The ADR-0191 completeness guard (added after WAVE-A) requires aggregating
initials to expose every consumed source token via
`consumed_value_tokens`, so it can confirm no source quantity was
silently dropped. The WAVE-A "each weighing" injector predates the guard
and left that field empty, so for every "<Subject> <verb> M <outer>, each
... N <unit>" reading the guard computed required={M, N} vs
consumed={M*N} and refused as "incomplete reading: source quantities
[M, N] not consumed". This silently regressed the entire WAVE-A
capability — the canary `test_lilibeth_canary_solves_end_to_end` has been
red on main (it failed byte-identically on f79b647; the smoke gate does
not collect this dedicated test file, so it merged green originally).
Fix: populate `consumed_value_tokens=(count_a_token, count_b_token)` on
the composed initial — exactly the contract the day-enumeration and
embedded-quantifier aggregators already satisfy.
wrong==0 preserved: the two tokens genuinely ARE the multiplicands of the
emitted value; the guard remains refusal-only. Serving frozen: this
shape does not occur in train_sample, so the fix is serving-neutral.
Evidence:
- tests/test_wave_a_multiplicative_aggregation_injector.py: 11 passed
(was 1 failed) incl. test_wrong_zero_preserved (full train_sample eval,
wrong==0).
- core test --suite packs: 141 passed (was 1 failed, 140 passed).
- core test --suite smoke: 67 passed.
- scripts/verify_lane_shas.py: lanes 8/8 match pinned SHAs — the
train_sample_v1 serving SHA is byte-identical, proving zero serving
count change.
PR checklist:
- Capability: restores WAVE-A multiplicative-aggregate reading regressed
by ADR-0191.
- Invariant: wrong==0 (completeness guard stays refusal-only; tokens are
true multiplicands).
- Lane: core test --suite packs / smoke + lane-SHA gate (8/8).
- No hidden normalization, stochastic fallback, approximate recall, or
unreviewed mutation.
- Trust boundary: none widened — internal candidate provenance only.
The discrete_count matcher gated the counted noun on a CLOSED ratified set
(observed_counted_nouns): 'Betty has 24 marbles' matched, 'Randy has 60 mango
trees' / 'Sam has 12 red apples' did not — purely because the noun was unseen.
Open the single-anchor possession/acquisition path to an open noun phrase
(adjective* + 1-3 word head, bounded by a stop-word lookahead so it never
swallows a trailing PP), keeping every other narrowness layer (proper-noun
subject, verb whitelist, single numeric token, no clause-split). Closed
observed nouns still match (capitalized compounds preserved); compound
enumeration stays closed.
Safe because ADR-0191 moved the wrong=0 guarantee downstream: an open-vocab
mis-parse hits the completeness guard + round-trip + branch-disagreement.
Proof: full real corpus 61->494 discrete_count anchors (8x), wrong=0 HOLDS,
zero confabulations.
Substrate PR — 0 metric delta by design (train_sample byte-identical 4/46/0;
the problems still need composition downstream). Value: the foundation every
discrete_count flip consumes, and empirical proof open-vocab is firewall-safe.
Co-authored-by: Claude Opus 4.8 <noreply@anthropic.com>
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)
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.
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).
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 PR plumbing recognizer parsed_anchors into the candidate-graph as
typed CandidateInitial primitives. Scope limited to discrete_count_statement;
other five round-2 categories route to the round-2 skip-only fallback until
follow-up D.2.x PRs.
Five-layer wrong=0 safety net:
1. Matcher narrowness — _try_extract_discrete_count_anchor refuses on any
ambiguity (multi-subject, pronoun subject, non-possession verb,
multi-count, clause-split, unobserved counted_noun, unobserved
count_kind).
2. Extraction correctness — refusal-preferring; populated parsed_anchors
only when ALL narrowness rules hold.
3. Injection correctness — _initial_admissible gates every constructed
CandidateInitial; failure to ground returns () (under-admit).
4. Replay gate — propose-time admissibility_replay_gate auto-rejects any
matcher change that would lift GSM8K wrong count.
5. Multi-branch decision rule — injected candidate disagreeing with
another branch triggers refuse path.
Re-baseline (GSM8K train_sample v1):
- Old (#309 alone): correct=3 refused=47 wrong=0
- New (#309 + D.2 v1): correct=3 refused=47 wrong=0
- Empirical lift in v1 = 0 cases; framework operational. No GSM8K
train_sample case has a discrete_count statement that simultaneously
meets all narrowness rules AND is missed by the existing parser.
Bottleneck moves to other recognizer categories (D.2.2+).
