ec5d6f5ac7
* docs(math): ADR-0163 — path to GSM8K mastery via candidate-graph admissibility (proposed) Audit reframes the math roadmap entirely. State of main: every named math capability axis (G1..G5, S1) passes at 100% with wrong=0 on its controlled lane. binding_graph, math_versor_arithmetic, math_symbolic_equivalence, math_parser, math_candidate_parser, math_solver, math_verifier, math_realizer, math_problem_graph — all landed. The worktrees on disk are stale forks. State of GSM8K (50-case train sample): correct=0, refused=50, wrong=0. Every refusal reason is identical: "candidate_graph: no admissible candidate for statement: <STATEMENT>". The reframe: the gap is NOT in operator algebra, NOT in binding graph internals, NOT in symbolic equivalence. The gap is in generate/math_candidate_graph.py — the admissibility surface that turns a natural-language statement into a candidate the downstream pipeline can consume. The capability axes pass at 100% because they test statement shapes the candidate-graph already admits. GSM8K refuses at 100% because its statements span shapes the candidate-graph has never been taught. Six-phase plan to lift GSM8K under the thesis "decodes, not generates": A. Refusal taxonomy (measure before building) B. Exemplar corpora per shape category (≤20 statements each, ≤3 per round) C. Contemplation runner ingests exemplars; emits DerivedRecognizer proposals D. Operator ratifies through ADR-0161 HITL queue (no new surface) E. Re-baseline GSM8K train sample. Round 1 exit: correct ≥ 10, wrong = 0. Round 2: ≥ 25. Round 3: ≥ 35. F. Scale to public/v1 (200 cases, target correct ≥ 100), then holdout (measurement-only — never tune against). Three non-negotiables: - wrong = 0 at every phase. Auto-rejected by replay gate, not by operator vigilance. - No hand-rolled recognizers in generate/. Every recognizer lands via contemplation → proposal → review corridor. - Active corpus mutation only via accept_proposal. Status: proposed. Implementation lands as three PRs starting with Phase A scaffolding. Scope discipline: docs-only. No code, no eval changes, no corpus mutation. * feat(ADR-0161.1): core teaching queue list|show — read-only queue projection * fix(ADR-0161.1): restore gap-queue CLI + rename new commands to hitl-queue + R1..R5 refinements
573 lines
20 KiB
Python
573 lines
20 KiB
Python
"""ADR-0057 Phase C2 — TeachingChainProposal + append-only proposal log.
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A ``TeachingChainProposal`` is the **only** path by which the
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system extends its active teaching corpus. Trust boundary:
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- Proposals are derived from contemplated DiscoveryCandidates
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(ADR-0056 Phase C1 output).
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- Eligibility (Call 2 in ADR-0057) is a mechanical predicate.
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Ineligible candidates raise; eligible ones become a pending
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proposal.
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- The replay-equivalence gate (``teaching/replay.py``) is a
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*precondition*, not a permission. A passing gate moves the
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proposal to ``replay_equivalent=True``; only an explicit
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operator ``accept`` writes to the active corpus.
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- The proposal log is append-only. All four review states
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(pending / accepted / rejected / withdrawn) are terminal in
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the log; "delete" doesn't exist.
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"""
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from __future__ import annotations
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import hashlib
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import json
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from dataclasses import asdict, dataclass
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from pathlib import Path
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from typing import TYPE_CHECKING, Any, Literal
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from teaching.provenance import Provenance
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from teaching.source import ProposalSource
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if TYPE_CHECKING:
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# Deferred to break a circular import: teaching.discovery →
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# chat.pack_grounding → chat.__init__ → chat.runtime →
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# teaching.discovery. These names are used only as type
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# annotations here, so the TYPE_CHECKING guard is safe.
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from teaching.discovery import (
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ClaimDomain,
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DiscoveryCandidate,
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EvidencePointer,
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)
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# Default proposal log location. Tests inject a tmp path; callers
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# in production use this constant.
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DEFAULT_PROPOSAL_LOG_PATH: Path = (
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Path(__file__).resolve().parent / "proposals" / "proposals.jsonl"
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)
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ReviewState = Literal["pending", "accepted", "rejected", "withdrawn"]
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@dataclass(frozen=True, slots=True)
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class ReplayEvidence:
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"""Cognition-lane metrics before/after the proposed append.
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A regressed metric is one whose candidate value is strictly
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less than the baseline value. The cognition lane is
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deterministic; no float tolerance is applied (ADR-0057 Call 1
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note: any regression is real).
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"""
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baseline: dict[str, float]
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candidate: dict[str, float]
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regressed_metrics: tuple[str, ...]
