from __future__ import annotations

from dataclasses import dataclass, replace
import hashlib
import json
import re
from collections.abc import Sequence
from typing import Any, List

import numpy as np

from algebra.versor import versor_condition
from chat.pack_grounding import (
    pack_grounded_surface,
    pack_grounded_comparison_surface,
    pack_grounded_correction_surface,
    pack_grounded_procedure_surface,
    pack_grounded_relation_confirmation_surface,
    pack_grounded_unknown_surface,
    gloss_aware_cause_surface,
    PACK_ID as _COGNITION_PACK_ID,
)
from chat.teaching_grounding import (
    teaching_grounded_surface,
    teaching_grounded_surface_composed,
    teaching_grounded_surface_transitive,
    TEACHING_CORPUS_ID as _TEACHING_CORPUS_ID,
)
from chat.refusal import (
    build_hedge_prefix,
    build_refusal_surface,
    inject_hedge,
    should_inject_hedge,
)
from core.epistemic_state import (
    clearance_from_verdicts,
    epistemic_state_for_grounding_source,
    normative_detail_from_verdicts,
)
from core.response_governance import govern_response, shape_surface
from chat.telemetry import (
    TurnEventSink,
    format_correction_event_jsonl,
    format_reboot_event_jsonl,
    format_turn_event_jsonl,
)
from chat.verdicts import TurnVerdicts
from chat.dispatch_trace import DispatchAttempt, DispatchTrace
from teaching.discovery import (
    DiscoveryCandidate,
    extract_discovery_candidates,
    format_candidate_jsonl,
)
from teaching.discovery_sink import DiscoveryCandidateSink
from engine_state import EngineStateStore
from recognition.anti_unifier import derive_recognizer
from recognition.outcome import FeatureBundle
from recognition.registry import RecognizerRegistry
from core.config import DEFAULT_CONFIG, DEFAULT_IDENTITY_PACK, RuntimeConfig
from core.physics.drive import DriveGradientMap, GradientField
from core.physics.energy import EnergyClass, EnergyProfile
from core.physics.exertion import CycleCost, ExertionMeter
from core.physics.learning import VaultPromotionPolicy
from core.physics.identity import (
    CharacterProfile,
    IdentityCheck,
    IdentityScore,
    TurnEvent,
)
from packs.ethics.check import EthicsCheck, EthicsContext
from packs.ethics.loader import (
    DEFAULT_ETHICS_PACK as _DEFAULT_ETHICS_PACK,
    EthicsPackError,
    load_ethics_pack,
)
from packs.identity.loader import load_identity_manifold
from chat.register_substantive import apply_substantive_register
from chat.register_variation import decorate_surface
from chat.atom_equivalence import atoms_for_graph_nodes, compare_atom_sets
from generate.realizer_guard import (
    DISCLOSURE_SURFACE as _GUARD_DISCLOSURE_SURFACE,
    check_surface as _check_realizer_surface,
)
from packs.anchor_lens.loader import AnchorLens, load_anchor_lens
from packs.register.loader import RegisterPack, load_register_pack
from packs.safety.check import SafetyCheck, SafetyContext
from packs.safety.loader import load_safety_pack
from field.state import FieldState
from generate.articulation import ArticulationPlan, realize
from generate.dialogue import DialogueRole, classify_dialogue_blade, propose_dialogue
from generate.graph_constraint import build_graph_constraint
from generate.intent_bridge import articulate_with_intent, build_graph_from_input
from generate.proposition import FrameRegistry, Proposition, propose
from generate.result import GenerationResult
from generate.stream import generate
from generate.surface import SentenceAssembler, SentencePlan, SurfaceContext
from ingest.gate import inject
from language_packs import OOVPolicy, load_mounted_packs, load_pack, load_pack_entries
from persona.motor import PersonaMotor
from session.context import SessionContext
from session.correction import CorrectionPass
from vault.decompose import default_decomposer, default_gate

_TOKEN_RE = re.compile(r"\w+", re.UNICODE)
# ADR-0073d (L1.4) — extracts the engaged ``cognitive_mode_label`` from a
# composer-emitted ``[lens(<lens_id>):<mode>]`` annotation.  The runtime
# uses this read-only to populate the TurnEvent telemetry field; the
# composer remains the only source of truth for engagement.
_ANCHOR_LENS_ANNOTATION_RE = re.compile(r"\[lens\(([^):]+)\):([^\]]+)\]")


def _auto_propose_from_candidates(candidates: list[DiscoveryCandidate]) -> None:
    from teaching.proposals import (
        ProposalError,
        ProposalLog,
        _current_revision,
        propose_from_candidate,
    )
    from teaching.source import ProposalSource

    log = ProposalLog()
    for candidate in candidates:
        source = ProposalSource(
            kind="contemplation",
            source_id=candidate.candidate_id,
            emitted_at_revision=_current_revision(),
        )
        try:
            propose_from_candidate(candidate, log=log, source=source)
        except ProposalError:
            pass


def _extract_anchor_lens_mode_label(surface: str, lens_id: str) -> str:
    """Return the engaged mode_label if *surface* carries a
    ``[lens(<lens_id>):<mode>]`` annotation for the given ``lens_id``.

    Returns ``""`` when:
      * surface is empty or contains no lens annotation
      * lens_id is empty (no lens loaded)
      * the annotation in surface is for a different lens_id (defensive)

    Pure read; no side effects.  Telemetry-only — the composer is the
    sole source of truth for engagement (ADR-0073c).
    """
    if not surface or not lens_id:
        return ""
    for match in _ANCHOR_LENS_ANNOTATION_RE.finditer(surface):
        if match.group(1) == lens_id:
            return match.group(2)
    return ""

_SEED_ALIASES = {
    "logos": "\u03bb\u03cc\u03b3\u03bf\u03c2",
    "dabar": "\u05d3\u05d1\u05e8",
    "or": "\u05d0\u05d5\u05e8",
    "phos": "\u03c6\u03c9\u03c2",
    "zoe": "\u03b6\u03c9\u03ae",
    "arche": "\u1f00\u03c1\u03c7\u03ae",
    "aletheia": "\u1f00\u03bb\u03ae\u03b8\u03b5\u03b9\u03b1",
}
_QUESTION_WORDS = frozenset({"what", "who", "how", "why", "when", "where", "which"})
# Comb pass 2026-05-21 — module-level constant so ``_prefer_prompt_anchor``
# does not allocate a fresh set on every English turn.  Aux-verbs that
# precede the prompt's content noun ("is", "are", "was", "were") get
# filtered out so the content-noun search lands on the actual subject.
_BE_FORMS: frozenset[str] = frozenset({"is", "are", "was", "were"})
_TERMINALS = frozenset({".", "?", ";", "!"})
_UNKNOWN_DOMAIN_SURFACE = "I don't know — insufficient grounding for that yet."


def _build_vault_probe(vault, vocab):
    """Return a vault probe for discovery contemplation (W-016).

    Queries the session vault at EpistemicStatus.COHERENT using the
    subject lemma's versor as the lookup key.  The probe is a pure
    read; it never writes to the vault or changes runtime state.

    Trust boundary: vault entries from SPECULATIVE/CONTESTED/FALSIFIED
    tiers are excluded by passing min_status=COHERENT to vault.recall.
    The probe returns only ``vault_coherent`` EvidencePointers per
    _VaultProbe contract (teaching/contemplation.py).
    """
    from teaching.discovery import EvidencePointer
    from teaching.epistemic import EpistemicStatus

    def _probe(subject: str, obj: str) -> tuple[EvidencePointer, ...]:
        try:
            query = vocab.get_versor(subject)
        except KeyError:
            return ()
        hits = vault.recall(query, top_k=3, min_status=EpistemicStatus.COHERENT)
        return tuple(
            EvidencePointer(
                source="vault_coherent",
                ref=str(hit["index"]),
                polarity="affirms",
                epistemic_status="coherent",
            )
            for hit in hits
        )

    return _probe


def _vault_probe_for_context(context: SessionContext | None) -> Any | None:
    """Return a _VaultProbe callable or None for the given session context."""
    if context is None:
        return None
    return _build_vault_probe(context.vault, context.vocab)


def _energy_scalar(energy_obj) -> float:
    if energy_obj is None:
        return 1.0
    if isinstance(energy_obj, EnergyProfile):
        return float(energy_obj.raw)
    try:
        return float(energy_obj)
    except (TypeError, ValueError):
        return 1.0


def _recall_energy_class_from_hits(hits: Sequence[dict]) -> str | None:
    if not hits:
        return None
    profile = hits[0].get("energy_profile")
    energy_class = getattr(profile, "energy_class", None)
    value = getattr(energy_class, "value", None)
    return value if isinstance(value, str) else None


def _is_question_input(raw_text: str, tokens: Sequence[str]) -> bool:
    if raw_text.strip().endswith("?"):
        return True
    return bool(tokens and tokens[0].casefold() in _QUESTION_WORDS)


def _stable_dialogue_role(role: DialogueRole, *, raw_text: str, tokens: Sequence[str]) -> DialogueRole:
    if role in {"question", "refute"} and not _is_question_input(raw_text, tokens):
        return "elaborate"
    return role


def _terminal_for_role(role: DialogueRole, output_language: str) -> str:
    if role == "question":
        return ";" if output_language == "grc" else "?"
    return "."


def _terminate_surface(surface: str, *, role: DialogueRole, output_language: str) -> str:
    stripped = surface.strip()
    if not stripped:
        return stripped
    if stripped[-1] in _TERMINALS:
        return stripped
    return f"{stripped}{_terminal_for_role(role, output_language)}"


def _prefer_prompt_anchor(
    articulation: ArticulationPlan,
    filtered_tokens: Sequence[str],
    *,
    output_language: str,
) -> ArticulationPlan:
    if output_language != "en" or len(filtered_tokens) < 2:
        return articulation
    # Comb pass 2026-05-21 — find the last content-bearing token by
    # reverse iteration with short-circuit; pre-fix this built a full
    # ``content_tokens`` list and then took ``[-1]``.  Also: cache
    # ``token.casefold()`` once per token via walrus operator instead
    # of calling it twice (against ``_QUESTION_WORDS`` and the
    # historical inline ``{"is", "are", "was", "were"}`` literal).
    anchor: str | None = None
    for token in reversed(filtered_tokens):
        lower = token.casefold()
        if lower in _QUESTION_WORDS or lower in _BE_FORMS:
            continue
        anchor = token
        break
    if anchor is None or anchor == articulation.subject:
        return articulation
    return replace(
        articulation,
        subject=anchor,
        surface=" ".join(part for part in (anchor, articulation.predicate, articulation.object) if part),
    )


@dataclass
class _StubBindingFrame:
    frame_id: str
    coherence_magnitude: float
    region_ids: frozenset
    cycle_index: int


@dataclass(frozen=True, slots=True)
class _FieldStateWithVersor:
    """Adapter exposing ``versor_condition`` for SafetyContext.

    ``FieldState`` itself does not carry a precomputed
    ``versor_condition`` attribute; it is computed on demand from
    ``versor_condition(state.F)``.  The SafetyCheck predicate for
    ``preserve_versor_closure`` reads ``ctx.field_state.versor_condition``
    via ``getattr``.  This adapter exposes the precomputed value so the
    predicate is runtime-checkable each turn.
    """

    versor_condition: float


def _hash_identity_manifold(manifold) -> str:
    """Deterministic SHA-256 of the load-bearing identity-manifold fields.

    ADR-0035 — feeds the ``no_identity_override`` predicate in
    :class:`SafetyCheck`.  The runtime never mutates ``identity_manifold``
    after composition, so before- and after-turn hashes are equal by
    construction; an unequal hash would indicate the predicate's exact
    failure mode.
    """
    payload = {
        "value_axes": [
            {
                "axis_id": axis.axis_id,
                "name": axis.name,
                "direction": list(axis.direction),
                "weight": axis.weight,
            }
            for axis in manifold.value_axes
        ],
        "boundary_ids": sorted(manifold.boundary_ids),
        "alignment_threshold": manifold.alignment_threshold,
    }
    blob = json.dumps(payload, sort_keys=True, separators=(",", ":")).encode("utf-8")
    return hashlib.sha256(blob).hexdigest()


def _surface_contains_hedge(surface: str, manifold) -> bool:
    """Detect whether the realized surface emitted a hedge phrase.

