3fd317290b
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).
1057 lines
49 KiB
Python
1057 lines
49 KiB
Python
"""ADR-0126 P3 — Candidate-graph assembly + decision rule.
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End-to-end orchestration:
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text
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→ sentence split
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→ per-sentence candidate extraction (P2)
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→ per-candidate round-trip admissibility filter (P1)
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→ bounded branch enumeration (Cartesian product, cap=64)
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→ per-branch graph construction + solve
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→ decision rule
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Decision rule (preserves wrong == 0):
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|admissible answers| == 0 → refuse
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|admissible answers| == 1 → emit
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|admissible answers| >= 2,
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all answers identical → emit common answer
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|admissible answers| >= 2,
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answers differ → refuse (genuine ambiguity)
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Per-sentence ambiguity tiebreaker (P3-local; orthogonal to the
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decision rule above):
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When a single sentence has multiple admissible candidates AND the
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resulting graphs all solve to the same numeric answer, we collapse
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to one candidate via the "most-grounded-slots-wins" heuristic.
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This handles cases like "Sam gives 3 apples to Tom" where both
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subtract and transfer pass round-trip — transfer has a target slot
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(more grounded content), so it wins on the tiebreaker. If the
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graphs differ in answer, we let the decision rule above refuse.
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"""
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from __future__ import annotations
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import json
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import re
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from collections.abc import Mapping
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from dataclasses import dataclass
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from itertools import product
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from typing import Final, Union
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from generate.comprehension.state import Hypothesis
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from generate.math_candidate_parser import (
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CandidateInitial,
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CandidateUnknown,
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classify_sentence,
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extract_capacity_candidates,
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extract_capacity_question_candidates,
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extract_conditional_op_question_candidates,
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extract_earnings_candidates,
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extract_earnings_question_candidates,
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extract_initial_candidates,
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extract_operation_candidates,
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extract_question_candidates,
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_TIME_UNITS_TO_SECONDS,
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_to_seconds,
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)
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from generate.math_problem_graph import (
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MathGraphError,
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MathProblemGraph,
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)
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from generate.math_roundtrip import CandidateOperation, roundtrip_admissible
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from generate.math_solver import SolveError, solve
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MAX_TOTAL_BRANCHES: Final[int] = 64
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"""Hard cap on Cartesian-product branch enumeration; exceeding refuses."""
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def _load_ratified_registry_or_empty() -> tuple:
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"""Return the ratified recognizer registry, or () on any failure.
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ADR-0163 §Phase D — the candidate-graph consults this registry
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before refusing on an empty per-statement choice list. Failures
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(e.g. malformed log) MUST NOT regress wrong=0; in that case the
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registry is treated as empty and the existing refusal path runs
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unchanged. The registry projection itself is in-process cached
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by ``generate.recognizer_registry``.
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"""
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try:
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from generate.recognizer_registry import load_ratified_registry
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return load_ratified_registry()
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except Exception: # pragma: no cover — defensive: empty registry on any I/O error
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return ()
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MAX_CANDIDATES_PER_SENTENCE: Final[int] = 4
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"""Hard cap on per-sentence candidate emission; exceeding refuses."""
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# ---------------------------------------------------------------------------
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# Result types
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# ---------------------------------------------------------------------------
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@dataclass(frozen=True, slots=True)
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class CandidateGraphAnswer:
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"""A successfully solved candidate graph.
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``answer`` is the numeric answer the solver produced for this
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branch. Multiple branches may produce the same answer; the
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decision rule collapses on equality.
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"""
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graph: MathProblemGraph
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answer: int | float
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@dataclass(frozen=True, slots=True)
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class CandidateGraphResult:
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"""Outcome of candidate-graph parsing + filtering + deciding.
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Exactly one of ``answer`` / ``refusal_reason`` is non-None.
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"""
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answer: int | float | None
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selected_graph: MathProblemGraph | None
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refusal_reason: str | None
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# Diagnostics for inner-loop signal in P6 runner.
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branches_enumerated: int
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branches_admissible: int
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# Reader trace events (JSON-encoded strings). Each element is a JSON
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# object carrying {"layer", "phase", "outcome", ...}. Carries the
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# ADR-0174 Phase-2 constraint-elimination events from the recognizer
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# path. Deferred: full integration with chat/telemetry.py JSONL sink.
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reader_trace: tuple[str, ...] = ()
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@property
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def is_admitted(self) -> bool:
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return self.answer is not None
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# ---------------------------------------------------------------------------
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# Sentence splitting + classification (mirrors math_parser._split_sentences)
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# ---------------------------------------------------------------------------
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_SENTENCE_SPLIT_RE: Final[re.Pattern[str]] = re.compile(r"(?<=[.?!])\s+")
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def _split_sentences(text: str) -> list[str]:
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text = text.strip()
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return [p.strip() for p in _SENTENCE_SPLIT_RE.split(text) if p.strip()]
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# ---------------------------------------------------------------------------
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# Per-sentence choice typing
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# ---------------------------------------------------------------------------
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# A statement sentence's choice space: a list of (initial-or-operation)
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# candidates that all passed the round-trip filter. A question sentence's
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# choice space: a list of CandidateUnknown.
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SentenceChoice = Union[CandidateInitial, CandidateOperation]
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def _filtered_statement_choices(sentence: str) -> list[SentenceChoice]:
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"""Return all admissible (initial | operation) candidates for a
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statement sentence, after applying the round-trip filter."""
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out: list[SentenceChoice] = []
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# Initial-possession candidates are checked structurally — we use
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# the operation round-trip filter shape only for CandidateOperation.
