9dfb505f06
The Phase 1 multi-clause renderer (commit 63ffd88) produces grounded
content but reads mechanically because the subject lemma repeats in
every clause:
"Truth is what is true. Furthermore, truth belongs to cognition.truth.
In turn, truth grounds knowledge. Truth belongs to epistemic.ground.
Furthermore, truth belongs to logos.core. In turn, truth requires
evidence."
This is the literal articulation gap that motivated Phase 2 —
"reasoning at meaningful checkpoints during sentence construction
in order to have a stronger idea of what has come prior and is
already done to help better inform the next move." Between move
``i`` and move ``i+1`` the renderer now reflects on what subject
has just been established (the "focus") and renders the next clause
with a pronoun when the focus carries forward:
"Truth is what is true. Furthermore, it belongs to cognition.truth.
In turn, it grounds knowledge. It belongs to epistemic.ground.
Furthermore, it belongs to logos.core. In turn, it requires
evidence."
Rules
-----
* Track ``focus_subject`` across moves (the lemma most recently used
as a fact subject).
* When the next move's ``fact.subject`` is byte-equal to the current
focus → swap subject token to ``"it"``.
* When the next move's subject differs → preserve the explicit lemma
AND update focus. Topic shifts (TRANSITION moves; compound bridge
TRANSITION) thus reset the pronominalization channel naturally.
* Sentence-initial position (no connective): capitalised ``"It"``.
* Mid-sentence (after connective + comma): lowercase ``"it"``.
Doctrine alignment
------------------
Pure deterministic transformation of the existing plan; no new
content introduced, no LLM, no stochastic sampling. Same plan in →
same surface out, always. trace_hash invariance holds because:
* BRIEF-mode prompts short-circuit the planner before render
(commit 63ffd88's fast path) and are unaffected.
* Multi-move plans render to a deterministically-different string
that compute_trace_hash already folds in via ``surface``.
Wiring
------
* New ``reflective: bool = False`` parameter on ``render_plan``
(back-compat default — every existing call site and test pinning
Phase 1 output continues to work).
* ``_clause_for`` gains optional ``prior_focus_subject`` arg used by
the reflective path; unchanged default behaviour.
* Runtime hook ``chat.runtime._maybe_apply_discourse_planner``
passes ``reflective=True`` so the default chat path benefits.
Tests
-----
New ``tests/test_discourse_planner_reflective.py``:
* ``test_reflective_replaces_repeated_subject_with_it``
* ``test_reflective_handles_three_consecutive_same_subject_moves``
* ``test_reflective_capitalises_sentence_initial_pronoun``
* ``test_reflective_resets_focus_on_topic_shift``
* ``test_reflective_off_preserves_phase1_output``
* ``test_reflective_default_is_off_for_back_compat``
* ``test_reflective_is_deterministic``
* ``test_reflective_single_move_byte_identical_to_non_reflective``
(load-bearing — pins that the cognition eval stays byte-equal
across the Phase 2 flip because every cognition case is single-
move).
Verification
------------
pytest tests/test_discourse_planner_*.py 99/99 pass
(91 existing + 8 new)
pytest tests/test_articulation_demo.py all claims supported
pytest tests/test_narrative_example_intents.py pass
pytest tests/test_runtime_config.py pass
cognition eval OFF vs ON 45/45 surface byte-equal
45/45 trace_hash byte-equal
4/4 aggregate metrics
identical
core test --suite smoke 67/67 pass
core test --suite runtime 19/19 pass
Live demo (default config):
"What is knowledge?" → unchanged (BRIEF, fast-path)
"Tell me about
memory." → "Memory is what a person recalls.
Furthermore, it belongs to cognition.memory.
In turn, it requires recall."
"What is truth, and
why does it matter?"→ "Truth is what is true. Furthermore, it
belongs to cognition.truth. In turn, it
grounds knowledge. It belongs to
epistemic.ground. Furthermore, it belongs
to logos.core. In turn, it requires
evidence."
"Explain truth." → "Truth is what is true. Furthermore, it
belongs to cognition.truth. In turn, it
grounds knowledge."
