feat(gsm8k): Gate A1 multiplicative comparative recognizer injection
Add COMPARATIVE_WITH_UNIT matcher/injector emitting compare_multiplicative for the closed v1 template family (twice/thrice/N-times/half/quarter/third). DCS yields comparative surfaces instead of detection-only fallback. Includes ratified exemplar corpus + accepted recognizer proposal, 19 unit tests, and live frontier proof that comparative_with_unit no-injection = 0. wrong=0 preserved; no report.json rebaseline.
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Shay
parent
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commit
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9 changed files with 473 additions and 1 deletions
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@ -48,7 +48,11 @@ from __future__ import annotations
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from typing import Mapping, Union
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from evals.refusal_taxonomy.shape_categories import ShapeCategory
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from generate.math_candidate_parser import CandidateInitial, CandidateOperation
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from generate.math_candidate_parser import (
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CandidateInitial,
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CandidateOperation,
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_build_compare_multiplicative,
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)
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from generate.math_problem_graph import (
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InitialPossession,
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MathGraphError,
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@ -56,6 +60,7 @@ from generate.math_problem_graph import (
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Quantity,
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Rate,
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)
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from generate.math_roundtrip import roundtrip_admissible
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from generate.recognizer_match import (
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RecognizerMatch,
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extract_proper_noun_subject,
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@ -714,6 +719,66 @@ def inject_rate_with_currency(
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return tuple(out)
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# ---------------------------------------------------------------------------
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# Gate A1 — comparative_with_unit → compare_multiplicative (Workstream A)
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# ---------------------------------------------------------------------------
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def inject_comparative_multiplicative(
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match: RecognizerMatch,
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sentence: str,
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) -> tuple[InjectorEmission, ...]:
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"""Narrow injector for ShapeCategory.COMPARATIVE_WITH_UNIT.
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Emits ``CandidateOperation(kind="compare_multiplicative")`` only when
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the matcher published a fully grounded comparative anchor and
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:func:`roundtrip_admissible` accepts the construction.
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"""
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if not match.parsed_anchors or len(match.parsed_anchors) != 1:
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return ()
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anchor = match.parsed_anchors[0]
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if not isinstance(anchor, dict):
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return ()
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if anchor.get("kind") != "comparative_multiplicative":
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return ()
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actor_token = anchor.get("actor_token")
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reference_token = anchor.get("reference_actor_token")
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unit_token = anchor.get("unit_token")
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factor_token = anchor.get("factor_token")
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matched_verb = anchor.get("matched_verb")
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direction = anchor.get("direction")
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factor = anchor.get("factor")
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if not all(
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isinstance(v, str) and v
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for v in (actor_token, reference_token, unit_token, factor_token, matched_verb, direction)
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):
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return ()
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if not isinstance(factor, (int, float)) or factor <= 0:
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return ()
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# Narrow actor binding (mirror rate v1): ProperName subject only.
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actor = extract_proper_noun_subject(sentence)
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if not actor or actor != actor_token:
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return ()
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cand = _build_compare_multiplicative(
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actor_raw=actor_token,
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factor=float(factor),
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matched_verb=matched_verb,
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matched_value_token=factor_token,
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unit_raw=unit_token,
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reference_raw=reference_token,
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source=sentence,
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direction=direction,
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)
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if cand is None or not roundtrip_admissible(cand):
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return ()
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return (cand,)
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_INJECTORS: Mapping[ShapeCategory, "type"] = {
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ShapeCategory.DISCRETE_COUNT_STATEMENT: inject_discrete_count_statement, # type: ignore[dict-item]
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# WAVE-A — multiplicative_aggregation now has a per-category
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@ -729,6 +794,10 @@ _INJECTORS: Mapping[ShapeCategory, "type"] = {
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# frontier for the currency-per-unit surfaces without touching
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# sealed lanes or any other category.
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ShapeCategory.RATE_WITH_CURRENCY: inject_rate_with_currency, # type: ignore[dict-item]
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# Gate A1 (Workstream A) — comparative_with_unit emits
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# CandidateOperation(kind="compare_multiplicative") for the closed
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# v1 multiplicative entity-comparison template family.
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ShapeCategory.COMPARATIVE_WITH_UNIT: inject_comparative_multiplicative, # type: ignore[dict-item]
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# All other recognizer categories continue to route to the
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# empty-tuple fallback (explicit "recognizer matched but produced
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# no injection" refusal in the candidate-graph). That is the
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@ -764,4 +833,5 @@ __all__ = [
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"inject_from_match",
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"inject_discrete_count_statement",
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"inject_rate_with_currency",
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"inject_comparative_multiplicative",
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]
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@ -810,6 +810,10 @@ def _match_discrete_count_statement(
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return None
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if _has_temporal_quantifier(padded):
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return None
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# Gate A1 — yield comparative multiplicative surfaces to
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# COMPARATIVE_WITH_UNIT instead of detection-only DCS fallback.