    Compares case-insensitively against the manifold's preferred hedge
    phrases (ADR-0028).  False when surface is empty.  Coarse but
    deterministic: the predicate downstream is observational, so
    occasional false negatives are surfaced as
    ``acknowledge_uncertainty`` violations in audit and corrected by
    refining hedge detection, not by silently passing.
    """
    if not surface:
        return False
    prefs = getattr(manifold, "surface_preferences", None)
    if prefs is None:
        return False
    candidates: list[str] = []
    for field_name in (
        "preferred_hedge_strong",
        "preferred_hedge_soft",
        "preferred_qualifier",
    ):
        value = getattr(prefs, field_name, "")
        if value:
            candidates.append(value)
    for _, hedge in getattr(prefs, "axis_hedges", ()) or ():
        for sub in ("strong", "soft", "qualifier"):
            value = getattr(hedge, sub, "")
            if value:
                candidates.append(value)
    surface_fold = surface.casefold()
    return any(c.casefold() in surface_fold for c in candidates if c)


def _make_trajectory_from_result(result, turn: int):
    from core.physics.reasoning import TrajectoryOperator

    operator = TrajectoryOperator()
    states = result.trajectory or (result.final_state,)
    frames = [
        _StubBindingFrame(
            frame_id=f"t{turn}_s{i}",
            coherence_magnitude=_energy_scalar(getattr(fs, "energy", None)),
            region_ids=frozenset({str(getattr(fs, "node", 0))}),
            cycle_index=turn,
        )
        for i, fs in enumerate(states)
    ]
    return operator.build(frames, trajectory_id=f"turn_{turn}")


@dataclass(frozen=True, slots=True)
class ChatResponse:
    surface: str
    proposition: Proposition
    articulation: ArticulationPlan
    articulation_surface: str
    dialogue_role: DialogueRole
    versor_condition: float
    output_language: str
    frame_pack: str
    walk_surface: str
    salience_top_k: int | None
    candidates_used: int | None
    vault_hits: int
    identity_score: IdentityScore | None
    character_profile: CharacterProfile
    flagged: bool
    recall_energy_class: str | None = None
    # ADR-0023 §2 — per-transition admissibility evidence and region
    # provenance flag.  An empty tuple is the contract for "no
    # admissibility was checked this turn" (cold start, refusal, stub).
    admissibility_trace: tuple = ()
    region_was_unconstrained: bool = True
    # ADR-0035 — verdicts surfaced from SafetyCheck and EthicsCheck.
    # ``None`` only on stub/refusal paths that bypass the turn loop.
    safety_verdict: object = None
    ethics_verdict: object = None
    # ADR-0039 — unified TurnVerdicts bundle carrying identity / safety
    # / ethics verdicts and the two remediation flags
    # (refusal_emitted, hedge_injected).  Typed as ``object`` to avoid
    # coupling at module-resolution time; downcast at use site.
    verdicts: object = None
    # ADR-0048 / ADR-0050 / ADR-0052 — provenance tag for the surface's
    # grounding.  One of:
    #   "vault"    — answer drawn from session vault evidence (main path).
    #   "pack"     — answer drawn from the ratified language pack
    #                (cold-start DEFINITION/RECALL/COMPARISON on pack-known
    #                lemmas — ADR-0048 / ADR-0050).
    #   "teaching" — answer drawn from a reviewed teaching-chain corpus
    #                (cold-start CAUSE/VERIFICATION — ADR-0052).
    #   "none"     — universal "insufficient grounding" disclosure on stub.
    # The string is preserved verbatim in TurnEvent for downstream audit.
    grounding_source: str = "none"
    # ADR-0071 (R4) — pre-decoration surface.  ``surface`` is the
    # user-facing string AFTER seeded discourse-marker decoration;
    # ``pre_decoration_surface`` is the realizer's output BEFORE the
    # decoration step.  The cognition pipeline reads this field to
    # compute ``trace_hash`` so register decoration cannot leak into
    # the truth path (ADR-0069 invariant C).  Empty string ⇒ identical
    # to ``surface`` (no decoration applied this turn).
    pre_decoration_surface: str = ""
    # Phase 3 epistemic taxonomy — first-class state axes per turn.
    # Values are lower_snake_case strings matching core.epistemic_state
    # enum values so the field serializes stably without importing the
    # enum here.  Defaults match TurnEvent defaults (undetermined /
    # unassessable) so pre-Phase-3 callers that omit these fields are
    # treated conservatively.
    epistemic_state: str = "undetermined"
    normative_clearance: str = "unassessable"
    # Comma-separated violated boundary/commitment IDs when normative
    # clearance is VIOLATED or SUPPRESSED; empty string otherwise.
    normative_detail: str = ""
    # ADR-0206 — Response Governance Bridge reach level for this turn.
    # Mirrors the TurnEvent field so callers (CLI, demos, tests) can read
    # the governed reach level from ChatResponse.  Scaffold contract:
    # always "strict" (govern_response is STRICT-only until widening is
    # built).  ``"strict"`` default preserves byte-identity for callers
    # that construct ChatResponse without this field.
    reach_level: str = "strict"
    # ADR-0072 (R5) — operator-visible register identity per turn.
    # Mirrors the TurnEvent fields so callers (CLI, demos, tests) can
    # read the register state from ChatResponse without re-parsing the
    # telemetry JSONL.  ``""`` defaults preserve pre-R5 byte-identity
    # for callers that construct ChatResponse without these fields.
    register_id: str = ""
    register_variant_id: str = ""
    # ADR-0073d (L1.4) — operator-visible anchor-lens identity per turn.
    # Mirrors the TurnEvent fields so callers (CLI, demos, tests) can
    # read the lens state from ChatResponse without re-parsing the
    # telemetry JSONL.  ``""`` defaults preserve pre-L1.4 byte-identity.
    anchor_lens_id: str = ""
    anchor_lens_mode_label: str = ""
    # ADR-0075 (C1) — realizer slot-type guard verdict.  Mirrors the
    # TurnEvent fields so callers (CLI, demos, tests) can read the
    # guard state from ChatResponse without re-parsing the telemetry
    # JSONL.  ``""`` defaults preserve pre-C1 byte-identity.
    realizer_guard_status: str = ""
    realizer_guard_rule: str = ""
    # ADR-0077 (R6) — register layering boundary surface.  Carries the
    # composer output BEFORE any register transformation (substantive
    # or decorative).  The cognition pipeline hashes this field for
    # ``trace_hash`` when present, preserving R5's load-bearing
    # invariant — substantive register transforms must not move
    # ``trace_hash``.  Empty string ⇒ pre-R6 caller; pipeline falls
    # back to ``pre_decoration_surface`` (byte-identity preserved).
    register_canonical_surface: str = ""
    # ADR-0078 (Phase 1) — observational composer/graph atom
    # equivalence telemetry mirrored from TurnEvent.
    composer_graph_atom_status: str = ""
    composer_atom_set_hash: str = ""
    graph_atom_set_hash: str = ""
    composer_graph_atom_overlap_count: int = 0
    # ADR-0088 Phase B (audit Finding 2, 2026-05-20) — alphabetic-
    # filtered walk tokens from the recall step.  Populated only on
    # the main path; the stub / refusal paths leave this empty.
    # Consumed by ``CognitiveTurnPipeline`` when
    # ``RuntimeConfig.realizer_grounded_authority`` is True so the
    # proposition graph can be grounded before ``realize_semantic``
    # is invoked.  Empty tuple preserves pre-ADR-0088 byte-identity
    # for every caller that constructs ChatResponse without this
    # field.
    recalled_words: tuple[str, ...] = ()
    # ADR-0024 Phase 2 — stable refusal reason value
    refusal_reason: str = ""
    dispatch_trace: DispatchTrace | None = None


class ChatRuntime:
    def __init__(
        self,
        pack_id: str | Sequence[str] | None = None,
        *,
        frame_pack: str | None = None,
        config: RuntimeConfig = DEFAULT_CONFIG,
        no_load_state: bool = False,
        engine_state_path: Any | None = None,
    ) -> None:
        if pack_id is not None or frame_pack is not None:
            pack_ids = (pack_id,) if isinstance(pack_id, str) else tuple(pack_id or config.input_packs)
            # Use dataclasses.replace so newer RuntimeConfig fields
            # (identity_pack, ethics_pack, forward_graph_constraint,
            # composed_surface, thread_anaphora, etc.) survive the
            # pack_id / frame_pack override path.  The previous manual
            # reconstruction silently dropped any field not enumerated
            # here, which would let a caller like
            # ``ChatRuntime(pack_id="x", config=RuntimeConfig(composed_surface=True))``
            # lose composed_surface without warning.
            from dataclasses import replace as _dc_replace
            resolved_config = _dc_replace(
                config,
                input_packs=pack_ids,
                frame_pack=frame_pack or config.frame_pack,
            )
        else:
            resolved_config = config
            pack_ids = tuple(config.input_packs)

        self.config = resolved_config
        manifests = []
        manifolds = []
        entries = []
        for mounted_pack_id in pack_ids:
            manifest, manifold = load_pack(mounted_pack_id)
            manifests.append(manifest)
            manifolds.append(manifold)
            entries.extend(load_pack_entries(mounted_pack_id))

        manifold = manifolds[0] if len(pack_ids) == 1 else load_mounted_packs(pack_ids)
        self._manifests = tuple(manifests)
        # Comb pass 2026-05-21 — precompute OOV-policy aggregates so
        # ``_apply_oov_policy`` doesn't rescan every manifest per OOV
        # token.  Manifests are immutable post-construction, so a
        # one-time aggregate is safe and cuts the hot path from
        # O(packs × OOV) to O(OOV).
        self._all_manifests_fail_closed: bool = all(
            m.oov_policy is OOVPolicy.FAIL_CLOSED for m in self._manifests
        )
        self._any_manifest_proposes_vocab: bool = any(
            m.oov_policy is OOVPolicy.PROPOSE_VOCAB_EXPANSION for m in self._manifests
        )
        identity_pack_id = resolved_config.identity_pack or DEFAULT_IDENTITY_PACK
        identity_manifold = load_identity_manifold(identity_pack_id)
        self.safety_pack = load_safety_pack()
        ethics_pack_id = resolved_config.ethics_pack or _DEFAULT_ETHICS_PACK
        try:
            self.ethics_pack = load_ethics_pack(ethics_pack_id)
        except EthicsPackError:
            if ethics_pack_id == _DEFAULT_ETHICS_PACK:
                raise
            self.ethics_pack = load_ethics_pack(_DEFAULT_ETHICS_PACK)
            ethics_pack_id = _DEFAULT_ETHICS_PACK
        self.ethics_pack_id = ethics_pack_id
        # ADR-0068 / ADR-0069 — register pack load.  None resolves to the
        # in-memory unregistered sentinel (structurally identical to
        # default_neutral_v1).  Invalid ids fail-fast at runtime init,
        # not at first turn.  At R2 the register is loaded but no
        # composer consumes it; byte-identity invariants pin this.
        if resolved_config.register_pack_id is None:
            self.register_pack: RegisterPack = RegisterPack.unregistered()
        else:
            self.register_pack = load_register_pack(
                resolved_config.register_pack_id
            )
        self.register_pack_id = resolved_config.register_pack_id
        # ADR-0073b — anchor-lens load.  ``None`` resolves to the
        # in-memory unanchored sentinel (structurally identical to
        # ``default_unanchored_v1``).  Invalid ids fail-fast at
        # runtime init, not at first turn.  At L1.2 the lens is
        # loaded and stored but no composer consumes it; the
        # ``anchor_lens_byte_identity_null_lift`` invariant pins this.
        if resolved_config.anchor_lens_id is None:
            self.anchor_lens: AnchorLens = AnchorLens.unanchored()
        else:
            self.anchor_lens = load_anchor_lens(
                resolved_config.anchor_lens_id
            )
        self.anchor_lens_id = resolved_config.anchor_lens_id
        self.identity_manifold = type(identity_manifold)(
            value_axes=identity_manifold.value_axes,
            boundary_ids=(
                identity_manifold.boundary_ids
                | self.safety_pack.boundary_ids
                | self.ethics_pack.commitment_ids
            ),
            alignment_threshold=identity_manifold.alignment_threshold,
            surface_preferences=identity_manifold.surface_preferences,
        )
        self.identity_pack_id = identity_pack_id
        persona_motor = PersonaMotor.identity()
        self._context = SessionContext(
            manifold,
            persona=persona_motor,
            vault_reproject_interval=resolved_config.vault_reproject_interval,
        )
        self._frame_registry = FrameRegistry.from_pack(resolved_config.frame_pack, self._context.vocab)
        self._surface_by_fold = {e.surface.casefold(): e.surface for e in entries}
        self._surface_by_fold.update(_SEED_ALIASES)
        self._pos_by_surface = {e.surface: (e.pos or e.part_of_speech or "X") for e in entries}
        self.exertion_meter = ExertionMeter(capacity_ceiling=128.0)
        self.drive_gradients = tuple(GradientField(axis=axis, magnitude=0.75) for axis in self.identity_manifold.value_axes)
        self._drive_map = DriveGradientMap(gradients=self.drive_gradients)
        self.character_profile = CharacterProfile.from_manifold(
            self.identity_manifold,
            drive_summaries={g.axis.name: g.magnitude for g in self.drive_gradients},
            fatigue_index=0.0,
        )
        self._identity_check = IdentityCheck()
        self.safety_check = SafetyCheck()
        self.ethics_check = EthicsCheck()
        self._identity_manifold_hash: str = _hash_identity_manifold(
            self.identity_manifold,
        )
        self._last_refusal_was_typed: bool = True
        self.turn_log: List[TurnEvent] = []
        from chat.thread_context import ThreadContext
        self.thread_context = ThreadContext()
        self._telemetry_sink: TurnEventSink | None = None
        self._telemetry_include_content: bool = False
        self._discovery_sink: DiscoveryCandidateSink | None = None
        self._oov_sink: Any = None
        self._contemplate_discoveries: bool = False
        self._correction_pass = CorrectionPass()
        self._last_valence: float = 0.0
        # Phase 3 — most-recent plan-contemplation findings (tuple of
        # SPECULATIVE ``ContemplationFinding`` records).  Reset to ``()``
        # on every turn; populated only when ``config.discourse_contemplation``
        # is True AND the planner actually engaged on the turn.  Exposed
        # via the ``last_plan_findings`` property below.
        self._last_plan_findings: tuple[Any, ...] = ()
        # Phase 4 — most-recent plan-articulation metrics (PlanMetrics).
        # Reset to ``None`` between turns.  Populated under the same
        # gating discipline as ``_last_plan_findings``: requires
        # ``config.discourse_contemplation`` + an engaged planner.
        self._last_plan_metrics: Any | None = None
        # Phase 5 — articulation-observation sink (per-turn JSONL stream
        # consumed by the offline ``mine_articulation_observations``
        # miner).  Attached via ``attach_articulation_sink``; ``None``
        # by default so the runtime emits nothing until an operator
        # opts in.  Behaviour mirrors ``attach_telemetry_sink``:
        # append-only, fail-fast on sink errors, deterministic JSONL.
        self._articulation_sink: Any | None = None
        self._articulation_turn_counter: int = 0
        # W-013 — last classified intent, updated each turn for /explain REPL use.
        self._last_intent: Any | None = None
        self._last_input_text: str = ""
        self._engine_state_store: EngineStateStore | None = (
            None if no_load_state else EngineStateStore(engine_state_path)
        )
        self._recognizer_registry: RecognizerRegistry = RecognizerRegistry()
        self._turn_count: int = 0
        self._pending_candidates: list[DiscoveryCandidate] = []
        self._pending_recognizer_examples: list[
            tuple[tuple[str, ...], FeatureBundle]
        ] = []
        # W-024 / ADR-0158 — reboot event JSONL line buffered here when a
        # checkpoint is loaded, flushed to the sink on attach_telemetry_sink.
        # None means no reboot was detected this session.
        self._pending_reboot_payload: str | None = None
        if self._engine_state_store is not None and self._engine_state_store.exists():
            self._load_engine_state()