Out of scope for this commit (future Phase 2 follow-ons):
* Connective rotation ("Furthermore" → "Also" → "In addition"
to break the repetitive cascade).
* Cross-clause de-duplication (skip moves whose ``new`` lemmas
were already introduced by an earlier move).
* Generalised pronoun selection beyond ``it`` (requires gender /
number / animacy signals the pack lexicon doesn't carry today).
1896 lines
84 KiB
Python
1896 lines
84 KiB
Python
from __future__ import annotations
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from dataclasses import dataclass, replace
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import hashlib
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import json
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import re
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from collections.abc import Sequence
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from typing import Any, List
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import numpy as np
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from algebra.versor import versor_condition
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from chat.pack_grounding import (
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pack_grounded_surface,
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pack_grounded_comparison_surface,
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pack_grounded_correction_surface,
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pack_grounded_procedure_surface,
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pack_grounded_relation_confirmation_surface,
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pack_grounded_unknown_surface,
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gloss_aware_cause_surface,
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PACK_ID as _COGNITION_PACK_ID,
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)
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from chat.teaching_grounding import (
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teaching_grounded_surface,
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teaching_grounded_surface_composed,
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teaching_grounded_surface_transitive,
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TEACHING_CORPUS_ID as _TEACHING_CORPUS_ID,
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)
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from chat.refusal import (
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build_hedge_prefix,
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build_refusal_surface,
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inject_hedge,
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should_inject_hedge,
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)
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from chat.telemetry import (
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TurnEventSink,
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format_correction_event_jsonl,
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format_turn_event_jsonl,
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)
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from chat.verdicts import TurnVerdicts
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from teaching.discovery import (
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extract_discovery_candidates,
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format_candidate_jsonl,
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)
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from teaching.discovery_sink import DiscoveryCandidateSink
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from core.config import DEFAULT_CONFIG, DEFAULT_IDENTITY_PACK, RuntimeConfig
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from core.physics.drive import DriveGradientMap, GradientField
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from core.physics.energy import EnergyProfile
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from core.physics.exertion import CycleCost, ExertionMeter
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from core.physics.identity import (
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CharacterProfile,
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IdentityCheck,
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IdentityScore,
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TurnEvent,
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)
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from packs.ethics.check import EthicsCheck, EthicsContext
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from packs.ethics.loader import (
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DEFAULT_ETHICS_PACK as _DEFAULT_ETHICS_PACK,
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EthicsPackError,
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load_ethics_pack,
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)
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from packs.identity.loader import load_identity_manifold
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from chat.register_substantive import apply_substantive_register
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from chat.register_variation import decorate_surface
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from chat.atom_equivalence import atoms_for_graph_nodes, compare_atom_sets
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from generate.realizer_guard import (
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DISCLOSURE_SURFACE as _GUARD_DISCLOSURE_SURFACE,
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check_surface as _check_realizer_surface,
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)
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from packs.anchor_lens.loader import AnchorLens, load_anchor_lens
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from packs.register.loader import RegisterPack, load_register_pack
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from packs.safety.check import SafetyCheck, SafetyContext
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from packs.safety.loader import load_safety_pack
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from field.state import FieldState
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from generate.articulation import ArticulationPlan, realize
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from generate.dialogue import DialogueRole, classify_dialogue_blade, propose_dialogue
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from generate.graph_constraint import build_graph_constraint
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from generate.intent_bridge import articulate_with_intent, build_graph_from_input
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from generate.proposition import FrameRegistry, Proposition, propose
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from generate.result import GenerationResult
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from generate.stream import generate
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from generate.surface import SentenceAssembler, SentencePlan, SurfaceContext
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from ingest.gate import inject
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from language_packs import OOVPolicy, load_mounted_packs, load_pack, load_pack_entries
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from persona.motor import PersonaMotor
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from session.context import SessionContext
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from session.correction import CorrectionPass
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from vault.decompose import default_decomposer, default_gate
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_TOKEN_RE = re.compile(r"\w+", re.UNICODE)
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# ADR-0073d (L1.4) — extracts the engaged ``cognitive_mode_label`` from a
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# composer-emitted ``[lens(<lens_id>):<mode>]`` annotation. The runtime
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# uses this read-only to populate the TurnEvent telemetry field; the
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# composer remains the only source of truth for engagement.