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if _is_comparative_multiplicative_v1_surface(statement):
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return None
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anchor = _try_extract_discrete_count_anchor(statement, padded, spec)
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if anchor is not None:
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@ -1831,6 +1835,113 @@ def _match_currency_amount(
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return (tuple(), "amount")
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# ---------------------------------------------------------------------------
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# Gate A1 — comparative_with_unit → compare_multiplicative (Workstream A)
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# ---------------------------------------------------------------------------
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from generate.math_candidate_parser import ( # noqa: E402
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_ANCHOR_TO_FACTOR,
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_COMPARE_MULT_ANCHOR_RE,
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_COMPARE_MULT_NTIMES_RE,
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_is_indefinite_quantifier,
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_resolve_value,
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)
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_DEFERRED_COMPARATIVE_FACTOR_SURFACES: Final[frozenset[str]] = frozenset({
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"double", "triple", "quadruple", "one-third",
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})
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def _is_comparative_multiplicative_v1_surface(statement: str) -> bool:
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"""True when *statement* matches the Gate A1 closed comparative template."""
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s = statement.strip()
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if _COMPARE_MULT_ANCHOR_RE.match(s) is not None:
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return True
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return _COMPARE_MULT_NTIMES_RE.match(s) is not None
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def _try_extract_comparative_multiplicative_anchor(
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statement: str,
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spec: Mapping[str, Any],
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) -> Mapping[str, Any] | None:
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"""Extract one comparative_multiplicative anchor when narrowness holds."""
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s = statement.strip()
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observed_anchors = set(spec.get("observed_factor_anchors") or ())
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allows_numeric = bool(spec.get("allows_numeric_factor"))
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m = _COMPARE_MULT_ANCHOR_RE.match(s)
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if m is not None:
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anchor_word = m.group("anchor").lower()
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if anchor_word in _DEFERRED_COMPARATIVE_FACTOR_SURFACES:
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return None
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if anchor_word not in observed_anchors:
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return None
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factor, direction = _ANCHOR_TO_FACTOR[anchor_word]
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actor_token = m.group("actor")
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unit_token = m.group("unit")
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reference_token = m.group("reference")
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phrase = f"{anchor_word} as many {unit_token}"
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return {
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"kind": "comparative_multiplicative",
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"actor_token": actor_token,
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"reference_actor_token": reference_token,
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"unit_token": unit_token,
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"factor_token": anchor_word,
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"factor": factor,
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"direction": direction,
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"matched_verb": anchor_word,
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"comparator_phrase": phrase,
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}
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m = _COMPARE_MULT_NTIMES_RE.match(s)
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if m is not None:
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if not allows_numeric:
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return None
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value_raw = m.group("value")
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if _is_indefinite_quantifier(value_raw):
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return None
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rv = _resolve_value(value_raw)
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if rv is None or rv.value <= 0:
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return None
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actor_token = m.group("actor")
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unit_token = m.group("unit")
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reference_token = m.group("reference")
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phrase = f"{value_raw} times as many {unit_token}"
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return {
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"kind": "comparative_multiplicative",
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"actor_token": actor_token,