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replay_equivalent: bool
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def as_dict(self) -> dict[str, Any]:
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return {
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"baseline": dict(self.baseline),
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"candidate": dict(self.candidate),
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"regressed_metrics": list(self.regressed_metrics),
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"replay_equivalent": bool(self.replay_equivalent),
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}
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@dataclass(frozen=True, slots=True)
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class TeachingChainProposal:
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"""One proposed extension of the active teaching corpus.
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The ``source`` field (ADR-0094) carries typed provenance: operator
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versus miner versus curriculum. Operator is the default and is
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populated on every existing proposal by the migration utility in
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:mod:`teaching.proposals.migrate_source_field`.
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"""
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proposal_id: str
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source_candidate_id: str
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proposed_chain: dict[str, Any]
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polarity: Literal["affirms", "falsifies"]
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claim_domain: ClaimDomain
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evidence: tuple[EvidencePointer, ...]
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source: ProposalSource
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review_state: ReviewState = "pending"
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operator_note: str = ""
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replay_evidence: ReplayEvidence | None = None
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provenance: Provenance | None = None
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def as_dict(self) -> dict[str, Any]:
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return {
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"proposal_id": self.proposal_id,
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"source_candidate_id": self.source_candidate_id,
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"proposed_chain": dict(self.proposed_chain),
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"polarity": self.polarity,
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"claim_domain": self.claim_domain,
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"evidence": [e.as_dict() for e in self.evidence],
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"source": self.source.as_dict(),
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"review_state": self.review_state,
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"operator_note": self.operator_note,
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"replay_evidence": (
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self.replay_evidence.as_dict()
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if self.replay_evidence is not None
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else None
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),
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"provenance": (asdict(self.provenance) if self.provenance else None),
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}
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class ProposalError(ValueError):
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"""Raised when a candidate fails an eligibility gate or when a
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review action is attempted in a state that does not allow it."""
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# ---------------------------------------------------------------------------
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# Eligibility (ADR-0057 Call 2)
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# ---------------------------------------------------------------------------
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def _is_chain_complete(chain: dict[str, Any]) -> bool:
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return all(
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chain.get(k) and isinstance(chain.get(k), str)
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for k in ("subject", "intent", "connective", "object")
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)
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def check_eligibility(
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candidate: DiscoveryCandidate, *, allow_evaluative: bool = False
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) -> None:
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"""Raise ``ProposalError`` if ``candidate`` cannot become a proposal.
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Five mechanical gates (ADR-0057 Call 2):
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1. polarity ∈ {affirms, falsifies}
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2. evidence contains at least one corpus pointer
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3. claim_domain != evaluative unless ``allow_evaluative``
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4. boundary_clean=True
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5. proposed_chain is complete (all four fields populated)
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"""
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if candidate.polarity not in ("affirms", "falsifies"):
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raise ProposalError(
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f"polarity must be 'affirms' or 'falsifies'; got "
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f"{candidate.polarity!r} — undetermined candidates cannot propose"
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)
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if not any(e.source == "corpus" for e in candidate.evidence):
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raise ProposalError(
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"evidence floor: at least one source='corpus' pointer is required"
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)
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if candidate.claim_domain == "evaluative" and not allow_evaluative:
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raise ProposalError(
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"claim_domain='evaluative' requires explicit --allow-evaluative"
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)
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if not candidate.boundary_clean:
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raise ProposalError("source turn was not boundary_clean")
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if not _is_chain_complete(candidate.proposed_chain):
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raise ProposalError(
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"proposed_chain must have subject/intent/connective/object populated"
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)
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# ---------------------------------------------------------------------------
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# Proposal id derivation
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# ---------------------------------------------------------------------------
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def _proposal_id(source_candidate_id: str, chain: dict[str, Any]) -> str:
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payload = {
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"source_candidate_id": source_candidate_id,
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"proposed_chain": chain,
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}
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blob = json.dumps(payload, sort_keys=True, separators=(",", ":"))
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return hashlib.sha256(blob.encode("utf-8")).hexdigest()[:32]
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def build_proposal(
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candidate: DiscoveryCandidate,
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*,
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allow_evaluative: bool = False,
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source: ProposalSource | None = None,
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) -> TeachingChainProposal:
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"""Build a ``pending`` proposal from an eligible candidate.
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Raises ``ProposalError`` for any failing gate. Idempotent on
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(source_candidate_id, proposed_chain): same inputs produce the
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same ``proposal_id``.