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# For CandidateInitial we apply a light structural check inline:
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# entity, value, unit, anchor must all ground in source. (P1's
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# roundtrip_admissible signature is operation-specific.)
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for ic in extract_initial_candidates(sentence):
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if _initial_admissible(ic):
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out.append(ic)
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for oc in extract_operation_candidates(sentence):
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if roundtrip_admissible(oc):
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out.append(oc)
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return out[:MAX_CANDIDATES_PER_SENTENCE]
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def _filtered_question_choices(
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sentence: str, problem_text: str | None = None
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) -> list[CandidateUnknown]:
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"""Return all admissible question candidates after the question-
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specific structural check.
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ADR-0163.D.3 — conditional-prefix recovery. When the existing
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parser returns no candidates AND the question begins with an
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"If X, ..." conditional prefix, strip the prefix and re-try.
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This admits the ``nested_question_target`` shape that the bare
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regex misses (11 of 38 GSM8K train_sample post-Phase-D question
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refusals share this shape). Skip-only safety: if the stripped
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question still produces no admissible candidate, refuse as before.
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ADR-0163.D.4 — ``problem_text`` is the full problem text used by
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the new question-grammar extensions for pronoun-entity resolution
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(Pattern C). When None, pronoun-entity branches refuse.
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"""
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out: list[CandidateUnknown] = []
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for qc in extract_question_candidates(sentence, problem_text):
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if _question_admissible(qc):
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out.append(qc)
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if not out:
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stripped = _strip_conditional_prefix(sentence)
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if stripped is not None and stripped != sentence:
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for qc in extract_question_candidates(stripped, problem_text):
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if _question_admissible(qc):
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out.append(qc)
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return out[:MAX_CANDIDATES_PER_SENTENCE]
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_CONDITIONAL_PREFIX_RE: re.Pattern[str] = re.compile(
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r"^\s*[Ii]f\s+.+?,\s+(?=[A-Za-z])",
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)
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def _strip_conditional_prefix(sentence: str) -> str | None:
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"""ADR-0163.D.3 — remove an ``If X, `` conditional prefix.
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Returns the suffix with its first letter upper-cased when the
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pattern matches; returns ``None`` if no conditional prefix is
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present. The transformation is deterministic and pure.
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"""
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m = _CONDITIONAL_PREFIX_RE.match(sentence)
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if m is None:
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return None
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suffix = sentence[m.end():]
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if not suffix:
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return None
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# Existing question regexes expect a leading "How" (case-insensitive
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# in pattern); upper-case the first character to mirror the
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# canonical surface form so the deterministic match holds.
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return suffix[0].upper() + suffix[1:]
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def _initial_admissible(ic: CandidateInitial) -> bool:
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"""Light structural ground-check for initial-possession candidates.
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Same shape as roundtrip_admissible but for the initial-possession
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slot set (entity, anchor, value, unit).
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RAT-1 — when ``ic.composition_evidence`` is non-None the candidate
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is a registry-gated composition (ADR-0169); the derived value /
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canonical unit / cross-sentence entity will not literally appear in
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source_span. Branch to :func:`_composed_initial_admissible` which
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checks the composition INPUT tokens (count, amount, currency
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symbol) ground instead. The composition_shape is gated upstream by
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the composition_registry consult in
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:func:`generate.recognizer_anchor_inject.inject_from_match`.
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"""
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if ic.composition_evidence is not None:
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return _composed_initial_admissible(ic)
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from generate.math_roundtrip import _tokens, _value_grounds, _token_in, _unit_grounds
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haystack = _tokens(ic.source_span)
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if not _token_in(ic.matched_anchor, haystack):
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return False
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if not _value_grounds(ic.matched_value_token, haystack):
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return False
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if not _unit_grounds(ic.matched_unit_token, ic.source_span, haystack):
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return False
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# Entity token: for multi-word entities ("the boys"), all words
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# must ground. Split + check each.
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for tok in ic.matched_entity_token.split():
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if not _token_in(tok, haystack):
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return False
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return True
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def _composed_initial_admissible(ic: CandidateInitial) -> bool:
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"""RAT-1 — admissibility gate for registry-gated composition candidates.
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Preserves wrong=0 by requiring each composition INPUT token to
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ground in source_span. The derived value (e.g. ``1200`` from
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``3 × 400``, or ``150`` from ``100 + 50``) and canonicalized unit
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are NOT required to be literal because they are deterministic
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arithmetic over grounded inputs.
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composition_evidence schema (all keys required):
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- composition_shape: str — the surface_pattern (registry-gated upstream)
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- input_tokens: str — pipe-separated list of literal tokens
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(e.g. "3|400" for multiplicative,
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"100|50" for additive)
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- entity_source: str — "same_sentence" | "prior_sentence"
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Optional:
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- currency_symbol: str — substring required in source_span (for currency shapes)
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Each input_token must ground in source_span tokens.
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matched_entity_token must be non-empty (matcher's binding is
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trusted; cross-unit/cross-sentence refusals happen upstream).
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"""
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from generate.math_roundtrip import _tokens, _token_in
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ev = ic.composition_evidence
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if not ev:
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return False
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required = {"composition_shape", "input_tokens", "entity_source"}
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if not required.issubset(ev.keys()):
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return False
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haystack = _tokens(ic.source_span)
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input_tokens = ev["input_tokens"].split("|") if ev["input_tokens"] else []
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if not input_tokens:
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return False
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for tok in input_tokens:
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if not _token_in(tok, haystack):
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return False
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currency_symbol = ev.get("currency_symbol")
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if currency_symbol and currency_symbol not in ic.source_span:
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return False
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if not ic.matched_entity_token or not ic.matched_entity_token.strip():
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return False
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return True
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def _question_admissible(qc: CandidateUnknown) -> bool:
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"""Light structural ground-check for question candidates."""