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"reference_actor_token": reference_token,
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"unit_token": unit_token,
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"factor_token": value_raw,
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"factor": float(rv.value),
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"direction": "times",
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"matched_verb": "times",
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"comparator_phrase": phrase,
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}
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return None
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def _match_comparative_with_unit(
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statement: str, spec: Mapping[str, Any]
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) -> tuple[tuple[Mapping[str, Any], ...], Literal["compare"]] | None:
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"""Gate A1 — multiplicative entity comparison with explicit reference.
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Strict match only: returns populated anchors on full v1 template
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match, ``None`` otherwise (no detection-only fallback).
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"""
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if spec.get("anchor_kind") != "comparative_multiplicative":
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return None
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anchor = _try_extract_comparative_multiplicative_anchor(statement, spec)
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if anchor is None:
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return None
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cmin = int(spec.get("anchor_count_min", 1))
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cmax = int(spec.get("anchor_count_max", 1))
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if not (cmin <= 1 <= cmax):
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return None
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return ((anchor,), "compare")
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_MATCHERS: Final[dict[ShapeCategory, Any]] = {
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ShapeCategory.DESCRIPTIVE_SETUP_NO_QUANTITY: _match_descriptive_setup_no_quantity,
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ShapeCategory.TEMPORAL_AGGREGATION: _match_temporal_aggregation,
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@ -1838,6 +1949,7 @@ _MATCHERS: Final[dict[ShapeCategory, Any]] = {
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ShapeCategory.DISCRETE_COUNT_STATEMENT: _match_discrete_count_statement,
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ShapeCategory.MULTIPLICATIVE_AGGREGATION: _match_multiplicative_aggregation,
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ShapeCategory.CURRENCY_AMOUNT: _match_currency_amount,
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ShapeCategory.COMPARATIVE_WITH_UNIT: _match_comparative_with_unit,
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}
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@ -0,0 +1,12 @@
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{"exemplar_id": "cwu-v1-0001", "shape_category": "comparative_with_unit", "statement": "Alice has twice as many apples as Bob.", "expected_graph": {"subject": "Alice", "quantity_anchors": [{"kind": "comparative_multiplicative", "subject_role": "Alice", "factor_token": "twice", "factor_kind": "anchor", "direction": "times", "unit_token": "apples", "reference_actor_token": "Bob"}], "graph_intent": "compare", "outcome": "admissible"}, "provenance": {"source": "gate_a1_seed", "author": "Grok (Gate A1)", "round": 1, "category_rank": 3}}
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{"exemplar_id": "cwu-v1-0002", "shape_category": "comparative_with_unit", "statement": "Jerry has thrice as many apples as Tom.", "expected_graph": {"subject": "Jerry", "quantity_anchors": [{"kind": "comparative_multiplicative", "subject_role": "Jerry", "factor_token": "thrice", "factor_kind": "anchor", "direction": "times", "unit_token": "apples", "reference_actor_token": "Tom"}], "graph_intent": "compare", "outcome": "admissible"}, "provenance": {"source": "gate_a1_seed", "author": "Grok (Gate A1)", "round": 1, "category_rank": 3}}
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{"exemplar_id": "cwu-v1-0003", "shape_category": "comparative_with_unit", "statement": "Brooke has three times as many jumping jacks as Sidney.", "expected_graph": {"subject": "Brooke", "quantity_anchors": [{"kind": "comparative_multiplicative", "subject_role": "Brooke", "factor_token": "three", "factor_kind": "numeric", "direction": "times", "unit_token": "jumping jacks", "reference_actor_token": "Sidney"}], "graph_intent": "compare", "outcome": "admissible"}, "provenance": {"source": "gate_a1_seed", "author": "Grok (Gate A1)", "round": 1, "category_rank": 3, "train_case_id": "gsm8k-train-sample-v1-0024"}}
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{"exemplar_id": "cwu-v1-0004", "shape_category": "comparative_with_unit", "statement": "Dana has 4 times as many pencils as Eli.", "expected_graph": {"subject": "Dana", "quantity_anchors": [{"kind": "comparative_multiplicative", "subject_role": "Dana", "factor_token": "4", "factor_kind": "numeric", "direction": "times", "unit_token": "pencils", "reference_actor_token": "Eli"}], "graph_intent": "compare", "outcome": "admissible"}, "provenance": {"source": "gate_a1_seed", "author": "Grok (Gate A1)", "round": 1, "category_rank": 3}}
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{"exemplar_id": "cwu-v1-0005", "shape_category": "comparative_with_unit", "statement": "Alice has half as many apples as Bob.", "expected_graph": {"subject": "Alice", "quantity_anchors": [{"kind": "comparative_multiplicative", "subject_role": "Alice", "factor_token": "half", "factor_kind": "anchor", "direction": "fraction", "unit_token": "apples", "reference_actor_token": "Bob"}], "graph_intent": "compare", "outcome": "admissible"}, "provenance": {"source": "gate_a1_seed", "author": "Grok (Gate A1)", "round": 1, "category_rank": 3}}