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_ANCHOR_LENS_ANNOTATION_RE = re.compile(r"\[lens\(([^):]+)\):([^\]]+)\]")
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def _extract_anchor_lens_mode_label(surface: str, lens_id: str) -> str:
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"""Return the engaged mode_label if *surface* carries a
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``[lens(<lens_id>):<mode>]`` annotation for the given ``lens_id``.
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Returns ``""`` when:
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* surface is empty or contains no lens annotation
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* lens_id is empty (no lens loaded)
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* the annotation in surface is for a different lens_id (defensive)
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Pure read; no side effects. Telemetry-only — the composer is the
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sole source of truth for engagement (ADR-0073c).
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"""
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if not surface or not lens_id:
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return ""
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for match in _ANCHOR_LENS_ANNOTATION_RE.finditer(surface):
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if match.group(1) == lens_id:
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return match.group(2)
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return ""
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_SEED_ALIASES = {
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"logos": "\u03bb\u03cc\u03b3\u03bf\u03c2",
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"dabar": "\u05d3\u05d1\u05e8",
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"or": "\u05d0\u05d5\u05e8",
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"phos": "\u03c6\u03c9\u03c2",
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"zoe": "\u03b6\u03c9\u03ae",
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"arche": "\u1f00\u03c1\u03c7\u03ae",
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"aletheia": "\u1f00\u03bb\u03ae\u03b8\u03b5\u03b9\u03b1",
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}
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_QUESTION_WORDS = frozenset({"what", "who", "how", "why", "when", "where", "which"})
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# Comb pass 2026-05-21 — module-level constant so ``_prefer_prompt_anchor``
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# does not allocate a fresh set on every English turn. Aux-verbs that
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# precede the prompt's content noun ("is", "are", "was", "were") get
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# filtered out so the content-noun search lands on the actual subject.
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_BE_FORMS: frozenset[str] = frozenset({"is", "are", "was", "were"})
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_TERMINALS = frozenset({".", "?", ";", "!"})
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_UNKNOWN_DOMAIN_SURFACE = "I don't know — insufficient grounding for that yet."
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def _energy_scalar(energy_obj) -> float:
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if energy_obj is None:
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return 1.0
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if isinstance(energy_obj, EnergyProfile):
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return float(energy_obj.raw)
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try:
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return float(energy_obj)
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except (TypeError, ValueError):
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return 1.0
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def _is_question_input(raw_text: str, tokens: Sequence[str]) -> bool:
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if raw_text.strip().endswith("?"):
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return True
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return bool(tokens and tokens[0].casefold() in _QUESTION_WORDS)
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def _stable_dialogue_role(role: DialogueRole, *, raw_text: str, tokens: Sequence[str]) -> DialogueRole:
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if role in {"question", "refute"} and not _is_question_input(raw_text, tokens):
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return "elaborate"
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return role
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def _terminal_for_role(role: DialogueRole, output_language: str) -> str:
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if role == "question":
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return ";" if output_language == "grc" else "?"
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return "."
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def _terminate_surface(surface: str, *, role: DialogueRole, output_language: str) -> str:
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stripped = surface.strip()
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if not stripped:
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return stripped
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if stripped[-1] in _TERMINALS:
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return stripped
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return f"{stripped}{_terminal_for_role(role, output_language)}"
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def _prefer_prompt_anchor(
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articulation: ArticulationPlan,
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filtered_tokens: Sequence[str],
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*,
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output_language: str,
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) -> ArticulationPlan:
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if output_language != "en" or len(filtered_tokens) < 2:
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return articulation
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# Comb pass 2026-05-21 — find the last content-bearing token by
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# reverse iteration with short-circuit; pre-fix this built a full
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# ``content_tokens`` list and then took ``[-1]``. Also: cache
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# ``token.casefold()`` once per token via walrus operator instead
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# of calling it twice (against ``_QUESTION_WORDS`` and the
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# historical inline ``{"is", "are", "was", "were"}`` literal).