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from generate.math_roundtrip import _tokens, _token_in, _unit_grounds
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haystack = _tokens(qc.source_span)
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if not _unit_grounds(qc.matched_unit_token, qc.source_span, haystack):
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return False
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if qc.matched_entity_token is not None:
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for tok in qc.matched_entity_token.split():
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if not _token_in(tok, haystack):
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return False
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return True
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# ---------------------------------------------------------------------------
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# Per-sentence ambiguity tiebreaker (most-grounded-slots-wins)
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# ---------------------------------------------------------------------------
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def _slot_count(choice: SentenceChoice) -> int:
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"""Count the number of distinct grounded content slots.
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More grounded slots → 'tighter' parse → preferred when answers
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agree. Implements the give-with-target case: transfer (4 slots:
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actor, verb, value, unit, target = 5) wins over subtract (4 slots)
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on the same sentence.
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"""
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if isinstance(choice, CandidateInitial):
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return 4 # entity, anchor, value, unit
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n = 4 # actor, verb, value, unit
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if choice.matched_target_token is not None:
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n += 1
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if choice.matched_reference_actor_token is not None:
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n += 1
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return n
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def _collapse_per_sentence_ties(
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choices: list[SentenceChoice],
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) -> list[SentenceChoice]:
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"""If multiple choices exist for one sentence, prefer the one with
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the most grounded slots (deterministic tiebreaker). Ties at the
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max slot-count return all tied choices; cross-sentence ambiguity
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still gets enumerated."""
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if len(choices) <= 1:
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return choices
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max_slots = max(_slot_count(c) for c in choices)
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return [c for c in choices if _slot_count(c) == max_slots]
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# ---------------------------------------------------------------------------
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# Graph construction from one branch
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# ---------------------------------------------------------------------------
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def _build_graph(
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statement_choices: list[SentenceChoice],
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question_choice: CandidateUnknown,
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) -> MathProblemGraph | None:
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"""Build a MathProblemGraph from one consistent branch of sentence
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choices, or return None if the branch cannot form a valid graph
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(entity universe violations, referential integrity, etc.).
|
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State threading is minimal in P3 scope (no pronoun resolution, no
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unit inheritance — those need richer per-branch state and land in
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a later sub-phase). The dataclass constructors catch every
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referential-integrity violation deterministically.
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"""
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entities: list[str] = []
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seen_entities: set[str] = set()
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def add_entity(e: str) -> None:
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if e not in seen_entities:
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entities.append(e)
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seen_entities.add(e)
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initials_list = []
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operations_list = []
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for choice in statement_choices:
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if isinstance(choice, CandidateInitial):
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add_entity(choice.initial.entity)
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initials_list.append(choice.initial)
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else:
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add_entity(choice.op.actor)
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if choice.op.target is not None:
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add_entity(choice.op.target)
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operations_list.append(choice.op)
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|
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if question_choice.unknown.entity is not None:
|
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if question_choice.unknown.entity not in seen_entities:
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return None # question references unknown entity
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try:
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return MathProblemGraph(
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entities=tuple(entities),
|
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initial_state=tuple(initials_list),
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operations=tuple(operations_list),
|
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unknown=question_choice.unknown,
|
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)
|
||
except MathGraphError:
|
||
return None
|
||
|
||
|
||
# ---------------------------------------------------------------------------
|
||
# Orchestrator
|
||
# ---------------------------------------------------------------------------
|
||
|
||
def parse_and_solve(text: str) -> CandidateGraphResult:
|
||
"""End-to-end: parse text via candidate-graph topology, solve each
|
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admissible branch, apply decision rule.
|
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|
||
Args:
|
||
text: The problem text to parse.
|
||
|
||
Returns :class:`CandidateGraphResult` with either an admitted
|
||
``answer`` + ``selected_graph`` or a ``refusal_reason`` string
|
||
naming why the problem was refused.
|
||
|
||
Preserves wrong == 0 by construction:
|
||
|
||
- A sentence the parser cannot match contributes [] to its choice
|
||
list → Cartesian product is empty → refusal.
|
||
- Every branch's graph must round-trip through the round-trip
|
||
filter at the per-sentence level (already applied during
|
||
filtering).
|
||
- Branches that disagree on the final answer trigger refusal.
|
||
|
||
ADR-0174 Phase 5a: the recognizer/candidate-graph path is the single
|
||
canonical reader. The flag-gated incremental-reader dispatch
|
||
(``_try_comprehension_reader`` / ``_try_reader_for_question``) was
|
||
retired — it admitted 0/50 on train_sample and only added a dead
|
||
fall-through. ``lifecycle.py`` itself survives because the ADR-0172
|
||
contemplation corridor (``comprehension/audit.py`` →
|
||
``teaching/math_*``) still uses its reader surface.
|
||
"""
|
||
if not isinstance(text, str) or not text.strip():
|
||
return CandidateGraphResult(
|
||
answer=None, selected_graph=None,
|
||
refusal_reason="empty or non-string problem",
|
||
branches_enumerated=0, branches_admissible=0,
|
||
)
|
||
|
||
sentences = _split_sentences(text)
|
||
if not sentences:
|
||
return CandidateGraphResult(
|
||
answer=None, selected_graph=None,
|
||
refusal_reason="no sentences found",
|
||
branches_enumerated=0, branches_admissible=0,
|
||
)
|
||
|
||
question_sentences = [s for s in sentences if s.rstrip().endswith("?")]