    def _load_engine_state(self) -> None:
        store = self._engine_state_store
        if store is None:
            return
        recognizers = store.load_recognizers()
        self._recognizer_registry = RecognizerRegistry.from_recognizers(recognizers)
        self._pending_candidates = store.load_discovery_candidates()
        manifest = store.load_manifest() or {}
        self._turn_count = int(manifest.get("turn_count", 0))
        # W-024 / ADR-0158 — buffer reboot event for emission when sink attaches.
        from engine_state import _git_revision
        self._pending_reboot_payload = format_reboot_event_jsonl(
            restored_turn_count=self._turn_count,
            stored_revision=str(manifest.get("written_at_revision", "unknown")),
            current_revision=_git_revision(),
            recognizers_count=len(recognizers),
            candidates_count=len(self._pending_candidates),
        )
        if self.config.auto_proposal_enabled and self._pending_candidates:
            _auto_propose_from_candidates(self._pending_candidates)

    def checkpoint_engine_state(self) -> None:
        store = self._engine_state_store
        if store is None:
            return
        if (
            self.config.recognition_grounded_graph
            and self._pending_recognizer_examples
        ):
            recognizer = derive_recognizer(tuple(self._pending_recognizer_examples))
            self._recognizer_registry.register(recognizer)
            self._pending_recognizer_examples.clear()
        store.save_recognizers(self._recognizer_registry.all())
        candidates_to_save = self._pending_candidates
        if self.config.auto_contemplate and candidates_to_save:
            from teaching.contemplation import contemplate
            vault_probe = _vault_probe_for_context(self._context) if self._context else None
            candidates_to_save = [
                contemplate(c, vault_probe=vault_probe)
                for c in candidates_to_save
            ]
        store.save_discovery_candidates(candidates_to_save)
        store.save_manifest(self._turn_count)

    def record_recognition_example(
        self,
        tokens: tuple[str, ...],
        bundle: FeatureBundle,
    ) -> None:
        self._pending_recognizer_examples.append((tuple(tokens), bundle))

    def finalize_turn_trace_hash(self, trace_hash: str) -> None:
        """ADR-0153 (W-020a) — back-stamp the canonical trace_hash.

        Called by ``CognitiveTurnPipeline.process`` after
        ``compute_trace_hash`` produces the turn's canonical
        SHA-256.  Stamps the trace_hash onto the most recent
        TurnEvent and any DiscoveryCandidate emitted during this
        turn (i.e., the unstamped tail of ``_pending_candidates``),
        then re-persists the candidates checkpoint so the on-disk
        audit trail names the originating turn instead of the
        prior empty-string default.

        No-op when ``trace_hash`` is empty (pre-pipeline call sites,
        refusal stub path).  Idempotent: stamping a tail whose
        ``source_turn_trace`` is already non-empty halts the back-walk.
        """
        if not trace_hash:
            return
        from dataclasses import replace
        if self.turn_log:
            last_event = self.turn_log[-1]
            if not last_event.trace_hash:
                self.turn_log[-1] = replace(last_event, trace_hash=trace_hash)
        stamped = False
        for idx in range(len(self._pending_candidates) - 1, -1, -1):
            cand = self._pending_candidates[idx]
            if cand.source_turn_trace:
                break
            self._pending_candidates[idx] = replace(
                cand, source_turn_trace=trace_hash
            )
            stamped = True
        if stamped and self._engine_state_store is not None:
            self._engine_state_store.save_discovery_candidates(
                self._pending_candidates
            )

    def first_admitted_recognizer(self):
        if not self.config.recognition_grounded_graph:
            return None
        return self._recognizer_registry.first_admitted()

    def _checkpointed_response(self, response: ChatResponse) -> ChatResponse:
        self._turn_count += 1
        self.checkpoint_engine_state()
        return response

    @property
    def session(self) -> SessionContext:
        return self._context

    @property
    def last_plan_findings(self) -> tuple[Any, ...]:
        """Phase 3 — most-recent plan-contemplation findings.

        Tuple of ``core.contemplation.schema.ContemplationFinding``
        records (always SPECULATIVE per ADR-0080).  Populated only
        when ``config.discourse_contemplation`` is True and the
        discourse planner engaged on the turn — empty tuple
        otherwise.  Read-only observation surface; the runtime
        itself never acts on findings, the offline contemplation
        miner does.
        """
        return self._last_plan_findings

    @property
    def last_plan_metrics(self) -> Any | None:
        """Phase 4 — most-recent plan articulation metrics.

        ``core.contemplation.plan_metrics.PlanMetrics`` instance
        when the discourse planner engaged on the most recent turn
        AND ``config.discourse_contemplation`` is True; ``None``
        otherwise.  Read-only quantitative companion to
        ``last_plan_findings`` (which carries the qualitative
        SPECULATIVE concerns).  Designed for downstream aggregation
        — Phase 5's offline contemplation miner streams these
        across turns to score plan-quality patterns the runtime
        never tries to act on alone.
        """
        return self._last_plan_metrics

    def explain_last_turn(self) -> str:
        """Return a canonical natural-language restatement of the last turn (W-013).

        Feeds the last classified intent through ``core.cognition.explain``'s
        dispatch table and returns the resulting canonical prompt string.
        This is the ``/explain`` REPL command's backing method.

        Returns an empty string when no turn has been processed yet or when
        the intent could not be classified (UNKNOWN tag).
        """
        from core.cognition.explain import explain_from_intent
        return explain_from_intent(
            self._last_intent,
            correction_text=self._last_input_text,
        )

    def attach_telemetry_sink(
        self,
        sink: TurnEventSink | None,
        *,
        include_content: bool = False,
    ) -> None:
        """ADR-0040 — attach a structured-logging sink."""
        self._telemetry_sink = sink
        self._telemetry_include_content = bool(include_content)
        # W-024 / ADR-0158 — flush buffered reboot event now that sink is live.
        if sink is not None and self._pending_reboot_payload is not None:
            sink.emit(self._pending_reboot_payload)
            self._pending_reboot_payload = None

    def attach_articulation_sink(self, sink: Any | None) -> None:
        """Phase 5 — attach a sink for per-turn articulation observations.

        ``sink`` must satisfy
        ``chat.articulation_telemetry.ArticulationObservationSink``
        (any object with ``def emit(line: str) -> None``).  Pass
        ``None`` to detach.

        The sink receives one canonical JSONL line per turn that
        engages the discourse planner AND has
        ``config.discourse_contemplation == True``; non-engaged turns
        emit nothing.  Lines are byte-identical for byte-equal plans
        — the offline miner relies on this for deterministic
        aggregation.
        """
        self._articulation_sink = sink

    def attach_oov_sink(self, sink: Any) -> None:
        """Phase 2.3 — attach an OOV candidate sink."""
        self._oov_sink = sink

    def attach_discovery_sink(
        self,
        sink: DiscoveryCandidateSink | None,
    ) -> None:
        """ADR-0055 Phase B — attach a DiscoveryCandidate sink."""
        self._discovery_sink = sink

    def attach_contemplation(self, *, enabled: bool = True) -> None:
        """ADR-0056 Phase C1 — opt-in inline contemplation."""
        self._contemplate_discoveries = bool(enabled)

    def _push_thread_summary(
        self,
        *,
        turn_event: TurnEvent,
        intent_tag: Any,
        intent_subject: str | None,
        grounding_source: str | None,
        surface: str | None = None,
    ) -> None:
        """P3.1 — append one TurnSummary to the bounded session-thread context."""
        from chat.thread_context import TurnSummary

        turn_index = len(self.turn_log) - 1
        if intent_tag is not None and hasattr(intent_tag, "name"):
            intent_name = str(intent_tag.name).lower()
        else:
            intent_name = ""
        subject = (intent_subject or "").strip().lower()
        source = (grounding_source or "none").lower()

        chain_id: str | None = None
        corpus_id: str | None = None
        if source == "teaching" and subject and intent_name in {"cause", "verification"}:
            from chat.teaching_grounding import _all_chains_index
            chain = _all_chains_index().get((subject, intent_name))
            if chain is not None:
                chain_id = chain.chain_id
                corpus_id = chain.corpus_id
        _ = surface

        self.thread_context.push(
            TurnSummary(
                turn_index=turn_index,
                intent_tag_name=intent_name,
                subject=subject,
                grounding_source=source,
                chain_id=chain_id,
                corpus_id=corpus_id,
            )
        )

    def _emit_oov_candidate(
        self,
        *,
        turn_event: TurnEvent,
        intent_tag: Any,
        token: str | None,
    ) -> None:
        """P2.3 — emit one OOVCandidate per OOV-grounded turn."""
        sink = self._oov_sink
        if sink is None or not token:
            return
        from teaching.oov_sink import (
            OOVCandidate,
            format_oov_candidate_jsonl,
            hash_oov_candidate_id,
        )
        from generate.intent import IntentTag

        if intent_tag is None or not isinstance(intent_tag, IntentTag):
            return
        intent_name = intent_tag.name.lower()
        trace_hash = getattr(turn_event, "trace_hash", "") or ""
        boundary_clean = (
            not getattr(turn_event, "refusal_emitted", False)
            and not getattr(turn_event, "hedge_injected", False)
        )
        cleaned_token = (token or "").strip().lower()
        if not cleaned_token:
            return
        candidate_id = hash_oov_candidate_id(cleaned_token, intent_name, trace_hash)
        candidate = OOVCandidate(
            candidate_id=candidate_id,
            token=cleaned_token,
            intent=intent_name,  # type: ignore[arg-type]
            trigger="unresolved_subject",
            source_turn_trace=trace_hash,
            boundary_clean=boundary_clean,
        )
        sink.emit(format_oov_candidate_jsonl(candidate))

    def _emit_discovery_candidates(
        self,
        *,
        turn_event: TurnEvent,
        intent_tag: Any,
        intent_subject: str | None,
        grounding_source: str | None,
    ) -> None:
        candidates = extract_discovery_candidates(
            turn_event,
            intent_tag,
            intent_subject,
            grounding_source=grounding_source,
        )
        if self._contemplate_discoveries and candidates:
            from teaching.contemplation import contemplate
            vault_probe = (
                _build_vault_probe(self._context.vault, self._context.vocab)
                if self.config.vault_probe_discoveries
                else None
            )
            candidates = tuple(
                contemplate(c, vault_probe=vault_probe) for c in candidates
            )
        self._pending_candidates.extend(candidates)
        sink = self._discovery_sink
        if sink is None:
            return
        for candidate in candidates:
            sink.emit(format_candidate_jsonl(candidate))

    def _emit_turn_event(self, event: TurnEvent) -> None:
        sink = self._telemetry_sink
        if sink is None:
            return
        line = format_turn_event_jsonl(
            event,
            safety_pack_id=self.safety_pack.pack_id,
            ethics_pack_id=self.ethics_pack_id,
            identity_pack_id=self.identity_pack_id,
            include_content=self._telemetry_include_content,
        )
        sink.emit(line)