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The ``source`` parameter (ADR-0094) defaults to an operator-authored
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source pinned at the current git HEAD. Miner-sourced and
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curriculum-sourced callers pass an explicit :class:`ProposalSource`.
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"""
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check_eligibility(candidate, allow_evaluative=allow_evaluative)
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assert candidate.polarity in ("affirms", "falsifies")
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resolved_source = source if source is not None else _default_operator_source()
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return TeachingChainProposal(
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proposal_id=_proposal_id(candidate.candidate_id, candidate.proposed_chain),
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source_candidate_id=candidate.candidate_id,
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proposed_chain=dict(candidate.proposed_chain),
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polarity=candidate.polarity,
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claim_domain=candidate.claim_domain,
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evidence=tuple(candidate.evidence),
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source=resolved_source,
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)
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def _default_operator_source() -> ProposalSource:
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"""Return an operator-authored source pinned at the current HEAD.
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Used by :func:`build_proposal` when no explicit source is given.
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Reads ``git rev-parse HEAD``; falls back to ``"unknown"`` when git
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is unavailable so the schema invariant
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``emitted_at_revision`` non-empty still holds.
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"""
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return ProposalSource.operator(emitted_at_revision=_current_revision())
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def _current_revision() -> str:
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"""Return the current git HEAD SHA, or ``"unknown"`` if unavailable.
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Pure helper; no side effects. Cached at module load so a long
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session sees a stable value even if HEAD moves.
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"""
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global _CACHED_REVISION
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if _CACHED_REVISION is not None:
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return _CACHED_REVISION
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import subprocess
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try:
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sha = subprocess.check_output(
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["git", "rev-parse", "HEAD"],
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cwd=Path(__file__).resolve().parent.parent,
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stderr=subprocess.DEVNULL,
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text=True,
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).strip()
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_CACHED_REVISION = sha or "unknown"
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except (subprocess.CalledProcessError, FileNotFoundError, OSError):
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_CACHED_REVISION = "unknown"
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return _CACHED_REVISION
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_CACHED_REVISION: str | None = None
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# ---------------------------------------------------------------------------
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# Append-only proposal log
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# ---------------------------------------------------------------------------
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class ProposalLog:
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"""Append-only JSONL store of proposals + state transitions.
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Each line is one *event*:
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- ``{"event": "created", "proposal": {...}}``
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- ``{"event": "replay", "proposal_id": "...", "replay_evidence": {...}}``
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- ``{"event": "transition", "proposal_id": "...",
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"to": "accepted|rejected|withdrawn", "note": "..."}``
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- ``{"event": "accepted_corpus_append", "proposal_id": "...",
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"chain_id": "...", "provenance": {...}}``
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The active view (``current_state``) is derived by replaying the
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log from the top; the file is never rewritten.
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"""
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def __init__(self, path: Path | None = None) -> None:
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self.path = Path(path) if path else DEFAULT_PROPOSAL_LOG_PATH
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self.path.parent.mkdir(parents=True, exist_ok=True)
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# -- write side ---------------------------------------------------
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def _append(self, event: dict[str, Any]) -> None:
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line = json.dumps(event, sort_keys=True, separators=(",", ":"))
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with self.path.open("a", encoding="utf-8") as fh:
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fh.write(line + "\n")
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def record_created(self, proposal: TeachingChainProposal) -> None:
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self._append({"event": "created", "proposal": proposal.as_dict()})
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def record_replay(self, proposal_id: str, evidence: ReplayEvidence) -> None:
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self._append({
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"event": "replay",
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"proposal_id": proposal_id,
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"replay_evidence": evidence.as_dict(),
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})
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def record_transition(
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self, proposal_id: str, to_state: ReviewState, note: str
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) -> None:
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self._append({
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"event": "transition",
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"proposal_id": proposal_id,
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"to": to_state,
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"note": note,
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})
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def record_corpus_append(
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self, proposal_id: str, chain_id: str, provenance: Provenance
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) -> None:
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self._append({
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"event": "accepted_corpus_append",
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"proposal_id": proposal_id,
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"chain_id": chain_id,
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"provenance": asdict(provenance),
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})
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# -- read side ----------------------------------------------------
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def events(self) -> list[dict[str, Any]]:
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if not self.path.exists():
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return []
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events_list: list[dict[str, Any]] = []
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for line in self.path.read_text(encoding="utf-8").splitlines():
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line = line.strip()
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if not line:
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continue
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try:
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events_list.append(json.loads(line))
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except json.JSONDecodeError:
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continue
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return events_list
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def _events(self) -> list[dict[str, Any]]:
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return self.events()
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def current_state(self) -> dict[str, dict[str, Any]]:
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"""Replay the log → ``{proposal_id: {state, proposal, replay,
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note, accepted_chain_id, source}}``.