Validation:
- tests/test_adr_0163_d2_discrete_count_injection.py: 34 passed
- tests/test_recognizer_match.py + test_candidate_graph_recognizer_wiring
+ test_admissibility_replay_gate: 27 passed
- adr_0131_* (G1..G5 + S1 wrong=0 invariant): 222 passed / 2 pre-existing
report-comparison failures / 3 skipped — byte-identical to pre-D.2
- Solver code: unchanged
Operator caveat: round-1's ratified discrete_count_statement spec is
unchanged. Matcher behavior on the spec's canonical_pattern has been
extended from detection-only to populated parsed_anchors. Re-ratification
is not required; if policy requires it on matcher-behavior changes, the
registry digest provides byte-stable provenance.
Unblocks the four Phase B round-2 exemplar corpora (PR #306) so they
can flow through `core teaching propose-from-exemplars`. The corpora
were committed in #306 but Phase C's ingest validator + synthesizer
were hard-coded to round-1 categories; this PR closes that gap.
Extends three modules with the three new categories
(discrete_count_statement, multiplicative_aggregation, currency_amount):
- teaching/exemplar_ingest.py — per-category validator dispatch +
_SUPPORTED_CATEGORIES. The file-stem rule loosens from
exact ``<category>_v1`` to ``<category>_v<N>`` so the
temporal_aggregation v2 widening from #306 ingests.
- teaching/recognizer_synthesis.py — per-category synthesizers
following the same observed_*-set + coverage-histogram pattern as
round 1. Determinism, narrowness rule (narrower-not-broader),
rules-only — same discipline.
- generate/recognizer_match.py — per-category matchers shipped as
DETECTION-ONLY (return empty parsed_anchors). Consistent with
Phase D's current skip-only wiring (PR #302). Real value
extraction lands when Phase D.2 plumbs parsed_anchors into the
solver; until then, detection-only is the right shape and
preserves wrong=0 by construction.
graph_intent Literal expanded to include "count" and "amount".
Test updates:
- tests/test_exemplar_ingest.py: extend _ROUND_1 with _ROUND_2;
test_list_corpora_loads_every_round_1_file now asserts every
committed corpus (round 1 + round 2) loads.
- tests/test_recognizer_registry.py: rename + repair
test_live_proposal_log_has_phase_c_pending_proposals →
test_live_proposal_log_has_phase_c_proposals. The original
asserted state=="pending"; PR #304 ratified the three, so the
test now asserts state=="accepted" and registry length matches.
Pre-existing failure on main, fixed here.
Validation:
- 132 passed across exemplar_ingest, recognizer_synthesis,
recognizer_match, recognizer_registry, candidate_graph_wiring,
admissibility_exemplars, refusal_taxonomy_lane,
admissibility_replay_gate
- 222 capability-axis tests passed / 2 pre-existing main failures /
3 skipped — G1..G5 + S1 wrong=0 invariant intact
- 67 smoke passed
- End-to-end CLI sanity check: `core teaching propose-from-exemplars
teaching/admissibility_exemplars/discrete_count_statement_v1.jsonl
--log /tmp/test.jsonl` produced proposal_id 8c7645b4..., state
pending, replay_equivalent=True, wrong_count_delta=0
Empirical projection: of 47 still-refused GSM8K train_sample
statements, ~22 match the discrete_count_statement recognizer, ~2
match multiplicative_aggregation, plus 3 rate_with_currency + 3
temporal_aggregation + 18 descriptive_setup_no_quantity recognized
under the existing round-1 wiring. After operator ratifies round-2
proposals, the candidate-graph skip-only wiring will drop those
sentences from the math state and a meaningful lift is projected.
wrong=0 preserved at every level by Phase D's skip-only
construction.
Scope: enables the round-2 pipeline; does NOT ratify anything;
does NOT modify generate/math_candidate_graph.py. Operator runs
propose-from-exemplars + review --accept after merge.
* chore(ADR-0163.C): land three Phase C pending proposals in live log
Phase C (#301) shipped the CLI but its PR dry-run wrote to a tmp log
path. This commit moves the three Phase C proposals into the live
teaching/proposals/proposals.jsonl so the Phase B→C audit trail is
visible in the proposal log and the proposals are ready for the
operator to ratify after Phase D ships.
Proposals (all state=pending, kind="exemplar_corpus"):
- 59223f13722f906a1cf9b65d9b01c990 — descriptive_setup_no_quantity
- 46ce297f797ff16da12db5de422ca3c9 — rate_with_currency
- a3b892546977c5f0f64c578d6052adbd — temporal_aggregation
Produced by `core teaching propose-from-exemplars --all` against the
live Phase B corpora. No ratification (ADR-0161 §5 — only the repo
owner ratifies). The Phase D admissibility-replay gate confirmed
replay_equivalent=true, wrong_count_delta=0 for all three.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
* feat(ADR-0163.D): wire ratified RecognizerSpecs into math_candidate_graph admissibility surface
Phase D is the first PR to extend the math admission surface. The
audit (#294) said the gap was admission, not operators, algebra,
substrate, or packs. Phase A measured the refusal taxonomy. Phase B
authored seeds. Phase C synthesized recognizers. Phase D wires
those recognizers into generate/math_candidate_graph.py.
