97971bd636
Introduces teaching/ module with three-stage correction pipeline: 1. correction.py — extracts CorrectionCandidate from correction intents, binding correction text to the prior turn it references 2. review.py — validates candidates: rejects identity overrides (17 marker patterns) and empty corrections; produces ReviewedTeachingExample with deterministic SHA-256 review hash 3. store.py — bounded FIFO store for accepted examples; emits PackMutationProposal objects instead of mutating the vocab manifold directly; retrievable by subject Design invariants: - Identity override attempts are rejected at the review gate - Pack mutations are proposal-only (applied=False by default) - All traces are deterministic: same input → same candidate_id and review_hash Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
104 lines
2.9 KiB
Python
104 lines
2.9 KiB
Python
"""Review gate — validate corrections before they become teaching examples.
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The reviewer enforces two hard constraints:
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1. Identity override rejected — corrections that attempt to redefine
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CORE's identity axes are blocked.
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2. Bounded — the correction must reference a specific prior turn and
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contain non-empty corrective content.
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Reviewed examples carry a deterministic trace (SHA-256 over their content)
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so that identical corrections on identical prior turns always produce the
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same review hash.
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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 dataclass
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from enum import Enum, unique
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from teaching.correction import CorrectionCandidate
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@unique
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class ReviewOutcome(Enum):
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ACCEPTED = "accepted"
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REJECTED_IDENTITY = "rejected_identity"
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REJECTED_EMPTY = "rejected_empty"
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_IDENTITY_MARKERS: frozenset[str] = frozenset({
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"you are",
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"your name is",
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"your identity",
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"you must be",
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"you should act as",
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"you are now",
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"forget your",
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"ignore your",
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"override your",
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"your personality",
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"your character",
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"pretend to be",
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"act as if you",
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"from now on you",
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})
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def _is_identity_override(text: str) -> bool:
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lower = text.lower().strip()
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return any(marker in lower for marker in _IDENTITY_MARKERS)
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def _review_hash(candidate: CorrectionCandidate, outcome: ReviewOutcome) -> str:
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payload = json.dumps(
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{
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"candidate_id": candidate.candidate_id,
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"outcome": outcome.value,
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"correction_text": candidate.correction_text,
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"prior_surface": candidate.prior_surface,
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"prior_turn": candidate.prior_turn,
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},
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sort_keys=True,
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ensure_ascii=False,
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)
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return hashlib.sha256(payload.encode("utf-8")).hexdigest()
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@dataclass(frozen=True, slots=True)
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class ReviewedTeachingExample:
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candidate: CorrectionCandidate
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outcome: ReviewOutcome
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review_hash: str
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@property
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def accepted(self) -> bool:
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return self.outcome is ReviewOutcome.ACCEPTED
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def as_dict(self) -> dict[str, object]:
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return {
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"candidate": self.candidate.as_dict(),
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"outcome": self.outcome.value,
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"review_hash": self.review_hash,
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}
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def review_correction(candidate: CorrectionCandidate) -> ReviewedTeachingExample:
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"""Review a correction candidate and produce a teaching example.
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Identity overrides are rejected. Empty corrections are rejected.
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Everything else is accepted.
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"""
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if _is_identity_override(candidate.correction_text):
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outcome = ReviewOutcome.REJECTED_IDENTITY
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elif not candidate.correction_text.strip():
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outcome = ReviewOutcome.REJECTED_EMPTY
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else:
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outcome = ReviewOutcome.ACCEPTED
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return ReviewedTeachingExample(
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candidate=candidate,
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outcome=outcome,
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review_hash=_review_hash(candidate, outcome),
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)
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