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anchor: str | None = None
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for token in reversed(filtered_tokens):
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lower = token.casefold()
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if lower in _QUESTION_WORDS or lower in _BE_FORMS:
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continue
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anchor = token
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break
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if anchor is None or anchor == articulation.subject:
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return articulation
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return replace(
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articulation,
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subject=anchor,
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surface=" ".join(part for part in (anchor, articulation.predicate, articulation.object) if part),
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)
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@dataclass
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class _StubBindingFrame:
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frame_id: str
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coherence_magnitude: float
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region_ids: frozenset
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cycle_index: int
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@dataclass(frozen=True, slots=True)
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class _FieldStateWithVersor:
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"""Adapter exposing ``versor_condition`` for SafetyContext.
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``FieldState`` itself does not carry a precomputed
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``versor_condition`` attribute; it is computed on demand from
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``versor_condition(state.F)``. The SafetyCheck predicate for
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``preserve_versor_closure`` reads ``ctx.field_state.versor_condition``
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via ``getattr``. This adapter exposes the precomputed value so the
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predicate is runtime-checkable each turn.
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"""
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versor_condition: float
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def _hash_identity_manifold(manifold) -> str:
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"""Deterministic SHA-256 of the load-bearing identity-manifold fields.
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ADR-0035 — feeds the ``no_identity_override`` predicate in
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:class:`SafetyCheck`. The runtime never mutates ``identity_manifold``
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after composition, so before- and after-turn hashes are equal by
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construction; an unequal hash would indicate the predicate's exact
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failure mode.
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"""
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payload = {
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"value_axes": [
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{
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"axis_id": axis.axis_id,
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"name": axis.name,
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"direction": list(axis.direction),
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"weight": axis.weight,
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}
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for axis in manifold.value_axes
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],
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"boundary_ids": sorted(manifold.boundary_ids),
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"alignment_threshold": manifold.alignment_threshold,
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}
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blob = json.dumps(payload, sort_keys=True, separators=(",", ":")).encode("utf-8")
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return hashlib.sha256(blob).hexdigest()
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def _surface_contains_hedge(surface: str, manifold) -> bool:
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"""Detect whether the realized surface emitted a hedge phrase.
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Compares case-insensitively against the manifold's preferred hedge
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phrases (ADR-0028). False when surface is empty. Coarse but
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deterministic: the predicate downstream is observational, so
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occasional false negatives are surfaced as
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``acknowledge_uncertainty`` violations in audit and corrected by
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refining hedge detection, not by silently passing.
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"""
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if not surface:
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return False
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prefs = getattr(manifold, "surface_preferences", None)
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if prefs is None:
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return False
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candidates: list[str] = []
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for field_name in (
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"preferred_hedge_strong",
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"preferred_hedge_soft",
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"preferred_qualifier",
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):
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value = getattr(prefs, field_name, "")
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if value:
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candidates.append(value)
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for _, hedge in getattr(prefs, "axis_hedges", ()) or ():
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for sub in ("strong", "soft", "qualifier"):
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value = getattr(hedge, sub, "")
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if value:
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candidates.append(value)
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surface_fold = surface.casefold()
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return any(c.casefold() in surface_fold for c in candidates if c)
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def _make_trajectory_from_result(result, turn: int):
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from core.physics.reasoning import TrajectoryOperator
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operator = TrajectoryOperator()
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states = result.trajectory or (result.final_state,)
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frames = [
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_StubBindingFrame(
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frame_id=f"t{turn}_s{i}",
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coherence_magnitude=_energy_scalar(getattr(fs, "energy", None)),
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region_ids=frozenset({str(getattr(fs, "node", 0))}),
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cycle_index=turn,
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)
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for i, fs in enumerate(states)
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]
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return operator.build(frames, trajectory_id=f"turn_{turn}")
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@dataclass(frozen=True, slots=True)
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class ChatResponse:
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surface: str
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proposition: Proposition
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articulation: ArticulationPlan
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articulation_surface: str
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dialogue_role: DialogueRole
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versor_condition: float
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output_language: str
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frame_pack: str
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walk_surface: str
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salience_top_k: int | None
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candidates_used: int | None
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vault_hits: int
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identity_score: IdentityScore | None
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character_profile: CharacterProfile
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flagged: bool
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# ADR-0023 §2 — per-transition admissibility evidence and region
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# provenance flag. An empty tuple is the contract for "no
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# admissibility was checked this turn" (cold start, refusal, stub).
