ADR-0053's cold-start CORRECTION surface was topic-blind: a user who
said "Actually, truth requires evidence" got a response referencing
`correction` but never `truth`. The holdout case correction_truth_040
expected `term=['truth']` and missed — one of the architectural gaps
surfaced by the epistemology v1 curriculum unit.
ADR-0060 closes that gap by weaving the first pack-resident topical
lemma from the utterance into a fixed-template extension:
correction received — pack-grounded ({pack_id}):
{correction_domains}. Noted topic: {lemma} ({lemma_domains}).
No prior turn in this session to correct yet.
Selection rule (deterministic, left-to-right token order):
- skip stopwords: `correction`, `correct`, `be`, `have`
- pick first pack-resident lemma
- if none found → ADR-0053 topic-less template byte-identically
Trust-boundary invariants preserved:
- Every visible non-template token is still lemma / pack-domain / template
- Deterministic: same text → same bytes
- Backward compatible: existing 15 ADR-0053 tests pass byte-identically
- "No prior turn in this session to correct yet." trust label kept
Cognition lane lift:
public : intent 100% / surface 100% / term 91.7% / versor 100% (unchanged)
holdout : intent 100% / surface 94.7% / term 75.0%→79.2% / versor 100%
The +4.2pp matches the single-case fix exactly (correction_truth_040).
Remaining 3 holdout misses (procedure_define_010, unknown_spirit_041,
unknown_word_018) are out of scope for this ADR.
- chat/pack_grounding.py — `_extract_correction_topic_lemma` helper +
optional `text` parameter on `pack_grounded_correction_surface`.
- chat/runtime.py — single-line call-site change to pass `text` through.
- tests/test_correction_topic_lemma.py — 14 new tests pin:
extraction (first lemma / skips correction / skips fillers / None on
empty / strips punctuation / case-insensitive); surface (contains
corrected lemma / contains topic domains / degrades to ADR-0053
byte-identically / preserves trust label / deterministic / correct
pack_id); end-to-end (correction_truth_040 emits 'truth' / no-pack-
lemma still grounds).
Why text-level extraction, not intent.subject:
`intent.subject` after ADR-0049 head-noun extraction returns
", truth requires evidence" for the test prompt — the CORRECTION
intent's subject-extractor preserves the post-marker tail. Parsing
the raw text at the surface layer is cleaner; isolates the fix;
doesn't perturb upstream classification logic.
Lanes (regression): smoke 67 / cognition 121 / teaching 17 /
correction tests 29 (15 ADR-0053 backward-compat + 14 ADR-0060 new) —
all green.
The non-negotiable field invariant (versor_condition < 1e-6) is
unaffected: this ADR changes surface composition only.
275 lines
10 KiB
Python
275 lines
10 KiB
Python
"""chat/pack_grounding.py — pack-grounded surface for cold-start DEFINITION
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and RECALL intents (ADR-0048).
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When the ``UnknownDomainGate`` fires with ``source="empty_vault"`` — i.e.
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the runtime has no session evidence yet — the runtime would otherwise
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emit the universal ``_UNKNOWN_DOMAIN_SURFACE`` disclosure on every turn,
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including for terms that are explicitly compiled into the ratified
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cognition pack.
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This module supplies a narrow, auditable alternative: when the input's
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intent is ``DEFINITION`` or ``RECALL`` AND the intent's subject lemma is
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present in ``en_core_cognition_v1``, return a deterministic surface
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composed from the pack lexicon's ``semantic_domains`` for that lemma,
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explicitly tagged as pack-sourced.
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Design constraints (matching the seven axioms):
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- Geometry-first: the pack lookup is by lemma surface, but the
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``semantic_domains`` were curated against the same versors the
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vocabulary carries; the surface refers only to the lemma and its
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curated descriptors — no synthesis, no LLM fallback.
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- Reconstruction-over-storage: the surface is reconstructed from the
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pack at call time; the lexicon is loaded once and cached because
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ratified packs are immutable.
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- Dual-correction: any lemma not in the pack returns ``None``;
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callers fall through to ``_UNKNOWN_DOMAIN_SURFACE`` unchanged.
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- Compilation-last: no tensors, no kernels — JSONL read and string
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formatting only.