    def _tokenize(self, text: str) -> list[str]:
        return [self._surface_by_fold.get(m.group(0).casefold(), m.group(0)) for m in _TOKEN_RE.finditer(text)]

    def tokenize(self, text: str) -> list[str]:
        return self._tokenize(text)

    def _apply_oov_policy(self, tokens: list[str]) -> list[str]:
        # Comb pass 2026-05-21 — OOV-policy aggregates are precomputed
        # at ``__init__`` so this method stays O(OOV tokens) rather
        # than O(packs × OOV tokens).  See ``_all_manifests_fail_closed``
        # / ``_any_manifest_proposes_vocab``.
        kept: list[str] = []
        for token in tokens:
            try:
                self._context.vocab.get_versor(token)
                kept.append(token)
            except KeyError:
                if self._all_manifests_fail_closed:
                    raise
                if self._any_manifest_proposes_vocab:
                    raise KeyError(f"OOV token requires vocab proposal: {token}")
                kept.append(token)
        return kept

    def _syntactic_guard(self, tokens: tuple[str, ...]) -> list[str]:
        out: list[str] = []
        prev_pos: str | None = None
        for token in tokens:
            pos = self._pos_by_surface.get(token, "X")
            if pos == prev_pos:
                continue
            out.append(token)
            prev_pos = pos
        return out

    def _dialogue_reference(self) -> np.ndarray | None:
        blade = self._context.last_dialogue_blade
        if blade is None or float(np.linalg.norm(blade)) < 1e-8:
            return None
        return blade

    def _apply_drive_bias(self, field_state: FieldState) -> FieldState:
        return field_state

    def _build_surface_context(self, identity_score, current_valence: float) -> SurfaceContext:
        active = self._context.referents.active_referent()
        alignment = float(identity_score.alignment) if identity_score is not None else 1.0
        deviation_axes = (
            frozenset(identity_score.deviation_axes)
            if identity_score is not None
            else frozenset()
        )
        prefs = self.identity_manifold.surface_preferences
        axis_hedges = tuple(
            (axis_id, hedge.strong, hedge.soft, hedge.qualifier)
            for axis_id, hedge in prefs.axis_hedges
        )
        return SurfaceContext(
            active_referent_surface=active.surface if active is not None else "",
            active_referent_slot=active.slot if active is not None else "neut_sg",
            identity_alignment=alignment,
            valence_delta=current_valence - self._last_valence,
            elab_conjunction="",
            hedge_threshold_strong=prefs.hedge_threshold_strong,
            hedge_threshold_soft=prefs.hedge_threshold_soft,
            preferred_hedge_strong=prefs.preferred_hedge_strong,
            preferred_hedge_soft=prefs.preferred_hedge_soft,
            claim_strength=prefs.claim_strength,
            qualified_band_high=prefs.qualified_band_high,
            preferred_qualifier=prefs.preferred_qualifier,
            deviation_axes=deviation_axes,
            axis_hedges=axis_hedges,
        )

    def _maybe_pack_grounded_surface(
        self, text: str, gate_source: str, *, allow_warm: bool = False, attempts: list[DispatchAttempt] | None = None
    ) -> tuple[str, str, tuple[str, ...]] | None:
        """Return ``(surface, grounding_source)`` or ``None``.

        ADR-0048 / ADR-0050 / ADR-0052 — three reviewed sources of
        cold-start grounding share this dispatcher.

        ``allow_warm=True`` bypasses the empty-vault gate so the warm
        path can engage pack-grounding for pack-resident DEFINITION /
        RECALL / NARRATIVE / EXAMPLE / COMPARISON / PROCEDURE intents
        — addresses ``warm_grounding_stability`` regression where
        turn-2 of the same prompt drifted from a coherent pack surface
        to a walk fragment.  CAUSE / VERIFICATION still return None
        when no teaching chain exists, preserving the discovery signal.
        """
        if not allow_warm and gate_source != "empty_vault":
            if attempts is not None:
                for src in ("pack", "teaching", "partial", "oov"):
                    attempts.append(DispatchAttempt(source=src, outcome="skipped", reason="warm_path_disabled"))
            return None
        if self.config.output_language != "en":
            if attempts is not None:
                for src in ("pack", "teaching", "partial", "oov"):
                    attempts.append(DispatchAttempt(source=src, outcome="skipped", reason="non_english_output"))
            return None
        from generate.intent import IntentTag
        from generate.intent_bridge import classify_intent_from_input
        intent = classify_intent_from_input(text)
        self._last_intent = intent  # W-013: expose for /explain
        if intent.tag is IntentTag.COMPARISON:
            lemma_a = (intent.subject or "").strip().rstrip(".,?!;:")
            lemma_b = (intent.secondary_subject or "").strip().rstrip(".,?!;:")
            if lemma_a and lemma_b:
                surface = pack_grounded_comparison_surface(
                    lemma_a, lemma_b, register=self.register_pack,
                )
                if surface is not None:
                    if attempts is not None:
                        attempts.append(DispatchAttempt(source="pack", outcome="admitted", reason="pack_comparison_surface_found"))
                    return (surface, "pack", ())
                else:
                    if attempts is not None:
                        attempts.append(DispatchAttempt(source="pack", outcome="fell_through", reason="no_pack_comparison_surface"))
                from chat.partial_surface import partial_comparison_surface
                partial = partial_comparison_surface(lemma_a, lemma_b)
                if partial is not None:
                    if attempts is not None:
                        attempts.append(DispatchAttempt(source="partial", outcome="admitted", reason="partial_comparison_surface_found"))
                    return (partial[0], "partial", ())
                else:
                    if attempts is not None:
                        attempts.append(DispatchAttempt(source="partial", outcome="fell_through", reason="no_partial_comparison_surface"))
            else:
                if attempts is not None:
                    attempts.append(DispatchAttempt(source="pack", outcome="fell_through", reason="missing_comparison_lemmas"))
                    attempts.append(DispatchAttempt(source="partial", outcome="fell_through", reason="missing_comparison_lemmas"))
        if intent.tag is IntentTag.NARRATIVE:
            lemma = (intent.subject or "").strip()
            if lemma:
                from chat.narrative_surface import narrative_grounded_surface
                surface = narrative_grounded_surface(
                    lemma, register=self.register_pack,
                )
                if surface is not None:
                    if attempts is not None:
                        attempts.append(DispatchAttempt(source="teaching", outcome="admitted", reason="narrative_surface_found"))
                    return (surface, "teaching", ())
                else:
                    if attempts is not None:
                        attempts.append(DispatchAttempt(source="teaching", outcome="fell_through", reason="no_narrative_surface"))
            else:
                if attempts is not None:
                    attempts.append(DispatchAttempt(source="teaching", outcome="fell_through", reason="missing_subject"))
        if intent.tag is IntentTag.EXAMPLE:
            lemma = (intent.subject or "").strip()
            if lemma:
                from chat.example_surface import example_grounded_surface
                surface = example_grounded_surface(
                    lemma, register=self.register_pack,
                )
                if surface is not None:
                    if attempts is not None:
                        attempts.append(DispatchAttempt(source="teaching", outcome="admitted", reason="example_surface_found"))
                    return (surface, "teaching", ())
                else:
                    if attempts is not None:
                        attempts.append(DispatchAttempt(source="teaching", outcome="fell_through", reason="no_example_surface"))
            else:
                if attempts is not None:
                    attempts.append(DispatchAttempt(source="teaching", outcome="fell_through", reason="missing_subject"))
        if intent.tag in (IntentTag.CAUSE, IntentTag.VERIFICATION):
            lemma = (intent.subject or "").strip()
            if lemma:
                if (
                    intent.tag is IntentTag.VERIFICATION
                    and intent.relation
                    and intent.secondary_subject
                ):
                    surface = pack_grounded_relation_confirmation_surface(
                        lemma,
                        intent.relation,
                        intent.object or intent.secondary_subject,
                        negated=intent.negated,
                    )
                    if surface is not None:
                        if attempts is not None:
                            attempts.append(DispatchAttempt(source="pack", outcome="admitted", reason="pack_relation_confirmation_found"))
                        return (surface, "pack", ())
                    else:
                        if attempts is not None:
                            attempts.append(DispatchAttempt(source="pack", outcome="fell_through", reason="no_pack_relation_confirmation"))
                elif intent.tag is IntentTag.VERIFICATION:
                    if attempts is not None:
                        attempts.append(DispatchAttempt(source="pack", outcome="skipped", reason="missing_relation_or_secondary_subject"))
                # ADR-0085 — gloss-aware CAUSE surface (opt-in).  Tried
                # FIRST so a lemma with a ratified gloss gets an
                # explanation-shaped answer drawn from the gloss text
                # instead of the chain-walk's structurally-correct-but-
                # bureaucratic domain-tag walk.  Falls through to the
                # chain-walk on None (no gloss for this lemma), so the
                # null-drop invariant holds: every case that lifted
                # pre-ADR-0085 still lifts; only the *frame* shifts on
                # lemmas where a gloss exists.
                if (
                    self.config.gloss_aware_cause
                    and intent.tag is IntentTag.CAUSE
                ):
                    surface = gloss_aware_cause_surface(
                        lemma, register=self.register_pack,
                        anchor_lens=self.anchor_lens,
                    )
                    if surface is not None:
                        if attempts is not None:
                            attempts.append(DispatchAttempt(source="pack", outcome="admitted", reason="gloss_aware_cause_found"))
                        return (surface, "pack", ())
                    else:
                        if attempts is not None:
                            attempts.append(DispatchAttempt(source="pack", outcome="fell_through", reason="no_gloss_aware_cause"))
                elif intent.tag is IntentTag.CAUSE:
                    if attempts is not None:
                        attempts.append(DispatchAttempt(source="pack", outcome="skipped", reason="gloss_aware_cause_disabled"))
                if self.config.transitive_surface:
                    # ADR-0083 — transitive supersedes composed.  At
                    # max_depth=1 this degrades byte-identically to the
                    # single-chain surface; at max_depth=2 byte-identical
                    # to ADR-0062 when no second hop exists.
                    surface = teaching_grounded_surface_transitive(
                        lemma,
                        intent.tag,
                        register=self.register_pack,
                        max_depth=self.config.transitive_max_depth,
                    )
                    reason_type = "transitive"
                elif self.config.composed_surface:
                    surface = teaching_grounded_surface_composed(
                        lemma, intent.tag, register=self.register_pack,
                    )
                    reason_type = "composed"
                else:
                    surface = teaching_grounded_surface(
                        lemma, intent.tag, register=self.register_pack,
                    )
                    reason_type = "standard"
                if surface is not None:
                    if attempts is not None:
                        attempts.append(DispatchAttempt(source="teaching", outcome="admitted", reason=f"teaching_{reason_type}_surface_found"))
                    return (surface, "teaching", ())
                else:
                    if attempts is not None:
                        attempts.append(DispatchAttempt(source="teaching", outcome="fell_through", reason=f"no_teaching_{reason_type}_surface"))
                from chat.cross_pack_grounding import cross_pack_grounded_surface
                surface = cross_pack_grounded_surface(
                    lemma, intent.tag, register=self.register_pack,
                )
                if surface is not None:
                    if attempts is not None:
                        attempts.append(DispatchAttempt(source="teaching", outcome="admitted", reason="cross_pack_grounded_surface_found"))
                    return (surface, "teaching", ())
                else:
                    if attempts is not None:
                        attempts.append(DispatchAttempt(source="teaching", outcome="fell_through", reason="no_cross_pack_grounded_surface"))
                # Deliberate non-fallback: when CAUSE / VERIFICATION
                # has no teaching chain or cross-pack chain rooted on
                # the subject, return None so the discovery layer logs
                # a "would_have_grounded" candidate identifying the
                # teaching-content gap.  Emitting the bare pack
                # disclosure here would mask that signal and give the
                # user a non-answer (a definition rather than a cause).
                # See ``tests/test_discovery_candidates``.
            else:
                if attempts is not None:
                    attempts.append(DispatchAttempt(source="pack", outcome="fell_through", reason="missing_subject"))
                    attempts.append(DispatchAttempt(source="teaching", outcome="fell_through", reason="missing_subject"))
        if intent.tag is IntentTag.CORRECTION:
            surface = pack_grounded_correction_surface(
                text, register=self.register_pack,
            )
            if surface is not None:
                if attempts is not None:
                    attempts.append(DispatchAttempt(source="pack", outcome="admitted", reason="pack_correction_surface_found"))
                return (surface, "pack", ())
            else:
                if attempts is not None:
                    attempts.append(DispatchAttempt(source="pack", outcome="fell_through", reason="no_pack_correction_surface"))
        if intent.tag is IntentTag.PROCEDURE:
            subject_text = (intent.subject or "").strip()
            if subject_text:
                surface = pack_grounded_procedure_surface(
                    subject_text, register=self.register_pack,
                )
                if surface is not None:
                    if attempts is not None:
                        attempts.append(DispatchAttempt(source="pack", outcome="admitted", reason="pack_procedure_surface_found"))
                    return (surface, "pack", ())
                else:
                    if attempts is not None:
                        attempts.append(DispatchAttempt(source="pack", outcome="fell_through", reason="no_pack_procedure_surface"))
            else:
                if attempts is not None:
                    attempts.append(DispatchAttempt(source="pack", outcome="fell_through", reason="missing_subject"))
        if intent.tag in (IntentTag.DEFINITION, IntentTag.RECALL):
            lemma = (intent.subject or "").strip()
            if not lemma:
                if attempts is not None:
                    attempts.append(DispatchAttempt(source="pack", outcome="fell_through", reason="missing_subject"))
                return None
            surface = pack_grounded_surface(
                lemma,
                register=self.register_pack,
                anchor_lens=self.anchor_lens,
            )
            if surface is not None:
                # ADR-0077 (R6) — expose the resolving lemma's
                # semantic_domains so the runtime's substantive-register
                # hook can fuel ``append_semantic_domain_clause``.  All
                # other composers return ``()`` because only the gloss
                # DEFINITION/RECALL path participates in convivial's
                # bounded propositional expansion in R6.
                from chat.pack_resolver import resolve_lemma
                resolved = resolve_lemma(lemma)
                domains = resolved[1] if resolved is not None else ()
                if attempts is not None:
                    attempts.append(DispatchAttempt(source="pack", outcome="admitted", reason="pack_grounded_surface_found"))
                return (surface, "pack", domains)
            else:
                if attempts is not None:
                    attempts.append(DispatchAttempt(source="pack", outcome="fell_through", reason="no_pack_grounded_surface"))
        if intent.tag is IntentTag.UNKNOWN:
            # ADR-0086 — UNKNOWN intent with pack-resident prompt
            # tokens.  The classifier could not assign a known dialogue
            # shape, but the prompt itself may contain lemmas that are
            # ratified in mounted lexicon packs (e.g. ``"light logos"``,
            # ``"spirit wisdom truth"``).  Surface those lemmas with
            # their semantic_domains rather than emit the bare
            # _UNKNOWN_DOMAIN_SURFACE disclosure.  Null-lift invariant:
            # when no prompt token resolves, composer returns None and
            # the caller falls through to the universal disclosure
            # byte-identically (preserves the ADR-0053 honesty contract
            # for fully-OOV prompts).
            surface = pack_grounded_unknown_surface(
                text, register=self.register_pack,
            )
            if surface is not None:
                if attempts is not None:
                    attempts.append(DispatchAttempt(source="pack", outcome="admitted", reason="pack_grounded_unknown_surface_found"))
                return (surface, "pack", ())
            else:
                if attempts is not None:
                    attempts.append(DispatchAttempt(source="pack", outcome="fell_through", reason="no_pack_grounded_unknown_surface"))

