ADR for anyone wiring a new injector.
Removed:
- `inject_rate_with_currency` function in generate/recognizer_anchor_inject.py
- Its `_INJECTORS` dispatch table entry
- Its `__all__` export
- tests/test_injector_rate_with_currency.py (371 lines, 16 tests)
## 2. Remove docs/handoff/GPT55-MOBILE-DISPATCH.md
Single-session travel-time scaffolding. The 5 tasks it named are
complete or superseded by ADR-0170's findings. Pure historical artifact.
## 3. Remove docs/handoff/WAVE-NEXT-INJECTORS.md
Superseded by docs/handoff/WAVE-NEXT-REVISED.md, which captures
everything load-bearing from the original brief in its A1–A4 findings
table. The "kept for history" justification didn't survive scrutiny:
the document was misframed (over-promised lift; misframed schema work
as injector work). Lessons captured in REVISED + ADR-0170.
Updated cross-references:
- WAVE-NEXT-REVISED.md: removed the "supersedes ... kept for history"
pointer; tightened cross-reference list
- ADR-0167-FOLLOWUPS.md §7: rewrote pointer to name ADR-0170 + REVISED
as the live plan rather than "the original is retained"
## Test plan
- 219 tests passed across G.2/G.4/G.5/S1/Brief 11/B1/B11A/wiring/partition/DCS-D.2
- evals/gsm8k_math/train_sample/v1/report.json untouched (regen
surfaces a separate stale-baseline test issue — out of cleanup scope)
- No runtime behavior change
## Net impact
- 5 files removed (~1200 lines)
- 1 file modified for explanatory comment (~30 lines)
- 2 doc files updated to remove dangling cross-references
- 0 behavioral change
Wave-Next A2 brief outcome: the Rate type (ADR-0122) DOES structurally
model a per-unit rate, but it is not a member of the per-sentence
injector contract's SentenceChoice union (CandidateInitial |
CandidateOperation). The injector therefore returns () and documents
the schema gap inline plus in audit_brief_11.md.
Lift count: 0 (expected — the brief explicitly anticipates this
outcome when the schema decision is "no"). Documenting the gap is
the deliverable.
- generate/recognizer_anchor_inject.py: new inject_rate_with_currency
+ dispatch-table entry routing ShapeCategory.RATE_WITH_CURRENCY.
- tests/test_injector_rate_with_currency.py: 16 tests pinning schema
evidence, schema refusal, dispatch wiring, case 0050 hazard,
determinism, and wrong=0 invariant.
- evals/gsm8k_math/train_sample/v1/audit_brief_11.md: appended
Wave-Next A2 section documenting the schema decision, eval delta
(3/0/47 unchanged), case 0050 hazard verification, and the
CandidateRate follow-up sequencing.
Case 0050 hazard pin: sentence 0 ("Mark does a gig every other day
for 2 weeks.") carries no currency symbol — rate_with_currency
never matches it; case stays refused at sentence_index=0.
## Summary
Two test failures on origin/main both trace to PR #315 (ADR-0163.D.2 —
discrete_count_statement recognizer + admissibility-intent chain). Earlier
runs treated them as "pre-existing unrelated" — they are not unrelated.
The first is a real wrong>0 hazard.
## Failure 1: silent admission via recognized-but-uninjectable statement
The ratified `discrete_count_statement` recognizer over-matches: ANY
sentence containing a number + noun resolves it, irrespective of the verb.
When `inject_from_match` returns `()` (the round-2 default for v1
categories without an injector), the old code path used `continue` to
silently drop the statement — and the solver then answered from whatever
initial state remained.
Reproduction:
parse_and_solve("Sam has 5 apples. Sam contemplates 3 apples. "
"How many apples does Sam have?")
→ is_admitted=True, answer=5.0 (silent admission of partial graph)
This is exactly the case-0050-class hazard wearing a different hat
(silently admitting an incomplete graph at the problem level).
ADR-0167 / Brief 11 §"correct-count greed" established the principle on
the reader path; this commit extends it to the recognizer path.
Fix: when a recognizer matches but produces no injection, REFUSE.
generate/math_candidate_graph.py:
- Replaced the skip-only `continue` with a CandidateGraphResult
refusal carrying the recognizer category in the reason.
tests/test_math_candidate_graph.py:
- test_unparseable_statement now accepts either the legacy
"no admissible candidate" reason or the new
"recognizer matched but produced no injection" reason.