|
||
statement_sentences = [s for s in sentences if not s.rstrip().endswith("?")]
|
||
|
||
# ADR-0136.S.0 — Strip context-filler sentences before any extraction.
|
||
# A sentence with no digit and no word-number cannot introduce parseable
|
||
# numeric state; skipping it is provably safe for wrong == 0.
|
||
#
|
||
# RAT-1 — but context-filler sentences DO carry proper-noun subjects
|
||
# that downstream composition shapes (case 0019: "John adopts a dog"
|
||
# establishes John before the composition sentence) need for
|
||
# cross-sentence subject binding. Capture the discourse subjects
|
||
# BEFORE filtering so the cross-sentence resolver can reach them.
|
||
from generate.recognizer_match import extract_proper_noun_subject as _rat1_extract_subj
|
||
_discourse_prior_subjects: dict[str, str] = {}
|
||
_running_subject: str | None = None
|
||
for _s in statement_sentences:
|
||
head = _rat1_extract_subj(_s)
|
||
if head is not None:
|
||
_running_subject = head
|
||
# Map this statement to the subject available BEFORE it.
|
||
_discourse_prior_subjects[_s] = _running_subject if _running_subject and _running_subject != head else (
|
||
_running_subject if head is None else _discourse_prior_subjects.get(_s)
|
||
)
|
||
# Re-walk to set the strict "prior" (head from EARLIER sentences only).
|
||
_running_subject = None
|
||
_discourse_prior_subjects = {}
|
||
for _s in statement_sentences:
|
||
_discourse_prior_subjects[_s] = _running_subject
|
||
head = _rat1_extract_subj(_s)
|
||
if head is not None:
|
||
_running_subject = head
|
||
|
||
numeric_statement_sentences = [
|
||
s for s in statement_sentences if classify_sentence(s) == "numeric_state"
|
||
]
|
||
if numeric_statement_sentences or not statement_sentences:
|
||
statement_sentences = numeric_statement_sentences
|
||
|
||
if len(question_sentences) != 1:
|
||
return CandidateGraphResult(
|
||
answer=None, selected_graph=None,
|
||
refusal_reason=(
|
||
f"expected exactly one question sentence; "
|
||
f"got {len(question_sentences)}"
|
||
),
|
||
branches_enumerated=0, branches_admissible=0,
|
||
)
|
||
|
||
# ADR-0136.S.1 — Rate/event short-circuit paths (before Cartesian product).
|
||
# Capacity path: single statement with one CandidateCapacity + matching question.
|
||
if len(statement_sentences) == 1:
|
||
cap_cands = extract_capacity_candidates(statement_sentences[0])
|
||
cap_q_cands = extract_capacity_question_candidates(question_sentences[0])
|
||
if len(cap_cands) == 1 and len(cap_q_cands) == 1:
|
||
cap = cap_cands[0]
|
||
cap_q = cap_q_cands[0]
|
||
actor_ok = (
|
||
cap_q.actor is None
|
||
or cap.actor.lower() == cap_q.actor.lower()
|
||
)
|
||
if actor_ok:
|
||
rate_per_sec = cap.count / _to_seconds(cap.per_count, cap.per_unit)
|
||
answer = rate_per_sec * _to_seconds(cap_q.per_count, cap_q.per_unit)
|
||
if answer > 0:
|
||
return CandidateGraphResult(
|
||
answer=answer,
|
||
selected_graph=None,
|
||
refusal_reason=None,
|
||
branches_enumerated=1,
|
||
branches_admissible=1,
|
||
)
|
||
else:
|
||
return CandidateGraphResult(
|
||
answer=None, selected_graph=None,
|
||
refusal_reason="capacity actor mismatch",
|
||
branches_enumerated=0, branches_admissible=0,
|
||
)
|
||
|
||
# Earnings path: single rate statement + matching question.
|
||
if len(statement_sentences) == 1:
|
||
earn_cands = extract_earnings_candidates(statement_sentences[0])
|
||
earn_q_cands = extract_earnings_question_candidates(question_sentences[0])
|
||
if len(earn_cands) == 1 and len(earn_q_cands) == 1:
|
||
earn = earn_cands[0]
|
||
earn_q = earn_q_cands[0]
|
||
if earn.actor.lower() == earn_q.actor.lower():
|
||
if earn.per_unit in _TIME_UNITS_TO_SECONDS:
|
||
rate_per_sec = earn.amount / _to_seconds(1, earn.per_unit)
|
||
answer = rate_per_sec * _to_seconds(
|
||
earn_q.time_count, earn_q.time_unit,
|
||
)
|
||
if answer > 0:
|
||
return CandidateGraphResult(
|
||
answer=answer,
|
||
selected_graph=None,
|
||
refusal_reason=None,
|
||
branches_enumerated=1,
|
||
branches_admissible=1,
|
||
)
|
||
else:
|
||
return CandidateGraphResult(
|
||
answer=None, selected_graph=None,
|
||
refusal_reason="earnings actor mismatch",
|
||
branches_enumerated=0, branches_admissible=0,
|
||
)