        # Check if any attempts have been recorded for pack/teaching/partial. If not, record them as skipped.
        if attempts is not None:
            has_pack = any(a.source == "pack" for a in attempts)
            has_teaching = any(a.source == "teaching" for a in attempts)
            has_partial = any(a.source == "partial" for a in attempts)
            if not has_pack:
                attempts.append(DispatchAttempt(source="pack", outcome="skipped", reason=f"intent_tag_{intent.tag.name.lower()}_not_targeted"))
            if not has_teaching:
                attempts.append(DispatchAttempt(source="teaching", outcome="skipped", reason=f"intent_tag_{intent.tag.name.lower()}_not_targeted"))
            if not has_partial:
                attempts.append(DispatchAttempt(source="partial", outcome="skipped", reason=f"intent_tag_{intent.tag.name.lower()}_not_targeted"))

        oov_lemma = (intent.subject or "").strip()
        if oov_lemma:
            from chat.oov_surface import oov_learning_invitation_surface
            oov_surface = oov_learning_invitation_surface(oov_lemma, intent.tag)
            if oov_surface is not None:
                if attempts is not None:
                    attempts.append(DispatchAttempt(source="oov", outcome="admitted", reason="oov_learning_invitation_surface_found"))
                return (oov_surface, "oov", ())
            else:
                if attempts is not None:
                    attempts.append(DispatchAttempt(source="oov", outcome="fell_through", reason="no_oov_learning_invitation_surface"))
        else:
            if attempts is not None:
                attempts.append(DispatchAttempt(source="oov", outcome="skipped", reason="missing_subject"))
        return None

    def _graph_atom_context(
        self,
        text: str,
        articulation: ArticulationPlan,
        *,
        region=None,
    ) -> tuple[tuple[str, ...], bool]:
        """Return ``(graph_atoms, graph_unconstrained)`` for observational telemetry."""
        if self.config.output_language != "en":
            return ((), True)
        graph = build_graph_from_input(text, articulation)
        graph_atoms = atoms_for_graph_nodes(graph)
        unconstrained = len(graph_atoms) == 0
        if region is not None:
            unconstrained = unconstrained or getattr(region, "allowed_indices", None) is None
        return (graph_atoms, unconstrained)

    def _composer_graph_atom_equivalence(
        self,
        *,
        grounding_source: str,
        composer_atoms: tuple[str, ...],
        graph_atoms: tuple[str, ...],
        graph_unconstrained: bool,
    ):
        applicable = grounding_source in {"pack", "teaching"}
        return compare_atom_sets(
            composer_atoms=composer_atoms,
            graph_atoms=graph_atoms,
            graph_unconstrained=graph_unconstrained,
            applicable=applicable,
        )

    def _maybe_apply_discourse_planner(
        self, text: str, source_tag: str
    ) -> tuple[str, str] | None:
        """Build and render a :class:`DiscoursePlan` for *text*.

        Returns ``(rendered_surface, new_source_tag)`` when the planner
        engages and produces more than one move, else ``None``.  Callers
        own assignment.  The returned ``new_source_tag`` is the source
        the planner actually used (``"teaching"`` when the plan
        contains any teaching fact, else ``"pack"``) so downstream
        labels reflect the surface's true provenance — particularly
        important when the planner engaged via the compound bypass
        (upstream tagged "oov" but rendered output is pack/teaching
        content).

        Gating discipline (must match both cold-start and warm hooks):

        * Returns ``None`` unless ``self.config.discourse_planner`` is True.
        * Returns ``None`` unless *source_tag* is one of ``pack`` or
          ``teaching``.  Vault / none / oov / empty paths are not
          replaced — the discovery-signal disclosure and the existing
          vault-grounded walk surfaces stay intact.
        * Returns ``None`` when the classified intent carries no
          subject (no head noun ⇒ no grounding bundle to plan over).
        * Returns ``None`` when the resulting plan has ≤ 1 move (BRIEF
          mode or empty bundle) — render in that case would just
          duplicate the existing single-sentence pack-grounded surface.
        * Returns ``None`` when the renderer produces an empty string.
        """

        # Phase 3 + 4 — reset plan-contemplation findings AND plan
        # metrics at the start of every call so they never leak across
        # turns; only successfully rendered plans (with contemplation
        # enabled) repopulate them.
        self._last_plan_findings = ()
        self._last_plan_metrics = None
        if not self.config.discourse_planner:
            return None
        from generate.discourse_planner import (
            GroundingBundle,
            plan_compound_discourse,
            plan_discourse,
            render_plan,
        )
        from generate.grounding_accessors import grounding_bundle_for
        from generate.intent import (
            classify_compound_intent,
            classify_response_mode,
        )
        from generate.intent_bridge import classify_intent_from_input

        compound = classify_compound_intent(text)
        mode = classify_response_mode(text)
        # Compound prompts implicitly request more depth than BRIEF
        # can express — a multi-part compound in BRIEF mode produces
        # one ANCHOR per part, which on shared-subject compounds
        # ("What is X, and why does it matter?") would emit duplicate
        # anchor sentences.  Upgrade to EXPLAIN so each sub-plan has
        # ANCHOR+SUPPORT+RELATION budget and the parts differentiate.
        from generate.intent import ResponseMode as _ResponseMode
        if compound.is_compound() and mode is _ResponseMode.BRIEF:
            mode = _ResponseMode.EXPLAIN

        # Fast path: BRIEF mode on a non-compound prompt can never
        # emit > 1 move (``_MODE_BUDGETS[BRIEF] = (1, 1)``).  The
        # downstream ``len(plan.moves) <= 1`` gate would always
        # reject — so short-circuit here, BEFORE the expensive
        # ``grounding_bundle_for`` query and ``plan_discourse``
        # selector logic.  This is the load-bearing perf win for
        # ``discourse_planner=True`` as the runtime default; without
        # it every single-fact prompt pays for a multi-source bundle
        # build it can't possibly use.  Confirmed empirically:
        # ``tests/test_cognition_eval_register_matrix.py`` runtime
        # collapsed from ~14 minutes to seconds after this gate
        # landed.
        if mode is _ResponseMode.BRIEF and not compound.is_compound():
            return None

        # Standard gate: when upstream grounded the surface in pack or
        # teaching, the planner is free to engage.
        standard_gate = source_tag in {"pack", "teaching"}
        # Compound bypass: when upstream produced an OOV / none surface
        # because the flat classifier saw a polluted subject (e.g.
        # ``"truth, and why does it matter"``), but the compound
        # decomposition reveals at least one pack-resident primary
        # part, the substrate exists — the planner engages on the
        # decomposed parts rather than the polluted flat surface.
        compound_bypass = False
        if not standard_gate and compound.is_compound():
            primary = compound.primary
            if primary.subject:
                probe = grounding_bundle_for(primary.subject)
                if not probe.is_empty():
                    compound_bypass = True
        if not standard_gate and not compound_bypass:
            return None

        if compound.is_compound():
            bundles = tuple(
                grounding_bundle_for(part.subject)
                if part.subject
                else GroundingBundle()
                for part in compound.parts
            )
            plan = plan_compound_discourse(compound, mode, bundles)
        else:
            # Use the intent_bridge classifier on single-part prompts to
            # preserve the pre-compound behavior exactly.
            intent = classify_intent_from_input(text)
            if not intent.subject:
                return None
            bundle = grounding_bundle_for(intent.subject)
            plan = plan_discourse(intent, mode, bundle)
        if len(plan.moves) <= 1:
            return None
        # Phase 3 + 4 — plan-level contemplation pre-flight + metrics.
        # Read-only, SPECULATIVE-only on the findings side; pure
        # measurements on the metrics side.  Stores both on the
        # runtime for offline miner consumption.  Does not mutate the
        # plan or block rendering — emits side observations only.
        if self.config.discourse_contemplation:
            from core.contemplation.plan_metrics import compute_plan_metrics
            from core.contemplation.plan_preflight import contemplate_plan
            self._last_plan_findings = contemplate_plan(plan)
            self._last_plan_metrics = compute_plan_metrics(plan)
        else:
            self._last_plan_findings = ()
            self._last_plan_metrics = None
        # Phase 2 — reflective rendering pronominalizes the focus
        # subject across consecutive same-subject moves, eliminating
        # the mechanical "Truth ... Truth ... Truth ..." cascade the
        # Phase 1 flat renderer produced.  Deterministic, replayable,
        # adds no new content — purely a rendering improvement.
        rendered = render_plan(plan, reflective=True)
        if not rendered:
            return None
        from generate.discourse_planner import FactSource
        plan_uses_teaching = any(
            m.fact is not None and m.fact.source is FactSource.TEACHING
            for m in plan.moves
        )
        new_source = "teaching" if plan_uses_teaching else "pack"
        # Phase 5 — emit one articulation observation per engaged turn.
        # Gated by both ``discourse_contemplation`` (so metrics +
        # findings exist to package) AND the presence of an attached
        # sink (so the runtime does no JSON work when nobody is
        # listening).  Sink errors are NOT swallowed — same fail-fast
        # contract as the telemetry sink.
        if (
            self._articulation_sink is not None
            and self.config.discourse_contemplation
            and self._last_plan_metrics is not None
        ):
            from chat.articulation_telemetry import (
                ArticulationObservation,
                format_articulation_observation_jsonl,
                prompt_hash,
            )
            anchor = plan.anchor()
            anchor_subject = (
                anchor.fact.subject
                if anchor is not None and anchor.fact is not None
                else (plan.intent.subject or "")
            )
            import hashlib as _hashlib
            plan_substrate_hash = _hashlib.sha256(
                plan.to_json().encode("utf-8")
            ).hexdigest()[:16]
            observation = ArticulationObservation(
                turn_id=self._articulation_turn_counter,
                anchor_subject=anchor_subject,
                prompt_hash=prompt_hash(text),
                plan_substrate_hash=plan_substrate_hash,
                metrics=self._last_plan_metrics.as_dict(),
                findings=tuple(
                    {
                        "kind": f.kind.value,
                        "subject": f.subject,
                        "predicate": f.predicate,
                        "object": f.object,
                    }
                    for f in self._last_plan_findings
                ),
            )
            self._articulation_sink.emit(
                format_articulation_observation_jsonl(observation)
            )
            self._articulation_turn_counter += 1
        return rendered, new_source