Both legitimately refuse; what matters is is_admitted=False.
tests/test_recognizer_skip_wrong_zero.py (NEW):
- 5 regression tests pinning the wrong=0 invariant:
* 3 parametrized verbs unknown to both regex parser and reader
(contemplates / ponders / memorises) — must all refuse
* Nonsense token — must refuse
* Anti-regression: known initial + known operation still admits
## Failure 2: cognition audit drop-reason taxonomy
The audit test hardcoded `dropped.reason.startswith("superseded_by:")`
as the only valid drop-reason prefix. Commit da70919 (ADR-0163.D.2)
ratified an admissibility-intent chain that the audit categorizes with
reason `unsupported_intent:admissibility`, which fails this assertion.
Fix: tests/test_teaching_audit.py — expand the allowed-prefix set to
include `unsupported_intent:` with a written rationale. Future drop
classes extend the allowlist deliberately rather than silently
broadening the assertion to any non-empty reason.
## Surfaced regression: partition-test allowlist (ADR-0167 FOLLOWUPS §2)
This PR modifies three test files that the
test_existing_cognition_tests_untouched assertion would reject under
its named-allowlist scheme. Added the three test paths to the allowlist
as the tactical fix; the architectural fix (retire / move to CI / move
to CODEOWNERS) is queued in docs/handoff/ADR-0167-FOLLOWUPS.md §2.
## Test plan
uv run pytest tests/test_recognizer_skip_wrong_zero.py \
tests/test_math_candidate_graph.py \
tests/test_teaching_audit.py \
tests/test_candidate_domain_partition.py \
tests/test_math_evidence_e2e.py \
tests/test_math_evidence_schema.py \
tests/test_math_contemplation_adapter.py \
tests/test_math_claim_signature.py \
tests/test_math_lexical_ratification.py \
tests/test_brief_11b_audit_artifact.py \
tests/test_brief_11b_step2_lexicon.py \
tests/test_brief_11_audit.py
→ 152 passed
## Hard invariants
- wrong == 0 — restored on the recognizer path (was silently violated on main)
- ADR-0166 — no new eval lanes
- No teaching-store mutation, no pack mutation
- The reader path was already correct (it refused these cases); this fix
brings the regex/recognizer path back in line
## Summary
PR 11B in the Brief 11 sequence. Closes the missing-operator inference gap
left by 11A (#343) and ships the per-case audit artifact that Brief 11 §Gate 2
identifies as "the main Brief 11 artifact."
## Why this PR does NOT touch the reader runtime
The naive closure fix for `pre_frame_filler_sentence` (drain
`statement_terminator` at pre-frame) lifts 2 cases from refused → admitted
but creates a `wrong > 0` hazard on `gsm8k-train-sample-v1-0050`:
```
Mark does a gig every other day for 2 weeks. For each gig, he plays 3 songs.
... How many minutes did he play?
```
With the drain enabled, the reader admits `Operation(mark, add, 3, songs)`
with unknown unit `minute` and would project to a wrong answer. The stricter
variant (`pending_entity_ref is None` + no quantities) fires on 0 of the 11
candidate cases. Per Brief 11 §"Failure modes to avoid §1 — Correct-count
greed," this PR rejects both variants and routes the closure fix to a
follow-up that adds the required verb vocabulary or sentence-intent
classifier.
## Deliverables
- `generate/comprehension/audit.py` — three new missing-operator labels:
- `pre_frame_filler_sentence` (8 cases)
- `descriptive_frame_question` (2 cases)
- `question_frame_slot` (1 case)
Closes the 11-case `None`-operator gap left by 11A.
- `evals/gsm8k_math/train_sample/v1/audit_brief_11.json` — per-case audit
artifact pinned by tests.
- `evals/gsm8k_math/train_sample/v1/audit_brief_11.md` — narrative summary
including the rejected-fix design tension and ranked Brief 11B-step-2
backlog.
- `tests/test_brief_11b_audit_artifact.py` — 12 tests pinning the new labels,
the per-case artifact, the wrong=0 invariant, and the refusal taxonomy.
