de3f40b549
Closes audit Finding 2 (2026-05-20) — Phase B substrate. Pre-fix ``CognitiveTurnPipeline.run()`` invoked ``realize_semantic`` on the ungrounded ``PropositionGraph``. Every non-COMPARISON / non-CORRECTION node was born with ``obj = "<pending>"`` and the realizer emitted surfaces like ``"X is defined as ..."`` that ``_is_useful_surface`` correctly rejected. The realizer therefore never won the surface resolver introduced by PR #76 — it was structurally present but semantically inert in the hot pipeline path. This PR follows the codebase's standard substantive-change pattern (ADR-0046 ``forward_graph_constraint``, ADR-0062 ``composed_surface``, ADR-0083 ``transitive_surface``, ADR-0085 ``gloss_aware_cause``): ship the wiring behind a flag, default ``False``, with a CI-pinned null-lift invariant. Changes: * ``RuntimeConfig.realizer_grounded_authority: bool = False`` — operator-level opt-in. * ``ChatResponse.recalled_words: tuple[str, ...] = ()`` — alphabetic-filtered walk tokens from the recall step, populated on the main path of ``ChatRuntime._chat``. ``walk_tokens`` is now computed unconditionally so non-English packs also surface them (English keeps using them for ``articulate_with_intent`` as before). * ``CognitiveTurnPipeline.run()`` — when the flag is set and the response carries any recalled words, calls ``ground_graph(graph, response.recalled_words)`` and re-invokes ``realize_semantic`` on the grounded graph. The surface resolver (PR #76) then picks the realizer's grounded output when it clears ``_is_useful_surface`` and the unknown-domain gate did not fire. Phase A (realizer fluency parity — gloss-aware templates, 3sg verb agreement, pack-provenance tag) is documented in ADR-0088 §Phase A and is the prerequisite for enabling this flag in production. The known fluency gap (e.g. ``"Light is a visible medium that reveal truth"`` — subject-verb disagreement leaking from realizer templates) is the reason the flag ships default-off: operators get the wiring stable now, the realizer becomes a real authority once Phase A's fluency upgrade lands. Verification: * 4 new tests in ``tests/test_realizer_grounded_authority_flag.py``: - flag defaults to ``False`` on ``DEFAULT_CONFIG`` - flag-off produces byte-identical surface + trace_hash (null-lift invariant) - ``recalled_words`` is populated on the main path - flag-on runs end-to-end without crashing (surface is well-formed regardless of which authority won the resolver) * ``core eval cognition`` — public 100/100/91.7/100, byte-identical to the MEMORY baseline (default-off). * ``core test --suite cognition`` — 120/0/1. * ``core test --suite smoke`` — 67/0. * ``core test --suite runtime`` — 19/0.
1809 lines
80 KiB
Python
1809 lines
80 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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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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_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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content_tokens = [
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token
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for token in filtered_tokens
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if token.casefold() not in _QUESTION_WORDS and token.casefold() not in {"is", "are", "was", "were"}
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]
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if not content_tokens:
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return articulation
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anchor = content_tokens[-1]
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if 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
|
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# JSONL. ``""`` defaults preserve pre-C1 byte-identity.
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realizer_guard_status: str = ""
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realizer_guard_rule: str = ""
|
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# ADR-0077 (R6) — register layering boundary surface. Carries the
|
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# composer output BEFORE any register transformation (substantive
|
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# or decorative). The cognition pipeline hashes this field for
|
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# ``trace_hash`` when present, preserving R5's load-bearing
|
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# invariant — substantive register transforms must not move
|
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# ``trace_hash``. Empty string ⇒ pre-R6 caller; pipeline falls
|
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# back to ``pre_decoration_surface`` (byte-identity preserved).
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register_canonical_surface: str = ""
|
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# ADR-0078 (Phase 1) — observational composer/graph atom
|
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# equivalence telemetry mirrored from TurnEvent.
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composer_graph_atom_status: str = ""
|
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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)
|
|
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]:
|
|
kept: list[str] = []
|
|
for token in tokens:
|
|
try:
|
|
self._context.vocab.get_versor(token)
|
|
kept.append(token)
|
|
except KeyError:
|
|
if all(manifest.oov_policy is OOVPolicy.FAIL_CLOSED for manifest in self._manifests):
|
|
raise
|
|
if any(manifest.oov_policy is OOVPolicy.PROPOSE_VOCAB_EXPANSION for manifest in self._manifests):
|
|
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)
|
|
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
|
|
|
|
# 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
|
|
rendered = render_plan(plan)
|
|
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.")
|
|
|
|
probe_state = self._context.probe_ingest(filtered)
|
|
direct_hits = self._context.vault.recall(probe_state.F, 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=probe_state.F,
|
|
decomposer=default_decomposer,
|
|
)
|
|
if gate_decision.fire:
|
|
committed = self._context.commit_ingest(filtered)
|
|
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,
|
|
)
|
|
|
|
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)
|