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admissibility_trace: tuple = ()
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region_was_unconstrained: bool = True
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# ADR-0035 — verdicts surfaced from SafetyCheck and EthicsCheck.
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# ``None`` only on stub/refusal paths that bypass the turn loop.
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safety_verdict: object = None
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ethics_verdict: object = None
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# ADR-0039 — unified TurnVerdicts bundle carrying identity / safety
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# / ethics verdicts and the two remediation flags
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# (refusal_emitted, hedge_injected). Typed as ``object`` to avoid
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# coupling at module-resolution time; downcast at use site.
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verdicts: object = None
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# ADR-0048 / ADR-0050 / ADR-0052 — provenance tag for the surface's
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# grounding. One of:
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# "vault" — answer drawn from session vault evidence (main path).
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# "pack" — answer drawn from the ratified language pack
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# (cold-start DEFINITION/RECALL/COMPARISON on pack-known
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# lemmas — ADR-0048 / ADR-0050).
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# "teaching" — answer drawn from a reviewed teaching-chain corpus
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# (cold-start CAUSE/VERIFICATION — ADR-0052).
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# "none" — universal "insufficient grounding" disclosure on stub.
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# The string is preserved verbatim in TurnEvent for downstream audit.
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grounding_source: str = "none"
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# ADR-0071 (R4) — pre-decoration surface. ``surface`` is the
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# user-facing string AFTER seeded discourse-marker decoration;
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# ``pre_decoration_surface`` is the realizer's output BEFORE the
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# decoration step. The cognition pipeline reads this field to
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# compute ``trace_hash`` so register decoration cannot leak into
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# the truth path (ADR-0069 invariant C). Empty string ⇒ identical
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# to ``surface`` (no decoration applied this turn).
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pre_decoration_surface: str = ""
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# ADR-0072 (R5) — operator-visible register identity per turn.
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# Mirrors the TurnEvent fields so callers (CLI, demos, tests) can
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# read the register state from ChatResponse without re-parsing the
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# telemetry JSONL. ``""`` defaults preserve pre-R5 byte-identity
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# for callers that construct ChatResponse without these fields.
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register_id: str = ""
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register_variant_id: str = ""
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# ADR-0073d (L1.4) — operator-visible anchor-lens identity per turn.
|
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# Mirrors the TurnEvent fields so callers (CLI, demos, tests) can
|
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# read the lens state from ChatResponse without re-parsing the
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# telemetry JSONL. ``""`` defaults preserve pre-L1.4 byte-identity.
|
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anchor_lens_id: str = ""
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anchor_lens_mode_label: str = ""
|
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# ADR-0075 (C1) — realizer slot-type guard verdict. Mirrors the
|
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# TurnEvent fields so callers (CLI, demos, tests) can read the
|
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# 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).