## Bottleneck taxonomy (after Brief 11B labelling)
| missing_operator | count | category |
|-------------------------------|------:|------------------------|
| quantity_extraction | 9 | incomplete_operation |
| lexicon_entry | 9 | unknown_word |
| multi_quantity_composition | 8 | incomplete_operation |
| pre_frame_filler_sentence | 8 | unexpected_category |
| pronoun_resolution | 3 | unresolved_pronoun |
| fraction_percentage_literal | 3 | unexpected_category |
| unit_binding | 3 | unattached_quantity |
| descriptive_frame_question | 2 | unexpected_category |
| (others, 1 each) | 5 | various |
## Test plan
- 12 new tests in `tests/test_brief_11b_audit_artifact.py` pass
- 23 existing 11A tests in `tests/test_brief_11_audit.py` pass
- No runtime changes; reader byte-identical to main
## Hard invariants preserved
- `wrong == 0` — no runtime change, no new admissions
- ADR-0166 — no new canonical eval lanes added; existing
`evals/gsm8k_math/train_sample/v1/` artifact set extended
- No teaching store / pack mutation
## Follow-up
- **11B-step-2** — verb-vocabulary expansion or sentence-intent classifier
for `pre_frame_filler_sentence` (8 cases). See audit_brief_11.md §"design
tension" for the rejected one-line variants and why they fail wrong=0.
- **11C** — existing-lane capability snapshot (still gated on 11B-step-2 or
another closure pass).
Extend the comprehension reader from question-only scope to whole-
problem scope. Phase 1 (Brief 8 / #326) implemented question_frame;
this brief implements initial_state_frame, operation_frame, and
descriptive_frame, plus finalize() projection into a strict
ADR-0115 MathProblemGraph.
Architecturally correct under ADR-0164.3; not yet productive on
GSM8K train_sample. Below-floor measurement documented; specific
bottlenecks tabled for Phase 2.1 follow-up.
What landed
- Frame-opener dispatch in lifecycle.py for the three new statement
frames, plus rule handlers (_rule_op_*, _rule_preframe_*,
_rule_descriptive_*).
- finalize(state) -> MathProblemGraph | ReaderRefusal: pure
projection with closure checks (entity registry non-empty,
unknown target bound, every op/initial references a known entity,
Decimal precision projects losslessly).
- _classify extended to 3-tuple (category, surface, decimal_value)
with possessive strip retry. Brief 8.2's sentence-initial
lookup-first + gender-skip preserved AND extended to mid-sentence
(gender is enrichment everywhere, never admission).
- Whole-problem coexistence dispatch in math_candidate_graph.py
(config.comprehension_reader_questions=True): reader attempts the
whole problem; on any ReaderRefusal falls through to existing
regex parser. All-or-nothing per the brief.
- Lexicon expansion (carried into renamed proper_noun_gender_*
files): +2 accumulation_verb (adopt, invest), +2 currency_unit_noun
(dollar, cent), +6 capacity_verb (fill, lift, play, work, finish,
drive), +5 female names (allison, brooke, jan, marion, sidney),
+14 male names (bart, fernando, georgie, jake, jed, jeremie, jose,
orlando, rex, rudolph, steve, troy, xavier, yun), +numerous
count_unit_noun, drain_token, time_unit_noun.
- ADR-0164.4-phase2-statement-frame-reader.md — the architectural
rationale and acceptance contract.
Measurement (reader_phase2_delta.json):
flag-OFF: correct=3 refused=47 wrong=0
flag-ON: correct=3 refused=47 wrong=0
delta: 0/0/0
Below the brief's floor of correct >= 4. Architecture is sound — the
reader admits cases as graphs when the structure resolves, refuses
cleanly otherwise, preserves wrong=0 across both flag states.
Bottleneck table (from per-case attribution):
count refusal_class dominant cause
----- ---------------------- ------------------------------------
18 incomplete_operation multi-quantity ops; no-quantity op
11 unknown_word "hundred", "presently", "one-hour",
non-math verbs (compound numerics,
lexicon gaps)
6 unexpected_category fraction / percentage literals;
multi-subject sentences
6 unresolved_pronoun "them", "their", "his" with no
compatible entity
5 unattached_quantity quantity never bound to a unit
1 no_question_target question parsed but slot never set
Closing the gate to mixed-bounded [4, 24] is Phase 2.1 scope: extend
composition rules for multi-quantity ops, add fraction/percentage
primitives (per ADR-0164.1 amendment), expand lexicon for the
remaining unknown_word cases, extend pronoun resolution.