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- Trust boundary: every surface produced here is explicitly tagged
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``pack:en_core_cognition_v1`` so the audit contract distinguishes
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pack-grounded surfaces from vault-grounded surfaces and from the
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universal disclosure.
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"""
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from __future__ import annotations
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import json
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from functools import lru_cache
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from pathlib import Path
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PACK_ID: str = "en_core_cognition_v1"
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_PACK_LEXICON_PATH = (
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Path(__file__).resolve().parent.parent
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/ "language_packs"
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/ "data"
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/ PACK_ID
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/ "lexicon.jsonl"
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)
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@lru_cache(maxsize=1)
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def _pack_index() -> dict[str, tuple[str, ...]]:
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"""Load the cognition pack lexicon once and return ``{lemma: semantic_domains}``.
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Ratified packs are immutable; safe to cache for the process lifetime.
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Returns an empty dict if the pack is unavailable — callers must treat
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a missing pack as "no pack-grounded surface available."
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"""
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if not _PACK_LEXICON_PATH.exists():
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return {}
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out: dict[str, tuple[str, ...]] = {}
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for line in _PACK_LEXICON_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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entry = json.loads(line)
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except json.JSONDecodeError:
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continue
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lemma = entry.get("lemma") or entry.get("surface")
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if not lemma:
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continue
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domains = tuple(entry.get("semantic_domains", ()))
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if domains:
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out[lemma.lower()] = domains
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return out
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def pack_grounded_surface(lemma: str) -> str | None:
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"""Return a deterministic pack-grounded surface for *lemma*, or ``None``.
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The surface format is fixed:
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"{lemma} — pack-grounded ({pack_id}): {d1}; {d2}; {d3}. No session evidence yet."
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Only the lemma and up to three semantic_domains from the pack are
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emitted; both come directly from the ratified pack lexicon, with no
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rewording. The trailing disclosure is the constant trust-boundary
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label that distinguishes pack-grounded surfaces from vault-grounded
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surfaces (which would carry session evidence) and from the universal
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"insufficient grounding" disclosure (which carries neither).
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Returns ``None`` when:
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- the lemma is empty or not a string,
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- the pack lexicon file is unavailable,
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- the lemma is not present in the pack,
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- the pack entry has no ``semantic_domains``.
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"""
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if not lemma or not isinstance(lemma, str):
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return None
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key = lemma.strip().lower()
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if not key:
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return None
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index = _pack_index()
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domains = index.get(key)
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if not domains:
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return None
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head = "; ".join(domains[:3])
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return (
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f"{key} — pack-grounded ({PACK_ID}): {head}. "
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f"No session evidence yet."
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)
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def is_pack_lemma(lemma: str) -> bool:
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"""Return True iff *lemma* has an entry with ``semantic_domains`` in the pack."""
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if not lemma or not isinstance(lemma, str):
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return False
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return lemma.strip().lower() in _pack_index()
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_CORRECTION_TOPIC_STOPWORDS: frozenset[str] = frozenset({
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# The meta-cognition lemma itself — we never echo it as the topic
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# because it's already the subject of the acknowledgement template.
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"correction",
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"correct",
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# Common dialogue markers / fillers that classify as pack lemmas
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# but don't carry topical signal in a correction utterance.
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"be",
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"have",
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})
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def _extract_correction_topic_lemma(text: str) -> str | None:
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"""Return the first pack-resident, topical lemma in *text*, or None.
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Deterministic: tokens are processed in left-to-right utterance
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order; the first token that is pack-resident AND not in the
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correction-stopword set wins. Stopwords filter out the meta-
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cognition lemma itself (``correction``) and dialogue fillers
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(``be``, ``have``) that classify as pack lemmas but carry no
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topical signal.
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Used by ``pack_grounded_correction_surface`` to weave the
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corrected claim's subject into the acknowledgement template.
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"""
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if not text or not isinstance(text, str):
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return None
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index = _pack_index()
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# Tokenize: lowercase, strip surrounding punctuation, skip empties.
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raw = text.lower()
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# Replace common punctuation with whitespace; preserve word boundaries.
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for ch in ",.;:!?\"'()[]{}":
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raw = raw.replace(ch, " ")
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for token in raw.split():
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if not token:
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continue
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if token in _CORRECTION_TOPIC_STOPWORDS:
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continue
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if token in index:
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return token
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return None
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def pack_grounded_correction_surface(text: str | None = None) -> str | None:
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"""ADR-0053 + ADR-0060 — cold-start CORRECTION acknowledgement.