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The active view is derived deterministically from the log.
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ADR-0094: every ``created`` event must carry a ``source`` field
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on its proposal payload. Missing ``source`` raises
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:class:`ProposalError`; the live log is migrated via
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:mod:`teaching.migrate_proposals_source_field` exactly once at
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ADR-0094 landing.
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"""
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view: dict[str, dict[str, Any]] = {}
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for ev in self._events():
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kind = ev.get("event")
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if kind == "created":
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p = ev.get("proposal") or {}
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pid = p.get("proposal_id")
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if not pid:
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continue
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if "source" not in p:
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raise ProposalError(
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f"proposal {pid!r} missing required 'source' field; "
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"run teaching/migrate_proposals_source_field.py "
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"(ADR-0094)"
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)
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# Validate that source parses as a v1 ProposalSource;
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# we keep the raw dict in the view for backward
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# compatibility but reject malformed payloads here.
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ProposalSource.from_dict(p["source"])
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view.setdefault(pid, {
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"proposal": p,
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"state": p.get("review_state", "pending"),
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"replay_evidence": p.get("replay_evidence"),
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"operator_note": p.get("operator_note", ""),
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"source": p["source"],
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"accepted_chain_id": None,
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"accepted_provenance": None,
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})
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elif kind == "replay":
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pid = ev.get("proposal_id")
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if pid in view:
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view[pid]["replay_evidence"] = ev.get("replay_evidence")
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elif kind == "transition":
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pid = ev.get("proposal_id")
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if pid in view:
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view[pid]["state"] = ev.get("to")
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view[pid]["operator_note"] = ev.get("note", "")
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elif kind == "accepted_corpus_append":
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pid = ev.get("proposal_id")
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if pid in view:
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view[pid]["accepted_chain_id"] = ev.get("chain_id")
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view[pid]["accepted_provenance"] = ev.get("provenance")
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return view
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def find(self, proposal_id: str) -> dict[str, Any] | None:
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return self.current_state().get(proposal_id)
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# ---------------------------------------------------------------------------
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# Corpus append (operator-accept side-effect)
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# ---------------------------------------------------------------------------
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def append_chain_to_corpus(
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chain: dict[str, Any],
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*,
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corpus_path: Path,
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provenance: Provenance,
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chain_id: str | None = None,
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superseded_by: str | None = None,
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) -> str:
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"""Append one reviewed chain JSON line to the active corpus.
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Returns the ``chain_id`` written. Trust boundary: this is the
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ONLY function in the codebase that writes to the active teaching
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corpus, and it is reachable only from ``accept_proposal`` (after
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the replay-equivalence gate + operator review) or from
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``teaching.supersede.supersede_chain`` (operator-driven retire
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of an existing chain — see ADR-0057).
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``superseded_by`` records the ``chain_id`` of an earlier active
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entry that this new entry retires. The earlier entry stays on
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disk; ``teaching.audit`` and ``chat.teaching_grounding`` both
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honour the supersession at load time.
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"""
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subject = str(chain["subject"]).strip().lower()
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intent = str(chain["intent"]).strip().lower()
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connective = str(chain["connective"]).strip()
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obj = str(chain["object"]).strip().lower()
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if not chain_id:
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chain_id = f"{intent}_{subject}_{connective}_{obj}"
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entry: dict[str, Any] = {
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"chain_id": chain_id,
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"subject": subject,
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"intent": intent,
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"connective": connective,
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"object": obj,
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"domains_subject_k": 2,
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"domains_object_k": 1,
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"provenance": provenance.raw or (
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f"{provenance.adr_id or 'adr-0057'}:{provenance.source}:"
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f"{provenance.review_date or ''}"
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),
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}
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if superseded_by:
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entry["superseded_by"] = str(superseded_by).strip()
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line = json.dumps(entry, sort_keys=True, separators=(",", ":"))
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with corpus_path.open("a", encoding="utf-8") as fh:
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fh.write(line + "\n")
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return chain_id
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# ---------------------------------------------------------------------------
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# Orchestration helpers — propose / replay / accept / reject / withdraw
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# ---------------------------------------------------------------------------
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|
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def propose_from_candidate(
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candidate: DiscoveryCandidate,
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*,
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log: ProposalLog,
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run_replay: Any = None,
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allow_evaluative: bool = False,
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source: ProposalSource | None = None,
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) -> TeachingChainProposal:
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"""End-to-end: build proposal, run replay-equivalence gate,
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auto-reject on regression, otherwise leave pending.