Invariants preserved
- wrong = 0 in both flag states ✓
- flag-OFF byte-identical to today ✓
- determinism (50/50 identical runs) ✓
- Capability axes G1-G5, S1 unchanged ✓
- Reader tests: 19 (Phase 2) + 18 (Phase 1, post-update) + 53 (pack)
+ 76 (lexicon + primitives) = 166 specific to this change; all pass
- core test --suite smoke -q: 67 passed
Rebase note
This PR was authored against an older base; rebased onto current
main to incorporate #333 (Brief 8.2 universal proper_noun_token
primitive) and #334 (ADR-0166 measurement discipline). The rebase
required:
- Lexicon files renamed proper_noun_entity_* -> proper_noun_gender_*
(with the Phase 2 additions merged into the gender_* files)
- Compiled lexicon.jsonl unchanged from #333's 207-entry state
(Phase 2's per-category additions are runtime-visible via the
source loader, not via the compiled file)
- _classify reconciled with Brief 8.2's sentence-initial dispatch +
Phase 2's 3-tuple decimal-value return
- All dispatch tables and category checks updated to reference
proper_noun_token (singular) instead of proper_noun_entity_{f,m}
- Three Phase 1 test expectations updated to reflect Phase 2
behavior (proper noun at position 0 now opens statement pre-frame
instead of refusing; pronoun resolution applies per ADR-0164.2)
Per ADR-0166's three-question test, this PR is honest measurement:
capability exists, at least one case admits, lane distinguishes
presence from absence — which the bottleneck table demonstrates.
Refs ADR-0164.3 §Phasing Phase 2, ADR-0164.1 amendment (Brief 8.2),
ADR-0166 §"Mixed (notable but not blocking)" — except here, below
floor.
ADR-0164.1 amendment: replace name-whitelist entity admission with a
universal lexeme primitive that recognizes any capitalized token as a
proper noun. The gender-coded name lists are demoted from admission
criterion to enrichment-only lookup. A name outside the curated lists
still admits cleanly with gender="unknown" — ADR-0164.2's pronoun
resolution rules handle the unknown case via single-salient fallback
or refuse with ambiguous_pronoun_referent.
Universal at the primitive layer: the new proper_noun_token primitive
is domain-agnostic. It sits in the shared PRIMITIVE_REGISTRY and is
available to every current and future reader (math, narrative,
code-comment, multi-lingual). The math reader is its first consumer.
Pattern: ^[A-Z][A-Za-z'-]*[a-z][A-Za-z'-]*$
- requires capitalized first letter
- requires ≥1 lowercase letter (rejects all-caps acronyms)
- allows internal apostrophes (O'Brien) and hyphens (Mary-Anne)
- matches "Tina", "Bob", "Marnie", "McDonald" — rejects "TINA",
"123", "$5.00" (those go to their own primitives)
Sentence-initial lookup-first dispatch (lifecycle._classify):
- At token_index == 0: lookup() first, skipping proper_noun_gender_*
categories (treated as not-found so the primitive can fire). If
lookup misses, primitive scan picks up novel names. Inverts the
question from "is this a name?" to "is this a known common word?"
- At token_index > 0: primitive-first with UNIT_CATEGORY_TOKEN ceding
to operational lexicon for currency_unit_noun overrides.
Lexicon rename (per-category source files):
- proper_noun_entity_female.jsonl -> proper_noun_gender_female.jsonl
- proper_noun_entity_male.jsonl -> proper_noun_gender_male.jsonl
Compiled lexicon.jsonl: rename the two semantic_domain tags; drop
"marnie" (was only in proper_noun_entity_female, now absent from
the gender-coded sources). Net: 208 -> 207 entries. New manifest
checksum: 1fb9b0d790258736267d528e8e8a2436ce88b9ce690805fe2813ba077861ba2a
New helper gender_of_proper_noun(surface, lexicon) returns
Literal["female","male","neuter","unknown"] — pure enrichment lookup,
never gates admission.
Measurement (reader_phase1_plus_proper_noun_delta.json):
- pre-primitive baseline: correct=3 refused=47 wrong=0
- post-primitive measurement: correct=3 refused=47 wrong=0
- No regression on wrong=0
- No net admission increase observed in this train-sample harness;
the architectural value is for future text outside the curated
gender lists (Sonnet's #332 expanded those to cover GSM8K names).