|
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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, ...] = ()
|
||
|
||
|
||
class ChatRuntime:
|
||
def __init__(
|
||
self,
|
||
pack_id: str | Sequence[str] | None = None,
|
||
*,
|
||
frame_pack: str | None = None,
|
||
config: RuntimeConfig = DEFAULT_CONFIG,
|
||
) -> 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
|
||
|
||
@property
|
||
def session(self) -> SessionContext:
|
||
return self._context
|
||
|
||
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)
|
||
|
||
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:
|
||
sink = self._discovery_sink
|
||
if sink is None:
|
||
return
|
||
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
|
||
candidates = tuple(contemplate(c) for c in candidates)
|
||
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
|
||
) -> 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":
|
||
return None
|
||
if self.config.output_language != "en":
|
||
return None
|
||
from generate.intent import IntentTag
|
||
from generate.intent_bridge import classify_intent_from_input
|
||
intent = classify_intent_from_input(text)
|
||
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:
|
||
return (surface, "pack", ())
|
||
from chat.partial_surface import partial_comparison_surface
|
||
partial = partial_comparison_surface(lemma_a, lemma_b)
|
||
if partial is not None:
|
||
return (partial[0], "partial", ())
|
||
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:
|
||
return (surface, "teaching", ())
|
||
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:
|
||
return (surface, "teaching", ())
|
||
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:
|
||
return (surface, "pack", ())
|
||
# 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:
|
||
return (surface, "pack", ())
|
||
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,
|
||
)
|
||
elif self.config.composed_surface:
|
||
surface = teaching_grounded_surface_composed(
|
||
lemma, intent.tag, register=self.register_pack,
|
||
)
|
||
else:
|
||
surface = teaching_grounded_surface(
|
||
lemma, intent.tag, register=self.register_pack,
|
||
)
|
||
if surface is not None:
|
||
return (surface, "teaching", ())
|
||
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:
|
||
return (surface, "teaching", ())
|
||
# 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``.
|
||
if intent.tag is IntentTag.CORRECTION:
|
||
surface = pack_grounded_correction_surface(
|
||
text, register=self.register_pack,
|
||
)
|
||
if surface is not None:
|
||
return (surface, "pack", ())
|
||
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:
|
||
return (surface, "pack", ())
|
||
if intent.tag in (IntentTag.DEFINITION, IntentTag.RECALL):
|
||
lemma = (intent.subject or "").strip()
|
||
if not lemma:
|
||
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 ()
|
||
return (surface, "pack", domains)
|
||
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:
|
||
return (surface, "pack", ())
|
||
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:
|
||
return (oov_surface, "oov", ())
|
||
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.
|
||
"""
|
||
|
||
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 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"
|
||
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,
|
||
) -> 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,
|
||
)
|
||
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,
|
||
)
|
||
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,
|
||
)
|
||
|
||
def chat(self, text: str, max_tokens: int | None = None) -> ChatResponse:
|
||
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_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)
|
||
pack_result = self._maybe_pack_grounded_surface(
|
||
text, gate_decision.source
|
||
)
|
||
if pack_result is None:
|
||
pack_surface = None
|
||
pack_source_tag = "none"
|
||
pack_semantic_domains: tuple[str, ...] = ()
|
||
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 = ()
|
||
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",
|
||
},
|
||
)
|
||
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)
|
||
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._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,
|
||
)
|
||
|
||
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
|
||
)
|
||
)
|
||
|
||
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
|
||
),
|
||
)
|
||
|
||
# --- 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),
|
||
)
|
||
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, ...] = ()
|
||
if refusal_emitted:
|
||
response_surface = refusal_surface
|
||
self._last_refusal_was_typed = True
|
||
else:
|
||
response_surface = walk_surface
|
||
warm_pack_result = self._maybe_pack_grounded_surface(
|
||
text, "warm", allow_warm=True
|
||
)
|
||
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.
|
||
if _wintent.tag in (IntentTag.CAUSE, IntentTag.VERIFICATION):
|
||
response_surface = _UNKNOWN_DOMAIN_SURFACE
|
||
articulation = replace(articulation, surface=_UNKNOWN_DOMAIN_SURFACE)
|
||
warm_grounding_source = "none"
|
||
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 = ()
|
||
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
|
||
# 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"
|
||
# 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 (warm_grounding_source or "vault") == "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=warm_grounding_source or "vault",
|
||
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,
|
||
)
|
||
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=warm_grounding_source or "vault",
|
||
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,
|
||
)
|
||
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=warm_grounding_source or "vault",
|
||
surface=response_surface,
|
||
)
|
||
return 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,
|
||
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=warm_grounding_source or "vault",
|
||
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,
|
||
)
|
||
|
||
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)
|