    def _stub_response(
        self,
        field_state: FieldState,
        *,
        tokens: tuple[str, ...] = (),
        pack_grounded_surface: str | None = None,
        grounded_source_tag: str = "pack",
        pack_semantic_domains: tuple[str, ...] = (),
        graph_atoms: tuple[str, ...] = (),
        graph_unconstrained: bool = True,
        discovery_intent_tag: Any = None,
        discovery_intent_subject: str | None = None,
        dispatch_trace: DispatchTrace | None = None,
    ) -> ChatResponse:
        zero = np.zeros(field_state.F.shape, dtype=np.float32)
        prop = Proposition(
            subject="",
            predicate="",
            object_=None,
            surface=_UNKNOWN_DOMAIN_SURFACE,
            frame_id="unknown_domain",
            subject_versor=zero,
            predicate_versor=zero,
            object_versor=None,
            relation=zero,
        )
        art = ArticulationPlan(
            subject="",
            predicate="",
            object=None,
            surface=_UNKNOWN_DOMAIN_SURFACE,
            output_language=self.config.output_language,
            frame_id="unknown_domain",
        )
        safety_ctx = SafetyContext(
            field_state=_FieldStateWithVersor(
                versor_condition=float(versor_condition(field_state.F)),
            ),
            last_refusal_was_typed=self._last_refusal_was_typed,
            identity_manifold_hash_before=self._identity_manifold_hash,
            identity_manifold_hash_after=_hash_identity_manifold(self.identity_manifold),
        )
        safety_verdict = self.safety_check.check(safety_ctx, self.safety_pack)
        ethics_ctx = EthicsContext(
            alignment_score=0.0,
            hedge_threshold_soft=float(
                self.identity_manifold.surface_preferences.hedge_threshold_soft
            ),
            hedge_emitted=False,
            grounded_in_evidence=False,
            disclosure_emitted=True,
        )
        ethics_verdict = self.ethics_check.check(ethics_ctx, self.ethics_pack)
        refusal_surface = build_refusal_surface(
            safety_verdict, ethics_verdict, self.ethics_pack,
        )
        refusal_emitted = refusal_surface is not None
        if refusal_emitted:
            response_surface = refusal_surface
            self._last_refusal_was_typed = True
        elif pack_grounded_surface is not None:
            response_surface = pack_grounded_surface
            if (
                self.config.thread_anaphora
                and grounded_source_tag in {"pack", "teaching"}
                and discovery_intent_subject
                and discovery_intent_tag is not None
            ):
                from chat.anaphora import thread_anaphora_prefix
                prefix = thread_anaphora_prefix(
                    self.thread_context,
                    discovery_intent_subject,
                    discovery_intent_tag.name.lower(),
                    grounded_source_tag,
                )
                if prefix is not None:
                    response_surface = prefix + response_surface
        else:
            response_surface = _UNKNOWN_DOMAIN_SURFACE
        if pack_grounded_surface is not None and not refusal_emitted:
            grounding_source = grounded_source_tag
        else:
            grounding_source = "none"
        # ADR-0075 (C1) — realizer slot-type guard.  Runs BEFORE
        # register decoration so a register cannot accidentally heal
        # an illegal articulation by wrapping it, and BEFORE anchor-
        # lens annotation extraction so the lens annotation never
        # rides on a guard-rejected surface.  On rejection, route to
        # the bounded disclosure string and force grounding_source to
        # ``"none"`` (an illegal surface is ungrounded by construction).
        # The pre-guard candidate is preserved on walk_surface_stub
        # for telemetry — the stub path normally leaves walk_surface as
        # _UNKNOWN_DOMAIN_SURFACE, so this swap strictly increases
        # observability under rejection.
        guard_verdict_stub = _check_realizer_surface(
            response_surface,
            pos_lookup=self._pos_by_surface.get,
        )
        realizer_guard_status_stub = guard_verdict_stub.status
        realizer_guard_rule_stub = guard_verdict_stub.rule_id
        walk_surface_stub = _UNKNOWN_DOMAIN_SURFACE
        if guard_verdict_stub.status == "rejected":
            walk_surface_stub = response_surface
            response_surface = _GUARD_DISCLOSURE_SURFACE
            grounding_source = "none"
        # ADR-0077 (R6) — register layering separation.
        # ``register_canonical_surface`` is the composer / guard output
        # BEFORE any register transformation; the pipeline hashes this
        # field for ``trace_hash`` so substantive register transforms
        # cannot move the truth-path identity.  Substantive transforms
        # are skipped on ``grounding_source == "none"`` so the bounded
        # disclosure stays sacrosanct under terse_v1's drop_articles.
        register_canonical_surface_stub = response_surface
        if grounding_source == "none":
            substantive_surface_stub = response_surface
        else:
            substantive_surface_stub = apply_substantive_register(
                response_surface,
                self.register_pack,
                semantic_domains=pack_semantic_domains,
            )
        response_surface = substantive_surface_stub
        # ADR-0071 (R4) — apply seeded discourse-marker decoration to
        # the realized surface AFTER substantive register transforms.
        # Empty marker buckets ⇒ no-op (UNREGISTERED / neutral / terse).
        # Preserve the pre-decoration string so the pipeline can hash
        # the truth-path surface and trace_hash stays invariant under
        # register (ADR-0069 invariant C, strengthened by ADR-0077).
        pre_decoration_surface_stub = response_surface
        decoration_stub = decorate_surface(
            response_surface,
            self.register_pack,
            turn_idx=len(self.turn_log),
        )
        response_surface = decoration_stub.surface
        register_id_stub = (
            "" if self.register_pack.is_unregistered()
            else self.register_pack.register_id
        )
        # ADR-0073d — anchor-lens telemetry.  ``id`` reflects the loaded
        # pack (empty for UNANCHORED); ``mode_label`` reflects the
        # engaged label this turn (empty when the lens didn't fire on
        # this turn's lemma).  Mode is extracted from the pre-decoration
        # surface so register decoration cannot interfere.
        anchor_lens_id_stub = (
            "" if self.anchor_lens.is_unanchored()
            else self.anchor_lens.lens_id
        )
        anchor_lens_mode_label_stub = _extract_anchor_lens_mode_label(
            pre_decoration_surface_stub, anchor_lens_id_stub,
        )
        atom_equivalence_stub = self._composer_graph_atom_equivalence(
            grounding_source=grounding_source,
            composer_atoms=pack_semantic_domains,
            graph_atoms=graph_atoms,
            graph_unconstrained=graph_unconstrained,
        )
        verdicts_bundle = TurnVerdicts(
            identity_score=None,
            safety_verdict=safety_verdict,
            ethics_verdict=ethics_verdict,
            refusal_emitted=refusal_emitted,
            hedge_injected=False,
        )
        stub_epistemic_state = epistemic_state_for_grounding_source(grounding_source).value
        stub_normative_clearance = clearance_from_verdicts(verdicts_bundle).value
        stub_normative_detail = normative_detail_from_verdicts(verdicts_bundle)
        if tokens:
            stub_event = TurnEvent(
                turn=max(self._context.turn - 1, 0),
                input_tokens=tokens,
                surface=response_surface,
                walk_surface=walk_surface_stub,
                articulation_surface=_UNKNOWN_DOMAIN_SURFACE,
                dialogue_role="assert",
                identity_score=None,
                cycle_cost_total=0.0,
                vault_hits=0,
                versor_condition=float(versor_condition(field_state.F)),
                flagged=False,
                elaboration=None,
                safety_verdict=safety_verdict,
                ethics_verdict=ethics_verdict,
                verdicts=verdicts_bundle,
                grounding_source=grounding_source,
                register_id=register_id_stub,
                register_variant_id=decoration_stub.variant_id,
                anchor_lens_id=anchor_lens_id_stub,
                anchor_lens_mode_label=anchor_lens_mode_label_stub,
                realizer_guard_status=realizer_guard_status_stub,
                realizer_guard_rule=realizer_guard_rule_stub,
                register_canonical_surface=register_canonical_surface_stub,
                composer_graph_atom_status=atom_equivalence_stub.status,
                composer_atom_set_hash=atom_equivalence_stub.composer_atom_set_hash,
                graph_atom_set_hash=atom_equivalence_stub.graph_atom_set_hash,
                composer_graph_atom_overlap_count=atom_equivalence_stub.overlap_count,
                epistemic_state=stub_epistemic_state,
                normative_clearance=stub_normative_clearance,
                normative_detail=stub_normative_detail,
            )
            self.turn_log.append(stub_event)
            self._emit_turn_event(stub_event)
            if discovery_intent_tag is not None:
                self._emit_discovery_candidates(
                    turn_event=stub_event,
                    intent_tag=discovery_intent_tag,
                    intent_subject=discovery_intent_subject,
                    grounding_source=grounding_source,
                )
                if grounding_source == "oov":
                    self._emit_oov_candidate(
                        turn_event=stub_event,
                        intent_tag=discovery_intent_tag,
                        token=discovery_intent_subject,
                    )
            self._push_thread_summary(
                turn_event=stub_event,
                intent_tag=discovery_intent_tag,
                intent_subject=discovery_intent_subject,
                grounding_source=grounding_source,
                surface=response_surface,
            )
        return ChatResponse(
            surface=response_surface,
            proposition=prop,
            articulation=art,
            articulation_surface=_UNKNOWN_DOMAIN_SURFACE,
            dialogue_role="assert",
            versor_condition=versor_condition(field_state.F),
            output_language=self.config.output_language,
            frame_pack=self.config.frame_pack,
            walk_surface=walk_surface_stub,
            salience_top_k=None,
            candidates_used=None,
            vault_hits=0,
            identity_score=None,
            character_profile=self.character_profile,
            flagged=False,
            safety_verdict=safety_verdict,
            ethics_verdict=ethics_verdict,
            verdicts=verdicts_bundle,
            grounding_source=grounding_source,
            pre_decoration_surface=pre_decoration_surface_stub,
            register_id=register_id_stub,
            register_variant_id=decoration_stub.variant_id,
            anchor_lens_id=anchor_lens_id_stub,
            anchor_lens_mode_label=anchor_lens_mode_label_stub,
            realizer_guard_status=realizer_guard_status_stub,
            realizer_guard_rule=realizer_guard_rule_stub,
            register_canonical_surface=register_canonical_surface_stub,
            composer_graph_atom_status=atom_equivalence_stub.status,
            composer_atom_set_hash=atom_equivalence_stub.composer_atom_set_hash,
            graph_atom_set_hash=atom_equivalence_stub.graph_atom_set_hash,
            composer_graph_atom_overlap_count=atom_equivalence_stub.overlap_count,
            epistemic_state=stub_epistemic_state,
            normative_clearance=stub_normative_clearance,
            normative_detail=stub_normative_detail,
            refusal_reason=refusal_surface if refusal_emitted else "",
            dispatch_trace=dispatch_trace,
        )

    def chat(self, text: str, max_tokens: int | None = None) -> ChatResponse:
        self._last_input_text = text  # W-013: store for explain_last_turn()
        tokens = self._tokenize(text)
        filtered = self._apply_oov_policy(tokens)
        if not filtered:
            raise ValueError("ChatRuntime.chat() received no in-vocabulary tokens.")

        # ADR-0090 — unified-ingest path is flag-gated.  Default (False)
        # preserves the historical probe-then-commit behavior; True
        # commits first so the gate and the walk see the same field.
        # ``committed`` is materialized eagerly on the unified path and
        # lazily on the stub path of the historical flow; the explicit
        # ``FieldState | None`` declaration documents that and silences
        # Pyright's reportPossiblyUnbound across the conditional flow.
        committed: FieldState | None = None
        if self.config.unified_ingest:
            committed = self._context.commit_ingest(filtered)
            committed = self._apply_drive_bias(committed)
            gate_query = committed.F
        else:
            probe_state = self._context.probe_ingest(filtered)
            gate_query = probe_state.F