Tests:
- test_lexeme_primitives.py: registry count 8 -> 9, proper_noun_token
fires + variants (Bob, Marnie, McDonald, O'Brien, Mary-Anne),
numeric/all-caps refusals, numeric-literal still wins overlap on "123"
- test_reader_question_frame.py: 5 new tests for sentence-initial
dispatch + unknown-gender pronoun resolution + novel-name admission
via primitive (Zelda)
- test_en_core_math_v1_pack.py: category counts updated; mutual-exclusion
between gender_female and gender_male preserved; total 208 -> 207
- test_lexicon.py: category list + lookup assertion updated to renamed
proper_noun_gender_female
- test_proper_noun_primitive_universality.py: new test module asserting
domain-agnostic property of the primitive
Validation:
- pack + lexicon + primitive tests: 147 passed
- reader + universality tests: 22 passed
- smoke lane: 67 passed
Closes the engine_state question by leaving those files untracked
(repo discipline: runtime artifacts never enter PRs).
Refs ADR-0164.1 amendment, ADR-0164.2 §EntityRegistry, ADR-0165
§Legitimate uses (the new primitive passes the three-question test).
Phase A — RuntimeConfig flag:
core/config.py: adds `comprehension_reader_questions: bool = False`
Default OFF preserves byte-identical behaviour with today.
Phase B — Hybrid wiring in candidate-graph path:
generate/math_candidate_graph.py:
- _try_reader_for_question() dispatches to the comprehension reader
BEFORE the regex question parser; refusal falls through to regex
- reader_trace: tuple[str, ...] field on CandidateGraphResult captures
JSON-encoded admit/fallthrough events for audit
generate/comprehension/lifecycle_runtime_adapter.py (new):
- build_problem_state_from_candidates(): converts regex-parser output
to ProblemReadingState for the reader's pronoun-resolution step
- invoke_reader_for_question(): tokenises sentence, drives lifecycle
- project_to_candidate_unknown(): QuestionTargetSlot → CandidateUnknown
- trace-event constructors for admit and fallthrough
Phase C — Capability-axis regression:
All existing tests pass with flag OFF and ON; zero new regressions.
Two pre-existing failures on main are unrelated to this PR.
Phase D — GSM8K train_sample measurement:
evals/gsm8k_math/train_sample/v1/runner.py: --use-reader flag triggers
baseline-off + reader-on runs and writes reader_phase1_delta.json
evals/gsm8k_math/train_sample/v1/reader_phase1_delta.json (new):
baseline-off: correct=3 refused=47 wrong=0
reader-on: correct=3 refused=47 wrong=0
delta: all zeros — Mixed result expected (Phase 2 scope)
wrong=0 invariant preserved in both modes.
Phase E — Coexistence tests:
tests/test_reader_coexistence.py (new): 13 tests covering
flag-OFF byte-identity, flag-ON determinism, wrong=0 invariant,
trace shape validation, Brief-8 target admission, and fallthrough
preservation for unknown-unit words.
Admission gate result: Mixed (correct=3, below the ≥10 bar).
All statement-side barriers remain in place; Phase 2 (reader for
statement sentences) is required to drive correct≥10. Documented in
reader_phase1_delta.json and train_sample/v1/runner.py docstring.
Adds the three lifecycle functions for the incremental compositional
reader per ADR-0164.3 §Lifecycle API:
- begin_sentence(problem_state, source_text_offset) -> SentenceReadingState
- apply_word(sentence_state, problem_state, word) -> SentenceReadingState | ReaderRefusal
- end_sentence(sentence_state, problem_state) -> ProblemReadingState | ReaderRefusal
Phase 1 scope is question sentences only. The update rules for the
question_frame live in a single readable table (_QUESTION_FRAME_RULES);
statement-side frames (initial_state_frame, operation_frame,
descriptive_frame) refuse with a Phase-2 diagnostic.
The five Brief-8 GSM8K target question sentences (0007, 0017, 0027,
0036, 0043) produce valid QuestionTargetSlot outputs end-to-end.
_interface_stubs.py provides a thin, functional surface for the
lexeme-primitive scanner (Brief 6) and lexicon loader (Brief 7) so
this PR does not block on them. The stub honours the en_core_math_v1
pack entries and adds a closed Phase-1 supplemental vocabulary marked
for fold-in to the pack once Briefs 6/7 land.
Tests cover determinism (byte-equal canonical bytes), the five GSM8K
target sentences with expected (entity, unit_class, kind) triples,
all token-level and sentence-level refusal modes, and lifecycle
invariants (registry preservation, sentence_index advance).
Stacked on feat/state-two-level-split (PR #323) per ADR-0164.3
§Naming — state types live in state.py.
Shay