|
||
|
||
# ADR-0136.S.2 — Conditional-op question short-circuit.
|
||
# Shape: "If <Entity> <verb> <N> <unit>, how many <unit2> does <Entity2>
|
||
# <aux> [left|...]?" — given exactly one matching initial-state
|
||
# candidate for (entity, unit) across all statement sentences, the
|
||
# answer is initial_value ± operand by verb polarity. Refuses on any
|
||
# ambiguity (multiple matching ICs, no IC, negative answer); preserves
|
||
# wrong == 0.
|
||
cond_qs = extract_conditional_op_question_candidates(question_sentences[0])
|
||
if len(cond_qs) == 1:
|
||
cq = cond_qs[0]
|
||
all_ic: list[CandidateInitial] = []
|
||
for s in statement_sentences:
|
||
all_ic.extend(extract_initial_candidates(s))
|
||
matching = [
|
||
ic for ic in all_ic
|
||
if ic.initial.entity.lower() == cq.entity.lower()
|
||
and ic.initial.quantity.unit == cq.unit
|
||
]
|
||
if len(matching) == 1:
|
||
val = matching[0].initial.quantity.value
|
||
answer = val - cq.operand if cq.op == "subtract" else val + cq.operand
|
||
if answer >= 0:
|
||
return CandidateGraphResult(
|
||
answer=answer,
|
||
selected_graph=None,
|
||
refusal_reason=None,
|
||
branches_enumerated=1,
|
||
branches_admissible=1,
|
||
)
|
||
|
||
# Per-sentence choice spaces (after round-trip filter + tiebreaker).
|
||
#
|
||
# ADR-0163 §Phase D — ratified-recognizer admission guard.
|
||
# Before refusing on an empty choice list, consult the ratified
|
||
# RecognizerSpec registry. When the registry recognizes the
|
||
# statement, drop it from per_sentence_choices entirely instead of
|
||
# refusing: a recognized statement contributes ZERO math state so
|
||
# the Cartesian product remains identical to "this statement was
|
||
# never there," preserving wrong=0 by construction. Downstream
|
||
# consumption of parsed_anchors (turning recognized rate/temporal
|
||
# surfaces into solver state) is Phase E follow-up work.
|
||
_ratified_registry = _load_ratified_registry_or_empty()
|
||
per_sentence_choices: list[list[SentenceChoice]] = []
|
||
# ME-2 — track a running proper-noun subject across sentences so the
|
||
# recognizer matcher can resolve cross-sentence composition shapes
|
||
# (e.g. case 0019: "John adopts a dog... 3 vet appointments at
|
||
# $400 each"). Update AFTER each statement is processed (the current
|
||
# statement's subject is not yet trusted when matching that same
|
||
# statement; only prior sentences contribute).
|
||
_prior_subject: str | None = None
|
||
# ADR-0174 Phase 2 — statement-scoped trace of constraint eliminations.
|
||
# Merged into the question-stage reader_trace below so the consumer
|
||
# sees both per-sentence eliminations (Phase 2) and reader events
|
||
# (Phase 1, ADR-0164) in one stream.
|
||
_statement_trace: list[str] = []
|
||
for s_idx, s in enumerate(statement_sentences):
|
||
# RAT-1 — prefer the discourse-level prior (which sees context-filler
|
||
# sentences like "John adopts a dog from a shelter"); fall back to
|
||
# the in-loop running subject when discourse map has no entry.
|
||
_effective_prior = _discourse_prior_subjects.get(s, _prior_subject) or _prior_subject
|
||
choices = _filtered_statement_choices(s)
|
||
if not choices:
|
||
if _ratified_registry:
|
||
from generate.recognizer_match import (
|
||
extract_proper_noun_subject as _extract_subj,
|
||
match as _recognizer_match,
|
||
)
|
||
recognizer_match = _recognizer_match(
|
||
s, _ratified_registry, prior_subject=_effective_prior
|
||
)
|
||
if recognizer_match is not None:
|
||
# ADR-0163.D.2 — per-category anchor injection.
|
||
# The matcher may carry populated parsed_anchors that
|
||
# an injector turns into typed solver primitives
|
||
# (CandidateInitial / CandidateOperation). When the
|
||
# injector returns a non-empty tuple, the recognized
|
||
# statement contributes math state to the Cartesian
|
||
# product the same way the existing parser's output
|
||
# does — and every constructed candidate has already
|
||
# passed _initial_admissible upstream of this call.
|
||
# When the injector returns () (skip-only fallback —
|
||
# the round-2 default and the only path for v1
|
||
# categories without an injector), the statement is
|
||
# dropped from per_sentence_choices, preserving the
|
||
# wrong=0 safety net by construction.
|
||
from generate.recognizer_anchor_inject import (
|
||
inject_from_match,
|
||
)
|
||
injected = inject_from_match(recognizer_match, s)
|
||
# ADR-0174 Phase 3 — lookback pronoun resolution.
|
||
# When the matcher tagged any anchor with
|
||
# ``requires_pronoun_resolution``, the injected
|
||
# candidates carry the pronoun as actor/entity and
|
||
# are held until lookback either binds them to a
|
||
# discourse antecedent or drops them. The
|
||
# discourse map (_discourse_prior_subjects /
|
||
# _prior_subject) is consulted in the same
|
||
# precedence as ME-2 cross-sentence binding so
|
||
# behaviour is consistent across recognizer
|
||
# categories. When no antecedent is available,
|
||
# we drop the candidates (refusal-preferring;
|
||
# preserves wrong=0).
|
||
if injected and any(
|
||
isinstance(a, Mapping)
|
||
and a.get("requires_pronoun_resolution")
|
||
for a in recognizer_match.parsed_anchors
|
||
):
|
||
from generate.comprehension.lookback import (
|
||
PronounResolution,
|
||
reevaluate,
|
||
)
|
||
from generate.comprehension.constraint_propagation import (
|
||
hypothesis_from_initial as _hyp_from_initial,
|
||
hypothesis_from_operation as _hyp_from_operation,
|
||
)