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{"exemplar_id": "cwu-v1-0006", "shape_category": "comparative_with_unit", "statement": "Alice has a quarter as many apples as Bob.", "expected_graph": {"subject": "Alice", "quantity_anchors": [{"kind": "comparative_multiplicative", "subject_role": "Alice", "factor_token": "quarter", "factor_kind": "anchor", "direction": "fraction", "unit_token": "apples", "reference_actor_token": "Bob"}], "graph_intent": "compare", "outcome": "admissible"}, "provenance": {"source": "gate_a1_seed", "author": "Grok (Gate A1)", "round": 1, "category_rank": 3}}
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{"exemplar_id": "cwu-v1-0007", "shape_category": "comparative_with_unit", "statement": "Alice has a third as many apples as Bob.", "expected_graph": {"subject": "Alice", "quantity_anchors": [{"kind": "comparative_multiplicative", "subject_role": "Alice", "factor_token": "third", "factor_kind": "anchor", "direction": "fraction", "unit_token": "apples", "reference_actor_token": "Bob"}], "graph_intent": "compare", "outcome": "admissible"}, "provenance": {"source": "gate_a1_seed", "author": "Grok (Gate A1)", "round": 1, "category_rank": 3}}
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{"exemplar_id": "cwu-v1-0008", "shape_category": "comparative_with_unit", "statement": "Mason collected twice as many shells as Nora.", "expected_graph": {"subject": "Mason", "quantity_anchors": [{"kind": "comparative_multiplicative", "subject_role": "Mason", "factor_token": "twice", "factor_kind": "anchor", "direction": "times", "unit_token": "shells", "reference_actor_token": "Nora"}], "graph_intent": "compare", "outcome": "admissible"}, "provenance": {"source": "gate_a1_seed", "author": "Grok (Gate A1)", "round": 1, "category_rank": 3}}
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{"exemplar_id": "cwu-v1-0009", "shape_category": "comparative_with_unit", "statement": "Ivan has 3 times as many cards as Jerry.", "expected_graph": {"subject": "Ivan", "quantity_anchors": [{"kind": "comparative_multiplicative", "subject_role": "Ivan", "factor_token": "3", "factor_kind": "numeric", "direction": "times", "unit_token": "cards", "reference_actor_token": "Jerry"}], "graph_intent": "compare", "outcome": "admissible"}, "provenance": {"source": "gate_a1_seed", "author": "Grok (Gate A1)", "round": 1, "category_rank": 3}}
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{"exemplar_id": "cwu-v1-0010", "shape_category": "comparative_with_unit", "statement": "Kira gained twice as many points as Leo.", "expected_graph": {"subject": "Kira", "quantity_anchors": [{"kind": "comparative_multiplicative", "subject_role": "Kira", "factor_token": "twice", "factor_kind": "anchor", "direction": "times", "unit_token": "points", "reference_actor_token": "Leo"}], "graph_intent": "compare", "outcome": "admissible"}, "provenance": {"source": "gate_a1_seed", "author": "Grok (Gate A1)", "round": 1, "category_rank": 3}}
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{"exemplar_id": "cwu-v1-0011", "shape_category": "comparative_with_unit", "statement": "Nina studied three times as many pages as Omar.", "expected_graph": {"subject": "Nina", "quantity_anchors": [{"kind": "comparative_multiplicative", "subject_role": "Nina", "factor_token": "three", "factor_kind": "numeric", "direction": "times", "unit_token": "pages", "reference_actor_token": "Omar"}], "graph_intent": "compare", "outcome": "admissible"}, "provenance": {"source": "gate_a1_seed", "author": "Grok (Gate A1)", "round": 1, "category_rank": 3}}
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{"exemplar_id": "cwu-v1-0012", "shape_category": "comparative_with_unit", "statement": "Paula has half as many marbles as Quinn.", "expected_graph": {"subject": "Paula", "quantity_anchors": [{"kind": "comparative_multiplicative", "subject_role": "Paula", "factor_token": "half", "factor_kind": "anchor", "direction": "fraction", "unit_token": "marbles", "reference_actor_token": "Quinn"}], "graph_intent": "compare", "outcome": "admissible"}, "provenance": {"source": "gate_a1_seed", "author": "Grok (Gate A1)", "round": 1, "category_rank": 3}}
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@ -24,3 +24,4 @@
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{"chain_id":"admissibility_multiplicative_aggregation_recognizes_c70663bd722672930b6e2f24596fbab28e94135aeffd05e7037fc6f35e5702f7","connective":"recognizes","domains_object_k":1,"domains_subject_k":2,"intent":"admissibility","object":"c70663bd722672930b6e2f24596fbab28e94135aeffd05e7037fc6f35e5702f7","provenance":"adr-0057:discovery_promoted:2026-05-27","subject":"multiplicative_aggregation"}
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{"chain_id":"admissibility_currency_amount_recognizes_25df92963a15941294c232f37c91987bb35f91e8f3a483f950da43f84c2b7684","connective":"recognizes","domains_object_k":1,"domains_subject_k":2,"intent":"admissibility","object":"25df92963a15941294c232f37c91987bb35f91e8f3a483f950da43f84c2b7684","provenance":"adr-0057:discovery_promoted:2026-05-27","subject":"currency_amount"}
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{"chain_id":"admissibility_temporal_aggregation_recognizes_9684dd780b4d0d387facdce18b474e09413d671b0d4cb944c2754ba2a0bb6208","connective":"recognizes","domains_object_k":1,"domains_subject_k":2,"intent":"admissibility","object":"9684dd780b4d0d387facdce18b474e09413d671b0d4cb944c2754ba2a0bb6208","provenance":"adr-0057:discovery_promoted:2026-05-27","subject":"temporal_aggregation"}
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{"chain_id":"admissibility_comparative_with_unit_recognizes_f3be480f69b85cff21ff6525d769a92fa21f0ef89dfb5e3af076265b90d5883d","connective":"recognizes","domains_object_k":1,"domains_subject_k":2,"intent":"admissibility","object":"f3be480f69b85cff21ff6525d769a92fa21f0ef89dfb5e3af076265b90d5883d","provenance":"adr-0057:discovery_promoted:2026-06-17","subject":"comparative_with_unit"}
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@ -61,6 +61,8 @@ _SUPPORTED_CATEGORIES: frozenset[ShapeCategory] = frozenset({
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ShapeCategory.DISCRETE_COUNT_STATEMENT,
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ShapeCategory.MULTIPLICATIVE_AGGREGATION,
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ShapeCategory.CURRENCY_AMOUNT,
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# Gate A1 (Workstream A) — multiplicative comparative injection.