        direct_hits = self._context.vault.recall(gate_query, top_k=3)
        direct_recall_energy_class = _recall_energy_class_from_hits(direct_hits)
        direct_best = max((h["score"] for h in direct_hits), default=0.0)
        gate_decision = default_gate.check(
            direct_best,
            vault=self._context.vault,
            query=gate_query,
            decomposer=default_decomposer,
        )
        if gate_decision.fire:
            if not self.config.unified_ingest:
                committed = self._context.commit_ingest(filtered)
            assert committed is not None  # set above on both flag paths
            empty_result = GenerationResult(tokens=(), final_state=committed, vault_hits=0)
            attempts: list[DispatchAttempt] = []
            pack_result = self._maybe_pack_grounded_surface(
                text, gate_decision.source, attempts=attempts
            )
            if pack_result is None:
                pack_surface = None
                pack_source_tag = "none"
                pack_semantic_domains: tuple[str, ...] = ()
                final_attempts = []
                final_attempts.append(DispatchAttempt(source="pack", outcome="fell_through", reason="no_pack_resident_lemmas"))
                final_attempts.append(DispatchAttempt(source="teaching", outcome="fell_through", reason="no_teaching_chains"))
                final_attempts.append(DispatchAttempt(source="partial", outcome="fell_through", reason="no_partial_match"))
                final_attempts.append(DispatchAttempt(source="oov", outcome="fell_through", reason="no_oov_learning_invitation"))
                final_attempts.append(DispatchAttempt(source="universal_disclosure", outcome="admitted", reason="fallback_to_universal_disclosure"))
                attempts = final_attempts
                selected_source = "universal_disclosure"
            else:
                pack_surface, pack_source_tag, pack_semantic_domains = pack_result
                planned = self._maybe_apply_discourse_planner(
                    text, pack_source_tag
                )
                if planned is not None:
                    pack_surface, pack_source_tag = planned
                    # ADR-0077 — planner-rendered surfaces are outside
                    # the gloss DEFINITION/RECALL convivial-expansion
                    # path; drop the carried semantic_domains so the
                    # ``append_semantic_domain_clause`` knob is a no-op
                    # over planner output.
                    pack_semantic_domains = ()
                selected_source = pack_source_tag
            dispatch_trace = DispatchTrace(attempts=tuple(attempts), selected=selected_source)
            self._context.finalize_turn(
                empty_result,
                tokens_in=tuple(filtered),
                input_versor=committed.F,
                dialogue_role="assert",
                metadata={
                    "unknown": True,
                    "unknown_source": gate_decision.source,
                    "grounding_source": pack_source_tag if pack_surface else "none",
                },
            )
            # ADR-0148 — post-finalize promotion scan (flag-gated, null-drop when False).
            if self.config.vault_promotion_enabled:
                self._context.vault.promote_eligible_entries(VaultPromotionPolicy())
            discovery_intent_tag = None
            discovery_intent_subject: str | None = None
            stub_graph_atoms: tuple[str, ...] = ()
            stub_graph_unconstrained = True
            if (
                gate_decision.source == "empty_vault"
                and self.config.output_language == "en"
            ):
                from generate.intent_bridge import classify_intent_from_input
                _intent = classify_intent_from_input(text)
                self._last_intent = _intent  # W-013
                discovery_intent_tag = _intent.tag
                discovery_intent_subject = _intent.subject
                stub_articulation = ArticulationPlan(
                    subject=_intent.subject or "",
                    predicate="",
                    object=None,
                    surface="",
                    output_language=self.config.output_language,
                    frame_id="unknown_domain",
                )
                stub_graph_atoms, stub_graph_unconstrained = self._graph_atom_context(
                    text,
                    stub_articulation,
                )
            return self._checkpointed_response(
                self._stub_response(
                    committed,
                    tokens=tuple(filtered),
                    pack_grounded_surface=pack_surface,
                    grounded_source_tag=pack_source_tag,
                    pack_semantic_domains=pack_semantic_domains,
                    graph_atoms=stub_graph_atoms,
                    graph_unconstrained=stub_graph_unconstrained,
                    discovery_intent_tag=discovery_intent_tag,
                    discovery_intent_subject=discovery_intent_subject,
                    dispatch_trace=dispatch_trace,
                )
            )

        if self.config.unified_ingest:
            # ADR-0090 — commit + drive bias already ran before the gate
            # check; reuse the same field the gate decided against so the
            # walk navigates the manifold position the gate ratified.
            assert committed is not None  # set in the unified-ingest branch above
            field_state = committed
        else:
            field_state = self._context.commit_ingest(filtered)
            field_state = self._apply_drive_bias(field_state)
        reference_blade = self._dialogue_reference()
        base_proposition = propose(
            field_state,
            None,
            self._context.vocab,
            self._frame_registry,
            output_lang=self.config.output_language,
        )
        dialogue_role = _stable_dialogue_role(
            classify_dialogue_blade(base_proposition.relation, reference_blade),
            raw_text=text,
            tokens=tokens,
        )
        proposition = propose_dialogue(
            field_state,
            self._context.vault,
            self._context.vocab,
            self._frame_registry,
            reference_blade,
            output_lang=self.config.output_language,
        )
        articulation = realize(proposition, self._context.vocab, output_language=self.config.output_language)
        articulation = _prefer_prompt_anchor(articulation, filtered, output_language=self.config.output_language)
        self._context.record_dialogue(proposition)

        forward_region = None
        graph_atoms_main: tuple[str, ...] = ()
        graph_unconstrained_main = True
        if self.config.output_language == "en":
            pre_gen_graph = build_graph_from_input(text, articulation)
            graph_atoms_main = atoms_for_graph_nodes(pre_gen_graph)
            if self.config.forward_graph_constraint:
                forward_region = build_graph_constraint(pre_gen_graph, self._context.vocab)
            graph_unconstrained_main = (
                len(graph_atoms_main) == 0
                or (
                    forward_region is not None
                    and getattr(forward_region, "allowed_indices", None) is None
                )
            )

        # W-012 — catch InnerLoopExhaustion so the caller receives a
        # typed refusal ChatResponse instead of an unhandled exception.
        from generate.exhaustion import InnerLoopExhaustion as _ILE
        try:
            result = generate(
                field_state,
                self._context.vocab,
                self._context.persona,
                max_tokens=self.config.max_tokens if max_tokens is None else max_tokens,
                record_trajectory=True,
                vault=self._context.vault,
                recall_top_k=3 if self.config.allow_cross_language_recall else 0,
                output_lang=self.config.output_language,
                allow_cross_language_generation=self.config.allow_cross_language_generation,
                use_salience=self.config.use_salience,
                salience_top_k=self.config.salience_top_k,
                inhibition_threshold=self.config.inhibition_threshold,
                region=forward_region,
                inner_loop_admissibility=self.config.inner_loop_admissibility,
                admissibility_threshold=self.config.admissibility_threshold,
                admissibility_mode=self.config.admissibility_mode,
                admissibility_margin=self.config.admissibility_margin,
                stop_tokens=(
                    frozenset(self.config.stop_tokens)
                    if self.config.stop_tokens is not None
                    else None
                ),
            )
        except _ILE as _exhaustion_exc:
            self._context.finalize_turn(
                GenerationResult(tokens=(), final_state=field_state, vault_hits=0),
                tokens_in=tuple(filtered),
                input_versor=field_state.F,
                dialogue_role="assert",
                metadata={"exhaustion": True, "refusal_reason": _exhaustion_exc.reason.value},
            )
            stub = self._stub_response(
                field_state,
                tokens=tuple(filtered),
            )
            return self._checkpointed_response(
                replace(stub, refusal_reason=_exhaustion_exc.reason.value)
            )

        # --- Articulation fidelity: replace bare S-P-O join with intent-aware surface ---
        # Phase 2: pass proposition so the bridge grounds <pending> obj slots
        # from pack-resolved proposition slots (primary) rather than walk
        # tokens (supplemental backfill only).  walk_tokens still participates
        # as a fallback when proposition.object_ is None/empty.
        # ADR-0088 Phase B (audit Finding 2, 2026-05-20) — compute
        # walk_tokens unconditionally so non-English packs can also
        # surface them via ``ChatResponse.recalled_words`` for the
        # pipeline's opt-in ``ground_graph`` step.  English keeps
        # using them for ``articulate_with_intent`` grounding as
        # before.
        walk_tokens = tuple(
            tok for tok in (result.tokens or ()) if tok and tok.isalpha()
        )
        if self.config.output_language == "en":
            intent_surface = articulate_with_intent(
                text,
                articulation,
                walk_tokens,
                proposition=proposition,
            )
            if intent_surface:
                articulation = replace(articulation, surface=intent_surface)
        # --- end articulation fidelity ---

        reasoning_trajectory = _make_trajectory_from_result(result, self._context.turn)
        identity_score = self._identity_check.check(reasoning_trajectory, self.identity_manifold)
        flagged = identity_score.flagged
        cycle_cost = CycleCost(
            cycle_index=self._context.turn,
            attention_cost=float(result.candidates_used or 0),
            inhibition_cost=float(self.config.inhibition_threshold),
            digest_cost=0.0,
            trajectory_cost=float(len(result.trajectory or ())),
        )
        self.exertion_meter.record(cycle_cost)
        fatigue = self.exertion_meter.fatigue(at_cycle=self._context.turn)
        self.character_profile = CharacterProfile.from_manifold(
            self.identity_manifold,
            drive_summaries={g.axis.name: g.magnitude * (1.0 - fatigue.value) for g in self.drive_gradients},
            fatigue_index=fatigue.value,
        )

        self._context.finalize_turn(
            result,
            tokens_in=tuple(filtered),
            dialogue_role=str(dialogue_role),
        )
        # ADR-0148 — post-finalize promotion scan (flag-gated, null-drop when False).
        if self.config.vault_promotion_enabled:
            self._context.vault.promote_eligible_entries(VaultPromotionPolicy())
        current_valence = _energy_scalar(getattr(result.final_state, "valence", None))
        surface_ctx = self._build_surface_context(identity_score, current_valence)
        self._last_valence = current_valence
        surface = _terminate_surface(articulation.surface, role=dialogue_role, output_language=self.config.output_language)
        articulation = replace(articulation, surface=surface)
        sentence_plan: SentencePlan = SentenceAssembler().assemble(
            articulation,
            result.tokens,
            role=dialogue_role,
            context=surface_ctx,
        )
        walk_surface = sentence_plan.surface
        vault_hits = int(result.vault_hits)
        is_grounded = walk_surface != _UNKNOWN_DOMAIN_SURFACE
        hedge_emitted = _surface_contains_hedge(walk_surface, self.identity_manifold)
        safety_ctx = SafetyContext(
            field_state=_FieldStateWithVersor(
                versor_condition=float(versor_condition(result.final_state.F)),
            ),
            last_refusal_was_typed=self._last_refusal_was_typed,
            identity_manifold_hash_before=self._identity_manifold_hash,
            identity_manifold_hash_after=_hash_identity_manifold(self.identity_manifold),
        )
        safety_verdict = self.safety_check.check(safety_ctx, self.safety_pack)
        ethics_ctx = EthicsContext(
            alignment_score=float(getattr(identity_score, "alignment", 0.0)),
            hedge_threshold_soft=float(
                self.identity_manifold.surface_preferences.hedge_threshold_soft
            ),
            hedge_emitted=hedge_emitted,
            grounded_in_evidence=is_grounded,
            disclosure_emitted=not is_grounded,
        )
        ethics_verdict = self.ethics_check.check(ethics_ctx, self.ethics_pack)
        refusal_surface = build_refusal_surface(
            safety_verdict, ethics_verdict, self.ethics_pack,
        )
        refusal_emitted = refusal_surface is not None
        hedge_injected = False
        warm_grounding_source: str | None = None
        warm_pack_subject: str | None = None
        warm_pack_intent_tag: Any = None
        warm_pack_semantic_domains: tuple[str, ...] = ()
        attempts: list[DispatchAttempt] = []
        selected_source = "vault"
        if refusal_emitted:
            response_surface = refusal_surface
            self._last_refusal_was_typed = True
            for src in ("pack", "teaching", "partial", "oov", "universal_disclosure"):
                attempts.append(DispatchAttempt(source=src, outcome="skipped", reason="refusal_emitted"))
            selected_source = "none"
        else:
            response_surface = walk_surface
            warm_pack_result = self._maybe_pack_grounded_surface(
                text, "warm", allow_warm=True, attempts=attempts
            )
            if warm_pack_result is None:
                from generate.intent import IntentTag
                from generate.intent_bridge import classify_intent_from_input
                _wintent = classify_intent_from_input(text)
                # Discovery-signal preservation on warm path: when CAUSE /
                # VERIFICATION lacks both a teaching chain and a cross-pack
                # chain, the cold path emits the unknown-domain disclosure.
                # The warm path must match — fabricating a vault-grounded
                # walk fragment ("Work infer.") would mask the very gap
                # the discovery layer is meant to surface.
                final_attempts = []
                final_attempts.append(DispatchAttempt(source="pack", outcome="fell_through", reason="no_pack_resident_lemmas"))
                final_attempts.append(DispatchAttempt(source="teaching", outcome="fell_through", reason="no_teaching_chains"))
                final_attempts.append(DispatchAttempt(source="partial", outcome="fell_through", reason="no_partial_match"))
                final_attempts.append(DispatchAttempt(source="oov", outcome="fell_through", reason="no_oov_learning_invitation"))
                if _wintent.tag in (IntentTag.CAUSE, IntentTag.VERIFICATION):
                    response_surface = _UNKNOWN_DOMAIN_SURFACE
                    articulation = replace(articulation, surface=_UNKNOWN_DOMAIN_SURFACE)
                    warm_grounding_source = "none"
                    final_attempts.append(DispatchAttempt(source="universal_disclosure", outcome="admitted", reason="cause_verification_warm_fallback"))
                    selected_source = "universal_disclosure"
                else:
                    final_attempts.append(DispatchAttempt(source="universal_disclosure", outcome="skipped", reason="used_walk_surface"))
                    selected_source = "vault"
                attempts = final_attempts
            elif warm_pack_result is not None:
                warm_pack_surface, warm_grounding_source, warm_pack_semantic_domains = warm_pack_result
                if self.config.thread_anaphora and warm_grounding_source in {"pack", "teaching"}:
                    from chat.anaphora import thread_anaphora_prefix
                    from generate.intent_bridge import classify_intent_from_input
                    _wintent = classify_intent_from_input(text)
                    warm_pack_intent_tag = _wintent.tag
                    warm_pack_subject = _wintent.subject
                    if warm_pack_subject and warm_pack_intent_tag is not None:
                        prefix = thread_anaphora_prefix(
                            self.thread_context,
                            warm_pack_subject,
                            warm_pack_intent_tag.name.lower(),
                            warm_grounding_source,
                        )
                        if prefix is not None:
                            warm_pack_surface = prefix + warm_pack_surface
                response_surface = warm_pack_surface
                articulation = replace(articulation, surface=warm_pack_surface)
                # Step 5 — discourse planner.  Opt-in; engages only on
                # pack/teaching-grounded turns where the response mode
                # asks for more than a single-sentence brief.  When the
                # planner returns a multi-move plan, replace the warm
                # surface with the deterministic multi-clause rendering.
                # BRIEF mode always collapses to a single ANCHOR move so
                # the flag-off path stays byte-identical to the existing
                # composer.
                planned = self._maybe_apply_discourse_planner(
                    text, warm_grounding_source or ""
                )
                if planned is not None:
                    planned_surface, planned_source = planned
                    response_surface = planned_surface
                    articulation = replace(articulation, surface=planned_surface)
                    warm_grounding_source = planned_source
                    # ADR-0077 — planner-rendered surfaces are outside
                    # the gloss DEFINITION/RECALL convivial-expansion
                    # path; drop the carried semantic_domains so the
                    # ``append_semantic_domain_clause`` knob is a no-op
                    # over planner output.
                    warm_pack_semantic_domains = ()
                selected_source = warm_grounding_source
            if should_inject_hedge(ethics_verdict, self.ethics_pack):
                hedge_prefix = build_hedge_prefix(self.identity_manifold)
                before = response_surface
                response_surface = inject_hedge(response_surface, hedge_prefix)
                hedge_injected = response_surface != before
        dispatch_trace = DispatchTrace(attempts=tuple(attempts), selected=selected_source)
        # ADR-0075 (C1) — realizer slot-type guard (main path).  Runs
        # AFTER all composer / planner / hedge transformations and
        # BEFORE register decoration so a single seam covers every
        # articulation path.  On rejection: surface is replaced with
        # the bounded disclosure string, grounding_source forced to
        # ``"none"``, and walk_surface preserves the rejected
        # candidate so the manifold-walk evidence is overwritten only
        # in the rejection branch (the contract says illegal
        # articulation evidence is the relevant telemetry).
        guard_verdict_main = _check_realizer_surface(
            response_surface,
            pos_lookup=self._pos_by_surface.get,
        )
        realizer_guard_status_main = guard_verdict_main.status
        realizer_guard_rule_main = guard_verdict_main.rule_id
        if guard_verdict_main.status == "rejected":
            walk_surface = response_surface
            response_surface = _GUARD_DISCLOSURE_SURFACE
            warm_grounding_source = "none"
        main_grounding_source = warm_grounding_source or "vault"
        recall_energy_class_main = (
            direct_recall_energy_class
            if main_grounding_source == "vault"
            else None
        )
        if recall_energy_class_main:
            from generate.realizer import energy_modulated_surface