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A CORRECTION intent (``"No, that's wrong"``, ``"Actually, X means Y"``)
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is meta-cognitive: it claims the previous turn was incorrect. On a
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cold-start session there is no prior turn to apply the correction
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to, so the doctrine-aligned response is **not** to define what
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correction is (that would be the DEFINITION path) but to
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acknowledge receipt and state explicitly that no prior turn exists
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in this session.
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Surface format (fixed templates, all atoms pack-sourced):
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- **Without topic** (text=None or no pack-resident lemma found):
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"correction received — pack-grounded ({pack_id}): {d1}; {d2}; {d3}.
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No prior turn in this session to correct yet."
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- **With topic** (text supplied AND pack lemma found):
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"correction received — pack-grounded ({pack_id}): {d1}; {d2}; {d3}.
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Noted topic: {lemma} ({td1}; {td2}).
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No prior turn in this session to correct yet."
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Every visible non-template token is either the lemma ``correction``,
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the corrected-topic lemma, or a verbatim ``semantic_domains`` string
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from the ratified pack. No inference; no rewording.
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The trailing disclosure (``No prior turn in this session to correct
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yet.``) is the constant trust-boundary label distinguishing this
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cold-start acknowledgement from the post-correction teaching
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repair path (``teaching/correction.py``) which engages once a
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prior turn exists.
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Returns ``None`` if the pack is unavailable or has no entry for
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``correction`` — callers fall through to the universal disclosure
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unchanged.
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"""
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index = _pack_index()
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domains = index.get("correction")
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if not domains:
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return None
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head = "; ".join(domains[:3])
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topic_lemma = _extract_correction_topic_lemma(text) if text else None
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if topic_lemma is not None:
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topic_domains = index.get(topic_lemma, ())
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topic_head = "; ".join(topic_domains[:2]) if topic_domains else ""
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if topic_head:
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return (
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f"correction received — pack-grounded ({PACK_ID}): {head}. "
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f"Noted topic: {topic_lemma} ({topic_head}). "
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f"No prior turn in this session to correct yet."
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)
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return (
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f"correction received — pack-grounded ({PACK_ID}): {head}. "
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f"Noted topic: {topic_lemma}. "
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f"No prior turn in this session to correct yet."
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)
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return (
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f"correction received — pack-grounded ({PACK_ID}): {head}. "
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f"No prior turn in this session to correct yet."
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)
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def pack_grounded_comparison_surface(
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lemma_a: str, lemma_b: str
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) -> str | None:
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"""ADR-0050 — deterministic pack-grounded surface for COMPARISON intent.
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Returns a surface that composes each lemma's pack semantic_domains
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side-by-side, with no rewording or inference:
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"{a} (d_a1; d_a2) contrasts with {b} (d_b1; d_b2) — pack-grounded
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({pack_id}). No session evidence yet."
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Up to two semantic_domains per side are emitted to keep the surface
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compact. All visible tokens are either the lemmas themselves or
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verbatim pack strings; the verb "contrasts with" is the fixed
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COMPARISON template constant (mirroring the relation predicate
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`contrasts_with` already humanised by ``humanize_predicate``).
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Returns ``None`` when:
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- either lemma is empty or not a string,
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- either lemma is not present in the pack,
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- the two lemmas are identical (a comparison between a term
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and itself carries no contrastive evidence — defer to the
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single-lemma ``pack_grounded_surface`` path or to the
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universal disclosure).
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"""
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if not lemma_a or not isinstance(lemma_a, str):
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return None
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if not lemma_b or not isinstance(lemma_b, str):
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return None
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key_a = lemma_a.strip().lower()
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key_b = lemma_b.strip().lower()
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if not key_a or not key_b:
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return None
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if key_a == key_b:
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return None
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index = _pack_index()
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domains_a = index.get(key_a)
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domains_b = index.get(key_b)
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if not domains_a or not domains_b:
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return None
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head_a = "; ".join(domains_a[:2])
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head_b = "; ".join(domains_b[:2])
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return (
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f"{key_a} ({head_a}) contrasts with {key_b} ({head_b}) "
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f"— pack-grounded ({PACK_ID}). No session evidence yet."
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)
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