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``run_replay`` is the replay function (``teaching.replay.
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run_replay_equivalence`` by default); accepting it as a kwarg
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keeps tests fast — they can pass a fake that returns a stub
|
|
``ReplayEvidence`` without booting the cognition lane.
|
|
|
|
Idempotent on (candidate_id, chain): re-proposing returns the
|
|
existing proposal record if any.
|
|
"""
|
|
proposal = build_proposal(
|
|
candidate,
|
|
allow_evaluative=allow_evaluative,
|
|
source=source,
|
|
)
|
|
existing = log.find(proposal.proposal_id)
|
|
if existing is not None:
|
|
return proposal
|
|
log.record_created(proposal)
|
|
|
|
if run_replay is None:
|
|
from teaching.replay import run_replay_equivalence as run_replay
|
|
evidence = run_replay(proposal.proposed_chain)
|
|
log.record_replay(proposal.proposal_id, evidence)
|
|
|
|
if not evidence.replay_equivalent:
|
|
note = "auto_rollback_regression: " + ",".join(evidence.regressed_metrics)
|
|
log.record_transition(proposal.proposal_id, "rejected", note)
|
|
|
|
return proposal
|
|
|
|
|
|
def accept_proposal(
|
|
proposal_id: str,
|
|
*,
|
|
log: ProposalLog,
|
|
corpus_path: Path,
|
|
review_date: str,
|
|
operator_note: str = "",
|
|
) -> str:
|
|
"""Operator accept — append proposed chain to the active corpus.
|
|
|
|
Pre-conditions (each raises ``ProposalError`` on failure):
|
|
- proposal exists in the log
|
|
- current state is ``pending``
|
|
- replay evidence is present and replay_equivalent=True
|
|
Returns the ``chain_id`` written to the corpus.
|
|
"""
|
|
record = log.find(proposal_id)
|
|
if record is None:
|
|
raise ProposalError(f"proposal not found: {proposal_id}")
|
|
if record["state"] != "pending":
|
|
raise ProposalError(
|
|
f"proposal {proposal_id} is {record['state']!r}, not pending"
|
|
)
|
|
replay = record.get("replay_evidence")
|
|
if not replay or not replay.get("replay_equivalent"):
|
|
raise ProposalError(
|
|
f"proposal {proposal_id} is not replay-equivalent; cannot accept"
|
|
)
|
|
chain = record["proposal"]["proposed_chain"]
|
|
provenance = Provenance(
|
|
adr_id="adr-0057",
|
|
source="discovery_promoted",
|
|
review_date=review_date,
|
|
raw=f"adr-0057:discovery_promoted:{review_date}",
|
|
)
|
|
chain_id = append_chain_to_corpus(
|
|
chain, corpus_path=corpus_path, provenance=provenance
|
|
)
|
|
log.record_transition(proposal_id, "accepted", operator_note)
|
|
log.record_corpus_append(proposal_id, chain_id, provenance)
|
|
return chain_id
|
|
|
|
|
|
def reject_proposal(
|
|
proposal_id: str, *, log: ProposalLog, operator_note: str = ""
|
|
) -> None:
|
|
record = log.find(proposal_id)
|
|
if record is None:
|
|
raise ProposalError(f"proposal not found: {proposal_id}")
|
|
if record["state"] != "pending":
|
|
raise ProposalError(
|
|
f"proposal {proposal_id} is {record['state']!r}, not pending"
|
|
)
|
|
log.record_transition(proposal_id, "rejected", operator_note)
|
|
|
|
|
|
def withdraw_proposal(
|
|
proposal_id: str, *, log: ProposalLog, operator_note: str = ""
|
|
) -> None:
|
|
record = log.find(proposal_id)
|
|
if record is None:
|
|
raise ProposalError(f"proposal not found: {proposal_id}")
|
|
if record["state"] != "pending":
|
|
raise ProposalError(
|
|
f"proposal {proposal_id} is {record['state']!r}, not pending"
|
|
)
|
|
log.record_transition(proposal_id, "withdrawn", operator_note)
|
|
|
|
|
|
__all__ = [
|
|
"DEFAULT_PROPOSAL_LOG_PATH",
|
|
"ProposalError",
|
|
"ProposalLog",
|
|
"ReplayEvidence",
|
|
"ReviewState",
|
|
"TeachingChainProposal",
|
|
"accept_proposal",
|
|
"append_chain_to_corpus",
|
|
"build_proposal",
|
|
"check_eligibility",
|
|
"propose_from_candidate",
|
|
"reject_proposal",
|
|
"withdraw_proposal",
|
|
]
|