            try:
                ec = EnergyClass(recall_energy_class_main)
            except ValueError:
                pass
            else:
                response_surface = energy_modulated_surface(response_surface, ec)
                articulation = replace(articulation, surface=response_surface)
        # ADR-0077 (R6) — register layering separation (main path).  See
        # the stub-path equivalent for full semantics: the canonical
        # surface is captured pre-substantive so the cognition pipeline
        # can hash it for ``trace_hash``, preserving register
        # invariance under R6's stronger consumer set.  Substantive
        # transforms are skipped on ungrounded turns so the bounded
        # disclosure stays sacrosanct under terse's drop_articles.
        register_canonical_surface_main = response_surface
        if main_grounding_source == "none":
            substantive_surface_main = response_surface
        else:
            substantive_surface_main = apply_substantive_register(
                response_surface,
                self.register_pack,
                semantic_domains=warm_pack_semantic_domains,
            )
        response_surface = substantive_surface_main
        # ADR-0071 (R4) — seeded discourse-marker decoration runs AFTER
        # substantive register transforms and is the last step before
        # TurnEvent is sealed.  Applies uniformly to every grounding
        # path (vault / pack / teaching / planner / hedge-prefixed).
        # No-op for registers with empty marker buckets (UNREGISTERED /
        # default_neutral_v1 / terse_v1).  Pre-decoration surface is
        # preserved separately so the cognition pipeline can hash the
        # truth-path surface and trace_hash stays invariant under
        # register (ADR-0069 inv C, strengthened by ADR-0077).
        pre_decoration_surface_main = response_surface
        decoration_main = decorate_surface(
            response_surface,
            self.register_pack,
            turn_idx=len(self.turn_log),
        )
        response_surface = decoration_main.surface
        register_id_main = (
            "" if self.register_pack.is_unregistered()
            else self.register_pack.register_id
        )
        # ADR-0073d — anchor-lens telemetry (main path).  See stub-path
        # comment above for semantics.
        anchor_lens_id_main = (
            "" if self.anchor_lens.is_unanchored()
            else self.anchor_lens.lens_id
        )
        anchor_lens_mode_label_main = _extract_anchor_lens_mode_label(
            pre_decoration_surface_main, anchor_lens_id_main,
        )
        atom_equivalence_main = self._composer_graph_atom_equivalence(
            grounding_source=main_grounding_source,
            composer_atoms=warm_pack_semantic_domains,
            graph_atoms=graph_atoms_main,
            graph_unconstrained=graph_unconstrained_main,
        )
        verdicts_bundle = TurnVerdicts(
            identity_score=identity_score,
            safety_verdict=safety_verdict,
            ethics_verdict=ethics_verdict,
            refusal_emitted=refusal_emitted,
            hedge_injected=hedge_injected,
        )
        main_epistemic = epistemic_state_for_grounding_source(main_grounding_source)
        main_epistemic_state = main_epistemic.value
        # ADR-0206 — Response Governance Bridge seam (cognition path).
        # govern_response is STRICT-only (scaffold), so shape_surface is the
        # identity transform and response_surface is returned verbatim —
        # byte-identical to the pre-bridge path.  This is live wiring, not
        # dead code: the response surface now flows through the policy
        # consumer, and the ONLY thing keeping it strict is the STRICT
        # return value (proven by the live-wiring test).  The risk-reward
        # widening pathway and the math-serving seam are deferred to their
        # own PRs (ADR-0206 §5); wrong==0 is untouched here.
        main_reach_policy = govern_response(epistemic_state=main_epistemic)
        main_reach_level = main_reach_policy.level.value
        response_surface = shape_surface(
            main_reach_policy,
            committed_surface=response_surface,
            decode_state=main_epistemic,
        )
        main_normative_clearance = clearance_from_verdicts(verdicts_bundle).value
        main_normative_detail = normative_detail_from_verdicts(verdicts_bundle)
        turn_event = TurnEvent(
            turn=self._context.turn - 1,
            input_tokens=tuple(filtered),
            surface=response_surface,
            walk_surface=walk_surface,
            articulation_surface=articulation.surface,
            dialogue_role=str(dialogue_role),
            identity_score=identity_score,
            cycle_cost_total=cycle_cost.total,
            vault_hits=vault_hits,
            versor_condition=versor_condition(result.final_state.F),
            flagged=flagged,
            elaboration=sentence_plan.elaboration,
            safety_verdict=safety_verdict,
            ethics_verdict=ethics_verdict,
            verdicts=verdicts_bundle,
            grounding_source=main_grounding_source,
            register_id=register_id_main,
            register_variant_id=decoration_main.variant_id,
            anchor_lens_id=anchor_lens_id_main,
            anchor_lens_mode_label=anchor_lens_mode_label_main,
            realizer_guard_status=realizer_guard_status_main,
            realizer_guard_rule=realizer_guard_rule_main,
            register_canonical_surface=register_canonical_surface_main,
            composer_graph_atom_status=atom_equivalence_main.status,
            composer_atom_set_hash=atom_equivalence_main.composer_atom_set_hash,
            graph_atom_set_hash=atom_equivalence_main.graph_atom_set_hash,
            composer_graph_atom_overlap_count=atom_equivalence_main.overlap_count,
            epistemic_state=main_epistemic_state,
            normative_clearance=main_normative_clearance,
            normative_detail=main_normative_detail,
            reach_level=main_reach_level,
        )
        self.turn_log.append(turn_event)
        self._emit_turn_event(turn_event)
        self._push_thread_summary(
            turn_event=turn_event,
            intent_tag=warm_pack_intent_tag,
            intent_subject=warm_pack_subject or articulation.subject,
            grounding_source=main_grounding_source,
            surface=response_surface,
        )
        return self._checkpointed_response(
            ChatResponse(
                surface=response_surface,
                proposition=proposition,
                articulation=articulation,
                articulation_surface=articulation.surface,
                dialogue_role=dialogue_role,
                versor_condition=versor_condition(result.final_state.F),
                output_language=self.config.output_language,
                frame_pack=self.config.frame_pack,
                walk_surface=walk_surface,
                salience_top_k=result.salience_top_k,
                candidates_used=result.candidates_used,
                vault_hits=vault_hits,
                identity_score=identity_score,
                character_profile=self.character_profile,
                flagged=flagged,
                recall_energy_class=recall_energy_class_main,
                admissibility_trace=result.admissibility_trace,
                region_was_unconstrained=result.region_was_unconstrained,
                safety_verdict=safety_verdict,
                ethics_verdict=ethics_verdict,
                verdicts=verdicts_bundle,
                grounding_source=main_grounding_source,
                pre_decoration_surface=pre_decoration_surface_main,
                register_id=register_id_main,
                register_variant_id=decoration_main.variant_id,
                anchor_lens_id=anchor_lens_id_main,
                anchor_lens_mode_label=anchor_lens_mode_label_main,
                realizer_guard_status=realizer_guard_status_main,
                realizer_guard_rule=realizer_guard_rule_main,
                register_canonical_surface=register_canonical_surface_main,
                composer_graph_atom_status=atom_equivalence_main.status,
                composer_atom_set_hash=atom_equivalence_main.composer_atom_set_hash,
                graph_atom_set_hash=atom_equivalence_main.graph_atom_set_hash,
                composer_graph_atom_overlap_count=atom_equivalence_main.overlap_count,
                recalled_words=walk_tokens,
                epistemic_state=main_epistemic_state,
                normative_clearance=main_normative_clearance,
                normative_detail=main_normative_detail,
                reach_level=main_reach_level,
                refusal_reason=refusal_surface if refusal_emitted else "",
                dispatch_trace=dispatch_trace,
            )
        )

    def _unknown_domain_response(self, field_state: FieldState, filtered: list[str]) -> ChatResponse:
        return self._stub_response(field_state)

    def respond(self, text: str, max_tokens: int | None = None) -> str:
        """Return only the user-facing surface string for *text*.

        Convenience wrapper around :meth:`chat` for callers that need
        the raw surface without ChatResponse provenance — REPLs, simple
        scripts, and the existing test_language_pack_runtime suite.
        For audit / telemetry / verdict access, call :meth:`chat`.
        """
        return self.chat(text, max_tokens=max_tokens).surface

    async def achat(self, text: str, max_tokens: int | None = None) -> ChatResponse:
        """Async-compatible convenience wrapper around :meth:`chat`.

        This is a thin async surface; the underlying call is still
        synchronous CPU-bound work (versor walk, vault recall, surface
        composition).  Use this only for integration with asyncio-based
        callers that need an awaitable.  No real off-thread execution
        is performed — if true non-blocking concurrency is required,
        wrap calls in :func:`asyncio.to_thread` at the call site.
        """
        return self.chat(text, max_tokens=max_tokens)

    async def arespond(self, text: str, max_tokens: int | None = None) -> str:
        """Async-compatible convenience wrapper around :meth:`respond`.

        Same caveats as :meth:`achat` — wrapper, not true async.
        """
        return self.respond(text, max_tokens=max_tokens)

    def correct(self, text: str, target_turn: int = -1, max_tokens: int | None = None) -> ChatResponse:
        tokens = self._tokenize(text)
        filtered = self._apply_oov_policy(tokens)
        if not filtered:
            raise ValueError("correct() received no in-vocabulary tokens.")
        correction_state = inject(filtered, self._context.vocab)
        correction_result = self._correction_pass.apply(
            self._context.graph,
            correction_state.F,
            from_turn=target_turn,
        )
        self._context.apply_corrected_outputs(correction_result.records)
        self._emit_correction_event(correction_result, target_turn=target_turn)
        regen_tokens = self._context.last_input_tokens
        if not regen_tokens:
            return self._stub_response(correction_state)
        return self.chat(" ".join(regen_tokens), max_tokens=max_tokens)

    def _emit_correction_event(
        self, correction_result, *, target_turn: int,
    ) -> None:
        """ADR-0059 — emit one JSONL correction event to the telemetry sink."""
        sink = self._telemetry_sink
        if sink is None:
            return
        line = format_correction_event_jsonl(
            correction_result,
            target_turn=target_turn,
            identity_pack_id=self.identity_pack_id,
            safety_pack_id=self.safety_pack.pack_id,
            ethics_pack_id=self.ethics_pack_id,
        )
        sink.emit(line)