|
||
|
||
# Extract the held pronoun (the matcher
|
||
# guarantees a single subject_role across the
|
||
# anchor set for discrete_count_statement v1).
|
||
_held_pronoun: str | None = None
|
||
for a in recognizer_match.parsed_anchors:
|
||
if (
|
||
isinstance(a, Mapping)
|
||
and a.get("requires_pronoun_resolution")
|
||
):
|
||
_sr = a.get("subject_role")
|
||
if isinstance(_sr, str):
|
||
_held_pronoun = _sr
|
||
break
|
||
|
||
_antecedent = _effective_prior
|
||
# ADR-0174 Phase 3a — multi-actor pronoun
|
||
# ambiguity defense. When the problem has
|
||
# more than one distinct proper-noun subject
|
||
# in prior context, picking the most-recent
|
||
# is a guess (e.g. "Alice has 5. Bob has 3.
|
||
# She buys 2." — "She" should bind to Alice
|
||
# by gender but the discourse map returns
|
||
# Bob). No safety net downstream would catch
|
||
# a wrong attribution (single-binding
|
||
# emission → no multi-branch disagreement;
|
||
# verifier re-derives the same wrong graph).
|
||
# Refuse rather than guess. Surfaced by
|
||
# 2026-05-28 Phase 1-3a lookback review;
|
||
# see project-adr-0174-multi-actor-pronoun-hazard
|
||
# memory and CLAUDE.md §Lookback Review
|
||
# Discipline.
|
||
_distinct_priors = {
|
||
v for v in _discourse_prior_subjects.values()
|
||
if v is not None
|
||
}
|
||
if _prior_subject is not None:
|
||
_distinct_priors.add(_prior_subject)
|
||
_multi_actor_ambiguous = len(_distinct_priors) > 1
|
||
if _held_pronoun is None or not _antecedent:
|
||
# No resolution path available — drop the
|
||
# held candidates and log the lookback
|
||
# event so the trace records why.
|
||
_statement_trace.append(json.dumps({
|
||
"layer": "lookback",
|
||
"phase": 3,
|
||
"outcome": "no_antecedent",
|
||
"pronoun": _held_pronoun or "<missing>",
|
||
"sentence_index": s_idx,
|
||
}, sort_keys=True))
|
||
injected = ()
|
||
elif _multi_actor_ambiguous:
|
||
# ADR-0174 Phase 4 — invoke in-loop
|
||
# contemplate before declaring ambiguity.
|
||
# When the gendered-names pack uniquely
|
||
# binds the pronoun to one antecedent,
|
||
# admit via the resolved binding. When
|
||
# contemplate returns None (ambiguous
|
||
# evidence, missing names, epicene
|
||
# pronoun), the Phase 3a defense fires
|
||
# cleanly — refusal-preferring discipline.
|
||
from generate.comprehension.contemplate import (
|
||
contemplate,
|
||
)
|
||
from generate.comprehension.state import (
|
||
ProblemReadingState as _PRS,
|
||
)
|
||
# Phase 4 invocation requires Hypothesis
|
||
# residuals so contemplate can match its
|
||
# contract. Build the held hypotheses
|
||
# here (mirrors the post-resolution path
|
||
# below) so contemplate has well-typed
|
||
# input even though Phase 4a only uses
|
||
# candidate_antecedents.
|
||
_ps_stub = _PRS(
|
||
entity_registry=(),
|
||
accumulated_initial_state=(),
|
||
accumulated_operations=(),
|
||
unknown_target_slot=None,
|
||
pronoun_resolution_history=(),
|
||
sentence_index=s_idx,
|
||
source_text_offset=0,
|
||
)
|
||
_ant_tuple = tuple(sorted(_distinct_priors))
|
||
_residual: tuple[Hypothesis, ...] = tuple(
|
||
Hypothesis(
|
||
candidate=(ant,), # sentinel; Phase 4a uses candidate_antecedents
|
||
category_assignments=(),
|
||
constraint_state=(),
|
||
confidence_rank=i,
|
||
unresolved=("actor_pronoun",),
|
||
)
|
||
for i, ant in enumerate(_ant_tuple)
|
||
)
|
||
_resolution = contemplate(
|
||
_ps_stub, _residual,
|
||
pronoun_hint=_held_pronoun,
|
||
candidate_antecedents=_ant_tuple,
|
||
)
|
||
if _resolution is not None and _resolution.source == "pack":
|
||
# Pack adapter encoded the chosen
|
||
# antecedent in evidence[-1] as
|
||
# ("en_core_names_v1", "chosen=<name>").
|
||
_chosen: str | None = None
|
||
for _src, _fact in _resolution.evidence:
|
||
if _fact.startswith("chosen="):
|
||
_chosen = _fact.split("=", 1)[1]
|
||
break
|
||
if _chosen is not None:
|
||
_statement_trace.append(json.dumps({
|
||
"layer": "contemplate",
|
||
"phase": 4,
|
||
"outcome": "resolved",
|
||
"source": _resolution.source,
|
||
"pronoun": _held_pronoun,
|
||
"resolved_to": _chosen,
|
||
"evidence": [list(e) for e in _resolution.evidence],
|
||
"sub_question": _resolution.sub_question,
|
||
"sentence_index": s_idx,
|
||
}, sort_keys=True))