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ShapeCategory.COMPARATIVE_WITH_UNIT,
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})
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@ -266,6 +268,45 @@ def _validate_multiplicative_aggregation(ctx: str, graph: Mapping[str, Any]) ->
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raise ExemplarIngestError(f"{ctx} outcome must be 'admissible'")
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def _validate_comparative_with_unit(ctx: str, graph: Mapping[str, Any]) -> None:
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anchors = graph["quantity_anchors"]
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if not isinstance(anchors, list) or not anchors:
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raise ExemplarIngestError(f"{ctx} comparative_with_unit needs ≥1 anchor")
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for a in anchors:
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if not isinstance(a, Mapping):
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raise ExemplarIngestError(f"{ctx} anchor must be a mapping")
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_require_keys(ctx, a, frozenset({
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"kind",
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"subject_role",
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"factor_token",
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"factor_kind",
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"direction",
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"unit_token",
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"reference_actor_token",
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}))
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if a["kind"] != "comparative_multiplicative":
|
||||
raise ExemplarIngestError(
|
||||
f"{ctx} anchor kind must be 'comparative_multiplicative'"
|
||||
)
|
||||
if a["factor_kind"] not in {"anchor", "numeric"}:
|
||||
raise ExemplarIngestError(
|
||||
f"{ctx} factor_kind {a['factor_kind']!r} must be 'anchor' or 'numeric'"
|
||||
)
|
||||
if a["direction"] not in {"times", "fraction"}:
|
||||
raise ExemplarIngestError(
|
||||
f"{ctx} direction {a['direction']!r} must be 'times' or 'fraction'"
|
||||
)
|
||||
for fld in (
|
||||
"subject_role", "factor_token", "unit_token", "reference_actor_token",
|
||||
):
|
||||
if not isinstance(a[fld], str) or not a[fld]:
|
||||
raise ExemplarIngestError(f"{ctx} {fld} must be non-empty str")
|
||||
if graph["graph_intent"] != "compare":
|
||||
raise ExemplarIngestError(f"{ctx} graph_intent must be 'compare'")
|
||||
if graph["outcome"] != "admissible":
|
||||
raise ExemplarIngestError(f"{ctx} outcome must be 'admissible'")
|
||||
|
||||
|
||||
def _validate_currency_amount(ctx: str, graph: Mapping[str, Any]) -> None:
|
||||
anchors = graph["quantity_anchors"]
|
||||
if not isinstance(anchors, list) or not anchors:
|
||||
|
|
@ -306,6 +347,7 @@ _CATEGORY_VALIDATORS = {
|
|||
ShapeCategory.DISCRETE_COUNT_STATEMENT: _validate_discrete_count_statement,
|
||||
ShapeCategory.MULTIPLICATIVE_AGGREGATION: _validate_multiplicative_aggregation,
|
||||
ShapeCategory.CURRENCY_AMOUNT: _validate_currency_amount,
|
||||
ShapeCategory.COMPARATIVE_WITH_UNIT: _validate_comparative_with_unit,
|
||||
}
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -78,3 +78,7 @@
|
|||
{"event":"transition","note":"WAVE-A re-seed with extract_values=True","proposal_id":"rat1-seed-4dc30608fb783bc7","review_date":"2026-05-27","to":"accepted"}
|
||||
{"event":"created","proposal":{"claim_domain":"factual","evidence":[],"polarity":"affirms","proposal_id":"rat1-seed-8c3d568c7f90771c","proposed_chain":{"connective":"ratifies","intent":"recognizer_spec_seed","object":"multiplicative_aggregate","recognizer_spec":{"canonical_pattern":{"anchor_kind":"multiplicative_aggregate","extract_values":true,"graph_intent":"aggregate","observed_units":["apple","apples","basket","baskets","book","books","ounce","ounces","strawberries","strawberry"],"outcome":"admissible","shape_category":"multiplicative_aggregation"},"coverage":{},"exemplar_count":0,"exemplar_digest":"8c3d568c7f90771c533e507e614b5385719420198941654a1305628c7b2d81c8","shape_category":"multiplicative_aggregation"},"subject":"multiplicative_aggregation"},"source":{"emitted_at_revision":"flywheel-demo","kind":"exemplar_corpus","source_id":"8c3d568c7f90771c533e507e614b5385719420198941654a1305628c7b2d81c8"}}}
|
||||
{"event":"transition","note":"flywheel-demo seed","proposal_id":"rat1-seed-8c3d568c7f90771c","review_date":"2026-05-27","to":"accepted"}
|
||||
{"event":"created","proposal":{"claim_domain":"factual","evidence":[{"epistemic_status":"coherent","polarity":"affirms","ref":"exemplar:cwu-v1-0001","source":"corpus"},{"epistemic_status":"coherent","polarity":"affirms","ref":"exemplar:cwu-v1-0002","source":"corpus"},{"epistemic_status":"coherent","polarity":"affirms","ref":"exemplar:gsm8k-train-sample-v1-0024","source":"corpus"},{"epistemic_status":"coherent","polarity":"affirms","ref":"exemplar:cwu-v1-0004","source":"corpus"},{"epistemic_status":"coherent","polarity":"affirms","ref":"exemplar:cwu-v1-0005","source":"corpus"},{"epistemic_status":"coherent","polarity":"affirms","ref":"exemplar:cwu-v1-0006","source":"corpus"},{"epistemic_status":"coherent","polarity":"affirms","ref":"exemplar:cwu-v1-0007","source":"corpus"},{"epistemic_status":"coherent","polarity":"affirms","ref":"exemplar:cwu-v1-0008","source":"corpus"},{"epistemic_status":"coherent","polarity":"affirms","ref":"exemplar:cwu-v1-0009","source":"corpus"},{"epistemic_status":"coherent","polarity":"affirms","ref":"exemplar:cwu-v1-0010","source":"corpus"},{"epistemic_status":"coherent","polarity":"affirms","ref":"exemplar:cwu-v1-0011","source":"corpus"},{"epistemic_status":"coherent","polarity":"affirms","ref":"exemplar:cwu-v1-0012","source":"corpus"}],"operator_note":"","polarity":"affirms","proposal_id":"bec14058b9afbb76216414e903106ae9","proposed_chain":{"connective":"recognizes","intent":"admissibility","object":"f3be480f69b85cff21ff6525d769a92fa21f0ef89dfb5e3af076265b90d5883d","recognizer_spec":{"canonical_pattern":{"allows_numeric_factor":true,"anchor_count_max":1,"anchor_count_min":1,"anchor_kind":"comparative_multiplicative","graph_intent":"compare","observed_factor_anchors":["half","quarter","third","thrice","twice"],"outcome":"admissible","shape_category":"comparative_with_unit","unresolved_notes":[]},"coverage":{"anchors_comparative_multiplicative":12,"factor:half":2,"factor:numeric":4,"factor:quarter":1,"factor:third":1,"factor:thrice":1,"factor:twice":3},"exemplar_count":12,"exemplar_digest":"4891b1ea35c17dcf92f089ebf0dbd6b4075ad0fe340ab31b36e24f7e4f30fadf","shape_category":"comparative_with_unit"},"subject":"comparative_with_unit"},"provenance":null,"replay_evidence":null,"review_state":"pending","source":{"emitted_at_revision":"ed2d04c99e90f96d04689d0d94c825fc9a48d5b1","kind":"exemplar_corpus","source_id":"4891b1ea35c17dcf92f089ebf0dbd6b4075ad0fe340ab31b36e24f7e4f30fadf"},"source_candidate_id":"3973a72c777f9418df3f7605c7e98e9c4a4b873ce3df111737227c4ea7afd0ed"}}
|
||||
{"event":"replay","proposal_id":"bec14058b9afbb76216414e903106ae9","replay_evidence":{"baseline":{"intent_accuracy":1.0,"surface_groundedness":1.0,"term_capture_rate":1.0,"versor_closure_rate":1.0},"candidate":{"intent_accuracy":1.0,"surface_groundedness":1.0,"term_capture_rate":1.0,"versor_closure_rate":1.0},"capability_axes":{"G1_verb_classes":{"correct":20,"refused":0,"wrong":0},"G2_comparatives":{"correct":29,"refused":0,"wrong":0},"G3_numerics":{"correct":20,"refused":6,"wrong":0},"G4_multi_clause":{"correct":32,"refused":0,"wrong":0},"G5_aggregate":{"correct":20,"refused":0,"wrong":0},"S1_rate_events":{"correct":20,"refused":0,"wrong":0}},"gsm8k_train_sample":{"correct":6,"refused":44,"wrong":0},"regressed_metrics":[],"replay_equivalent":true,"wrong_count_delta":0}}
|
||||
{"event":"transition","note":"Gate A1 ratification 2026-06-17","proposal_id":"bec14058b9afbb76216414e903106ae9","to":"accepted"}
|
||||
{"chain_id":"admissibility_comparative_with_unit_recognizes_f3be480f69b85cff21ff6525d769a92fa21f0ef89dfb5e3af076265b90d5883d","event":"accepted_corpus_append","proposal_id":"bec14058b9afbb76216414e903106ae9","provenance":{"adr_id":"adr-0057","raw":"adr-0057:discovery_promoted:2026-06-17","review_date":"2026-06-17","source":"discovery_promoted"}}
|
||||
|
|
|
|||
|
|
@ -347,6 +347,48 @@ def _synthesize_multiplicative_aggregation(
|
|||
return canonical_pattern, coverage
|
||||
|
||||
|
||||
def _synthesize_comparative_with_unit(
|
||||
corpus: ExemplarCorpus,
|
||||
) -> tuple[Mapping[str, Any], Mapping[str, int]]:
|
||||
"""Gate A1 — multiplicative entity comparison seeds."""
|
||||
exemplars = corpus.exemplars
|
||||
factor_anchors: list[str] = []
|
||||
anchor_counts: list[int] = []
|
||||
coverage_factor: dict[str, int] = {}
|
||||
has_numeric = False
|
||||
|
||||
for ex in exemplars:
|
||||
anchors = ex.expected_graph["quantity_anchors"]
|
||||
anchor_counts.append(len(anchors))
|
||||
for a in anchors:
|
||||
fk = a.get("factor_kind", "anchor")
|
||||
if fk == "numeric":
|
||||
has_numeric = True
|
||||
coverage_factor["numeric"] = coverage_factor.get("numeric", 0) + 1
|
||||
else:
|
||||
token = a["factor_token"]
|
||||
factor_anchors.append(token)
|
||||
coverage_factor[token] = coverage_factor.get(token, 0) + 1
|
||||
|
||||
canonical_pattern: dict[str, Any] = {
|
||||
"shape_category": ShapeCategory.COMPARATIVE_WITH_UNIT.value,
|
||||
"graph_intent": "compare",
|
||||
"outcome": "admissible",
|
||||
"anchor_kind": "comparative_multiplicative",
|
||||
"observed_factor_anchors": _sorted_unique(factor_anchors),
|
||||
"allows_numeric_factor": has_numeric,
|
||||
"anchor_count_min": min(anchor_counts),
|
||||
"anchor_count_max": max(anchor_counts),
|
||||
"unresolved_notes": _collect_author_notes(exemplars),
|
||||
}
|
||||
coverage: dict[str, int] = {
|
||||
"anchors_comparative_multiplicative": sum(anchor_counts),
|
||||
}
|
||||
for token, n in sorted(coverage_factor.items()):
|
||||
coverage[f"factor:{token}"] = n
|
||||
return canonical_pattern, coverage
|
||||
|
||||
|
||||
def _synthesize_currency_amount(
|
||||
corpus: ExemplarCorpus,
|
||||
) -> tuple[Mapping[str, Any], Mapping[str, int]]:
|
||||
|
|
@ -398,6 +440,7 @@ _SYNTHESIZERS = {
|
|||
ShapeCategory.DISCRETE_COUNT_STATEMENT: _synthesize_discrete_count_statement,
|
||||
ShapeCategory.MULTIPLICATIVE_AGGREGATION: _synthesize_multiplicative_aggregation,
|
||||
ShapeCategory.CURRENCY_AMOUNT: _synthesize_currency_amount,
|
||||
ShapeCategory.COMPARATIVE_WITH_UNIT: _synthesize_comparative_with_unit,
|
||||
}
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -107,6 +107,35 @@ def test_post_inc3_live_runner_has_zero_rate_no_injection():
|
|||
assert cats.get("rate_with_currency", 0) == 0
|
||||
|
||||
|
||||
def test_post_gate_a1_live_runner_has_zero_comparative_no_injection():
|
||||
"""Live train_sample: comparative_with_currency bucket closed at injector."""