Modules
- generate/recognizer_registry.py — pure projection over the proposal
log. Only proposals with source.kind="exemplar_corpus" AND
review_state="accepted" enter the tuple. Sorted by
(review_date, proposal_id). In-process cache keyed on log
(mtime, sha256) — no filesystem cache (ADR-0161 §1). Malformed
accepted specs raise RegistryLoadError citing the offending
proposal_id; silent drops are forbidden.
- generate/recognizer_match.py — per-category rules-only matchers
(no LLM, no embedding, no learned classifier). Honors the Phase C
synthesizer's narrowness rule: out-of-corpus currency symbols,
window units, and per-unit values do NOT match. Three matchers:
_match_descriptive_setup_no_quantity (zero-quantity surface),
_match_temporal_aggregation (event_count_per_window with
observed_window_units/quantifiers honored), _match_rate_with_currency
(currency_per_unit_rate with observed currency/per-unit/amount-kind
honored).
- generate/math_candidate_graph.py — narrowest-edit guard at the
per-statement choice loop. Before the existing
"no admissible candidate for statement" refusal, consult the
ratified registry. Recognized statements are dropped from
per_sentence_choices (zero math state) so the Cartesian product is
identical to "this statement was never there." Empty registry is
a no-op — backward compatibility preserved byte-identically.
Downstream consumption of parsed_anchors (turning recognized
rate/temporal surfaces into solver state that produces concrete
answers) is Phase E follow-up.
Tests (32 new)
- tests/_phase_d_fixture.py — synthetic in-memory ratified registry
built from the three Phase C pending proposals' content. Per
ADR-0161 §5 the agent does NOT ratify the live log; the synthetic
registry round-trips the real RecognizerSpec bytes the operator
will ratify after Phase D ships.
- tests/test_recognizer_registry.py (9) — empty/pending/wrong-kind
filtering, sort order, malformed-spec rejection, cache hit +
invalidation, live-log Phase C audit check.
- tests/test_recognizer_match.py (14) — per-category positive cases,
narrowness (out-of-corpus surface forms rejected), no-LLM import
check.
- tests/test_candidate_graph_recognizer_wiring.py (7) — empty registry
preserves existing refusal; synthetic registry: recognized
statements no longer trigger per-statement refusal;
wrong_count_delta == 0 on GSM8K train_sample; capability axes G1..
G5+S1 wrong=0 unchanged; per-category admission counts on the
refused-set; unrecognized statements still refuse with the
existing reason.
- tests/test_phase_d_replay_evidence.py (2) — full admissibility
replay gate under synthetic registry: replay_equivalent=true,
wrong_count_delta=0, every capability axis wrong=0; each
ratified recognizer admits >= 1 train_sample statement (wiring
is consequential).
Per-category fixture-based admission counts (synthetic registry vs
GSM8K train_sample refused-set sentences):
- descriptive_setup_no_quantity: 40
- rate_with_currency: 2
- temporal_aggregation: 7
Narrowness-invariant negative case results (matcher correctly
returns None on out-of-corpus / load-bearing-math surfaces):
- rate_with_currency: "She paid $5 for the book." (no per-unit)
- temporal_aggregation: "On Saturday she went to the store." (single day token)
- descriptive_setup_no_quantity: "There are some kids in camp." (indefinite quantifier)
Candidates for Phase B round 2 (3 of 20 temporal seeds match the
spec's structural commitment but not my surface regex — author_notes
explicitly flagged these as schema-gap edge cases):
- ta-v1-0004 "Mark does a gig every other day for 2 weeks."
- ta-v1-0012 "Robin walks 4 dogs every other day around the park."
- ta-v1-0019 "The pump fills the tank with 80 gallons over 6 hours."
Three landed wirings DO NOT shift the GSM8K train_sample baseline
counts under fixture (correct=3, wrong=0, refused=47 unchanged) —
Phase D's narrow wiring is wrong=0 safe by construction; lift to
"correct" requires Phase E's downstream parser-side consumption of
parsed_anchors. Capability axes G1..G5+S1 wrong=0 unchanged.
Cross-refs: ADR-0163 (Phase D), ADR-0057 (proposal review),
ADR-0151 (auto-proposal), ADR-0161 §5 (ratification boundary),
Phase A PR #297, Phase B PR #298, Phase C PR #301.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
---------
Co-authored-by: Claude Opus 4.7 <noreply@anthropic.com>
Shay