|
||
# Override _antecedent for the
|
||
# downstream PronounResolution
|
||
# path below; the multi-actor
|
||
# branch becomes admit-via-evidence
|
||
# instead of refuse-on-ambiguity.
|
||
_antecedent = _chosen
|
||
_multi_actor_ambiguous = False # admit path proceeds
|
||
if _multi_actor_ambiguous:
|
||
# Contemplate didn't disambiguate —
|
||
# original Phase 3a defense fires.
|
||
_statement_trace.append(json.dumps({
|
||
"layer": "contemplate",
|
||
"phase": 4,
|
||
"outcome": "ambiguous_unresolvable",
|
||
"pronoun": _held_pronoun,
|
||
"candidate_antecedents": sorted(_distinct_priors),
|
||
"sentence_index": s_idx,
|
||
}, sort_keys=True))
|
||
_statement_trace.append(json.dumps({
|
||
"layer": "lookback",
|
||
"phase": 3,
|
||
"outcome": "no_antecedent_ambiguous",
|
||
"pronoun": _held_pronoun,
|
||
"candidate_antecedents": sorted(_distinct_priors),
|
||
"sentence_index": s_idx,
|
||
}, sort_keys=True))
|
||
injected = ()
|
||
else:
|
||
_refinement = PronounResolution(
|
||
pronoun=_held_pronoun,
|
||
resolved_to=_antecedent,
|
||
evidence_source=(
|
||
"discourse_prior_subjects"
|
||
if s in _discourse_prior_subjects
|
||
else "running_subject"
|
||
),
|
||
)
|
||
_resolved: list[object] = []
|
||
_all_resolved = True
|
||
for _rank, _c in enumerate(injected):
|
||
if isinstance(_c, CandidateInitial):
|
||
_base = _hyp_from_initial(_c, _rank)
|
||
elif isinstance(_c, CandidateOperation):
|
||
_base = _hyp_from_operation(_c, _rank)
|
||
else:
|
||
_all_resolved = False
|
||
break
|
||
_held = Hypothesis(
|
||
candidate=_base.candidate,
|
||
category_assignments=_base.category_assignments,
|
||
constraint_state=_base.constraint_state,
|
||
confidence_rank=_base.confidence_rank,
|
||
unresolved=("actor_pronoun",),
|
||
)
|
||
_result = reevaluate(_held, _refinement)
|
||
_statement_trace.append(json.dumps({
|
||
"layer": "lookback",
|
||
"phase": 3,
|
||
"outcome": "admitted" if _result.refined else "eliminated",
|
||
"pronoun": _held_pronoun,
|
||
"resolved_to": _antecedent,
|
||
"confidence_rank": _rank,
|
||
"evidence_source": _refinement.evidence_source,
|
||
"sentence_index": s_idx,
|
||
}, sort_keys=True))
|
||
if _result.refined is None:
|
||
_all_resolved = False
|
||
break
|
||
_resolved.append(_result.refined.candidate) # type: ignore[arg-type]
|
||
if _all_resolved and _resolved:
|
||
injected = tuple(_resolved)
|
||
else:
|
||
injected = ()
|
||
if injected:
|
||
# ADR-0174 Phase 2 — hypothesis-based admission
|
||
# with structured elimination tracing. Each
|
||
# injected candidate becomes a Hypothesis with
|
||
# confidence_rank == emission order; the
|
||
# constraint propagator runs the same predicates
|
||
# _initial_admissible / roundtrip_admissible run
|
||
# today (decomposed into sub-checks) and returns
|
||
# (survivors, eliminations). Eliminations append
|
||
# as JSON trace events to reader_trace so the
|
||
# operator can see WHICH predicate eliminated the
|
||
# candidate, not just that admission failed.
|
||
# Admission semantics are byte-equivalent to the
|
||
# pre-Phase-2 inline loop: a candidate survives
|
||
# here iff it survived the predicate dispatch
|
||
# there.
|
||
from generate.comprehension.constraint_propagation import (
|
||
eliminate_violating,
|
||
hypothesis_from_initial,
|
||
hypothesis_from_operation,
|
||
)
|
||
|
||
hyps_in: list[Hypothesis] = []
|
||
for rank, c in enumerate(injected):
|
||
if isinstance(c, CandidateInitial):
|
||
hyps_in.append(
|
||
hypothesis_from_initial(c, rank)
|
||
)
|
||
elif isinstance(c, CandidateOperation):
|
||
hyps_in.append(
|
||
hypothesis_from_operation(c, rank)
|
||
)
|
||
survivors, eliminations = eliminate_violating(
|
||
tuple(hyps_in)
|
||
)
|
||
for elim in eliminations:
|
||
_statement_trace.append(json.dumps({
|
||
"layer": "constraint_propagation",
|
||
"phase": 2,
|
||
"outcome": "eliminated",
|
||
"confidence_rank": elim.confidence_rank,
|
||
"predicate": elim.predicate,
|
||
"reason": elim.reason,
|
||
"sentence_index": s_idx,
|
||
}, sort_keys=True))
|
||
admitted: list[SentenceChoice] = [
|
||
h.candidate for h in survivors # type: ignore[misc]
|
||
]
|
||
if len(admitted) == len(injected) and admitted:
|
||
per_sentence_choices.append(
|
||
_collapse_per_sentence_ties(admitted)
|
||
)