|
||||
import re
|
||||
from collections import Counter
|
||||
|
||||
from evals.gsm8k_math.train_sample.v1.runner import build_report
|
||||
from tests.gsm8k_train_sample_baseline import assert_monotonic_serving_counts
|
||||
|
||||
cases_path = _REPO_ROOT / "evals/gsm8k_math/train_sample/v1/cases.jsonl"
|
||||
cases = [
|
||||
json.loads(line)
|
||||
for line in cases_path.read_text(encoding="utf-8").splitlines()
|
||||
if line.strip()
|
||||
]
|
||||
report = build_report(cases)
|
||||
assert_monotonic_serving_counts(report["counts"])
|
||||
|
||||
cats: Counter[str] = Counter()
|
||||
for row in report["per_case"]:
|
||||
reason = row.get("reason", "")
|
||||
if "produced no injection" not in reason:
|
||||
continue
|
||||
m = re.search(r"category=(\w+)", reason)
|
||||
if m:
|
||||
cats[m.group(1)] += 1
|
||||
|
||||
assert cats.get("comparative_with_unit", 0) == 0
|
||||
|
||||
|
||||
def test_classify_and_extract_category_logic():
|
||||
"""Unit the internal classification on the exact reason strings the graph emits."""
|
||||
# We exercise via the public analyze path with a tiny synthetic report
|
||||
|
|
|
|||
159
tests/test_recognizer_comparative_inject.py
Normal file
159
tests/test_recognizer_comparative_inject.py
Normal file
|
|
@ -0,0 +1,159 @@
|
|||
"""Gate A1 — comparative_with_unit recognizer-anchor injection tests.
|
||||
|
||||
Mirrors the Inc2 rate injector ladder: unit confusers, live-registry dispatch,
|
||||
half/quarter/third serving proof, and DCS yield for comparative surfaces.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import types
|
||||
|
||||
import pytest
|
||||
|
||||
from evals.refusal_taxonomy.shape_categories import ShapeCategory
|
||||
from generate.math_candidate_parser import CandidateOperation
|
||||
from generate.math_problem_graph import Comparison
|
||||
from generate.math_roundtrip import roundtrip_admissible
|
||||
from generate.recognizer_anchor_inject import (
|
||||
inject_comparative_multiplicative,
|
||||
inject_from_match,
|
||||
)
|
||||
from generate.recognizer_match import RecognizerMatch, match
|
||||
from generate.recognizer_registry import load_ratified_registry
|
||||
|
||||
|
||||
def _stub_recognizer(category: ShapeCategory) -> types.SimpleNamespace:
|
||||
return types.SimpleNamespace(shape_category=category, canonical_pattern={})
|
||||
|
||||
|
||||
def _make_match(anchor: dict) -> RecognizerMatch:
|
||||
return RecognizerMatch(
|
||||
recognizer=_stub_recognizer(ShapeCategory.COMPARATIVE_WITH_UNIT),
|
||||
category=ShapeCategory.COMPARATIVE_WITH_UNIT,
|
||||
outcome="admissible",
|
||||
graph_intent="compare",
|
||||
parsed_anchors=(anchor,),
|
||||
)
|
||||
|
||||
|
||||
def _anchor(
|
||||
*,
|
||||
actor: str = "Alice",
|
||||
reference: str = "Bob",
|
||||
unit: str = "apples",
|
||||
factor_token: str = "twice",
|
||||
factor: float = 2.0,
|
||||
direction: str = "times",
|
||||
matched_verb: str = "twice",
|
||||
) -> dict:
|
||||
return {
|
||||
"kind": "comparative_multiplicative",
|
||||
"actor_token": actor,
|
||||
"reference_actor_token": reference,
|
||||
"unit_token": unit,
|
||||
"factor_token": factor_token,
|
||||
"factor": factor,
|
||||
"direction": direction,
|
||||
"matched_verb": matched_verb,
|
||||
"comparator_phrase": f"{factor_token} as many {unit}",
|
||||
}
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"sentence,actor,reference,unit,factor_token,factor,direction,matched_verb",
|
||||
[
|
||||
("Alice has twice as many apples as Bob.", "Alice", "Bob", "apples", "twice", 2.0, "times", "twice"),
|
||||
("Jerry has thrice as many apples as Tom.", "Jerry", "Tom", "apples", "thrice", 3.0, "times", "thrice"),
|
||||
("Dana has 4 times as many pencils as Eli.", "Dana", "Eli", "pencils", "4", 4.0, "times", "times"),