|
||
continue
|
||
# Recognized but no injection — REFUSE.
|
||
#
|
||
# The earlier "skip-only" reasoning ("zero math state
|
||
# contributed → wrong=0 preserved by construction") is
|
||
# wrong in the same way the case 0050 hazard was wrong:
|
||
# silently dropping a recognized math statement is
|
||
# equivalent to admitting an incomplete graph at the
|
||
# problem level — the solver answers from whatever
|
||
# remains, which is not the right answer to the input
|
||
# problem. ADR-0167 / Brief 11 §"correct-count greed"
|
||
# established this principle on the reader path; this
|
||
# commit extends it to the recognizer path.
|
||
#
|
||
# If the recognizer matches but the injector cannot
|
||
# produce typed solver state, the right answer is
|
||
# "I don't know" — i.e. refuse. When an injector is
|
||
# added that handles this shape, this branch becomes
|
||
# dead and can be retired.
|
||
return CandidateGraphResult(
|
||
answer=None, selected_graph=None,
|
||
refusal_reason=(
|
||
"recognizer matched but produced no injection "
|
||
f"for statement: {s!r} "
|
||
f"(category={recognizer_match.category.value})"
|
||
),
|
||
branches_enumerated=0, branches_admissible=0,
|
||
# ADR-0174 Phase 3a — preserve statement-stage
|
||
# trace events on early refusal so consumers see
|
||
# WHY admission failed (lookback no_antecedent,
|
||
# constraint_propagation eliminations, etc.).
|
||
reader_trace=tuple(_statement_trace),
|
||
)
|
||
return CandidateGraphResult(
|
||
answer=None, selected_graph=None,
|
||
refusal_reason=f"no admissible candidate for statement: {s!r}",
|
||
branches_enumerated=0, branches_admissible=0,
|
||
reader_trace=tuple(_statement_trace),
|
||
)
|
||
per_sentence_choices.append(_collapse_per_sentence_ties(choices))
|
||
# ME-2 — update prior_subject AFTER this sentence is processed.
|
||
# The current sentence's head proper-noun is now eligible to be
|
||
# the cross-sentence subject for the next sentence's composition
|
||
# match.
|
||
from generate.recognizer_match import (
|
||
extract_proper_noun_subject as _extract_subj_for_update,
|
||
)
|
||
_head = _extract_subj_for_update(s)
|
||
if _head is not None:
|
||
_prior_subject = _head
|
||
|
||
# ADR-0174 Phase 2 — seed reader_trace with statement-stage
|
||
# constraint-propagation events so consumers still see the Phase-2
|
||
# elimination events in one ordered stream. (ADR-0174 Phase 5a: the
|
||
# flag-gated question-reader dispatch was retired; the recognizer
|
||
# question parser is the single path.)
|
||
reader_trace: list[str] = list(_statement_trace)
|
||
question_choices = _filtered_question_choices(question_sentences[0], text)
|
||
|
||
if not question_choices:
|
||
return CandidateGraphResult(
|
||
answer=None, selected_graph=None,
|
||
refusal_reason=(
|
||
f"no admissible candidate for question: "
|
||
f"{question_sentences[0]!r}"
|
||
),
|
||
branches_enumerated=0, branches_admissible=0,
|
||
reader_trace=tuple(reader_trace),
|
||
)
|
||
|
||
# Cartesian product across statement choices × question choices.
|
||
total = 1
|
||
for choices in per_sentence_choices:
|
||
total *= len(choices)
|
||
total *= len(question_choices)
|
||
if total > MAX_TOTAL_BRANCHES:
|
||
return CandidateGraphResult(
|
||
answer=None, selected_graph=None,
|
||
refusal_reason=(
|
||
f"branch count {total} exceeds MAX_TOTAL_BRANCHES="
|
||
f"{MAX_TOTAL_BRANCHES} (refusing rather than truncating)"
|
||
),
|
||
branches_enumerated=total, branches_admissible=0,
|
||
reader_trace=tuple(reader_trace),
|
||
)
|
||
|
||
admissible: list[CandidateGraphAnswer] = []
|
||
branches_enumerated = 0
|
||
for combo in product(*per_sentence_choices, question_choices):
|
||
branches_enumerated += 1
|
||
*stmt_choices, q_choice = combo # type: ignore[misc]
|
||
graph = _build_graph(list(stmt_choices), q_choice) # type: ignore[arg-type]
|
||
if graph is None:
|
||
continue
|
||
try:
|
||
trace = solve(graph)
|
||
except SolveError:
|
||
continue
|
||
admissible.append(
|
||
CandidateGraphAnswer(graph=graph, answer=trace.answer_value)
|
||
)
|
||
|
||
if not admissible:
|
||
return CandidateGraphResult(
|
||
answer=None, selected_graph=None,
|
||
refusal_reason="no branch produced a solvable graph",
|
||
branches_enumerated=branches_enumerated,
|
||
branches_admissible=0,
|
||
reader_trace=tuple(reader_trace),
|
||
)
|
||
|
||
# Decision rule: all answers identical → emit; otherwise → refuse.
|
||
distinct_answers = {a.answer for a in admissible}
|
||
if len(distinct_answers) > 1:
|
||
return CandidateGraphResult(
|
||
answer=None, selected_graph=None,
|
||
refusal_reason=(
|
||
f"branches disagree on answer "
|
||
f"(distinct values: {sorted(distinct_answers)})"
|
||
),
|
||
branches_enumerated=branches_enumerated,
|
||
branches_admissible=len(admissible),
|
||
reader_trace=tuple(reader_trace),
|
||
)
|
||
|
||
# Single agreed answer. Pick the first admissible graph as the
|
||
# canonical representative (deterministic since product() is ordered).
|
||
chosen = admissible[0]
|
||
return CandidateGraphResult(
|
||
answer=chosen.answer,
|
||
selected_graph=chosen.graph,
|
||
refusal_reason=None,
|
||
branches_enumerated=branches_enumerated,
|
||
branches_admissible=len(admissible),
|
||
reader_trace=tuple(reader_trace),
|
||
)
|