|
||||
("Alice has half as many apples as Bob.", "Alice", "Bob", "apples", "half", 0.5, "fraction", "half"),
|
||||
("Alice has a quarter as many apples as Bob.", "Alice", "Bob", "apples", "quarter", 0.25, "fraction", "quarter"),
|
||||
("Alice has a third as many apples as Bob.", "Alice", "Bob", "apples", "third", 1.0 / 3.0, "fraction", "third"),
|
||||
],
|
||||
)
|
||||
def test_positive_surfaces_emit_compare_multiplicative(
|
||||
sentence, actor, reference, unit, factor_token, factor, direction, matched_verb
|
||||
):
|
||||
emitted = inject_comparative_multiplicative(_make_match(_anchor(
|
||||
actor=actor,
|
||||
reference=reference,
|
||||
unit=unit,
|
||||
factor_token=factor_token,
|
||||
factor=factor,
|
||||
direction=direction,
|
||||
matched_verb=matched_verb,
|
||||
)), sentence)
|
||||
assert len(emitted) == 1
|
||||
cand = emitted[0]
|
||||
assert isinstance(cand, CandidateOperation)
|
||||
assert cand.op.kind == "compare_multiplicative"
|
||||
assert isinstance(cand.op.operand, Comparison)
|
||||
assert cand.op.operand.factor == factor
|
||||
assert cand.op.operand.direction == direction
|
||||
assert cand.matched_value_token == factor_token
|
||||
assert cand.matched_verb == matched_verb
|
||||
assert roundtrip_admissible(cand) is True
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"sentence",
|
||||
[
|
||||
"Jerry has 3 times as many apples.",
|
||||
"Jerry has twice as many apples.",
|
||||
"Jerry has 3 times more apples than Bob.",
|
||||
"Alice has 3 more apples than Bob.",
|
||||
"He has twice as many apples as Bob.",
|
||||
"Alice lost twice as many apples as Bob.",
|
||||
"Alice has one-third as many apples as Bob.",
|
||||
"Alice has double as many apples as Bob.",
|
||||
"Jerry has 3 times",
|
||||
],
|
||||
)
|
||||
def test_confuser_surfaces_refuse_injection(sentence: str):
|
||||
registry = load_ratified_registry()
|
||||
m = match(sentence, registry)
|
||||
if m is None or m.category is not ShapeCategory.COMPARATIVE_WITH_UNIT:
|
||||
return
|
||||
assert inject_from_match(m, sentence, sealed=False) == ()
|
||||
|
||||
|
||||
def test_unknown_actor_refuses():
|
||||
emitted = inject_comparative_multiplicative(
|
||||
_make_match(_anchor(actor="fish")),
|
||||
"fish have twice as many apples as Bob.",
|
||||
)
|
||||
assert emitted == ()
|
||||
|
||||
|
||||
def test_dispatch_table_routes_comparative_with_unit():
|
||||
registry = load_ratified_registry()
|
||||
stmt = "Alice has twice as many apples as Bob."
|
||||
m = match(stmt, registry)
|
||||
assert m is not None
|
||||
assert m.category is ShapeCategory.COMPARATIVE_WITH_UNIT
|
||||
emitted = inject_from_match(m, stmt, sealed=False)
|
||||
assert len(emitted) == 1
|
||||
assert roundtrip_admissible(emitted[0]) is True
|
||||
|
||||
|
||||
def test_dcs_yields_comparative_not_initial_times():
|
||||
registry = load_ratified_registry()
|
||||
stmt = "Jerry has 3 times as many apples as Tom."
|
||||
m = match(stmt, registry)
|
||||
assert m is not None
|
||||
assert m.category is ShapeCategory.COMPARATIVE_WITH_UNIT
|
||||
emitted = inject_from_match(m, stmt, sealed=False)
|
||||
assert len(emitted) == 1
|
||||
assert emitted[0].op.kind == "compare_multiplicative"
|
||||
|
||||
|
||||
def test_matched_tokens_ground_in_source_sentence():
|
||||
sentence = "Nina studied three times as many pages as Omar."
|
||||
registry = load_ratified_registry()
|
||||
m = match(sentence, registry)
|
||||
assert m is not None
|
||||
emitted = inject_from_match(m, sentence, sealed=False)
|
||||
assert len(emitted) == 1
|
||||
c = emitted[0]
|
||||
assert c.matched_actor_token in sentence
|
||||
assert c.matched_reference_actor_token in sentence
|
||||
assert c.matched_unit_token in sentence
|
||||
Loading…
Reference in a new issue