core/language_packs/schema.py
Shay ef95d3e609 feat(adr-0021): epistemic_status surface wired across teaching + trace
ADR-0021 v1 schema land. epistemic_status is a position in the revision
graph, not a source-trust tier — coherence is the only admission signal.

Surfaces:
- teaching/epistemic.py: EpistemicStatus enum (COHERENT, CONTESTED,
  SPECULATIVE, FALSIFIED); ADMISSIBLE_AS_EVIDENCE = {COHERENT}.
- PackMutationProposal.epistemic_status (default SPECULATIVE) + immutable
  with_status() updater.
- ReviewedTeachingExample.epistemic_status (default SPECULATIVE);
  orthogonal to acceptance per ADR §Schema impact.
- LexicalEntry.epistemic_status (default "coherent" for seed; absent in
  JSONL is treated as the seed default — no retroactive tagging).
- compute_trace_hash + trace_hash_from_result + pipeline.py fold the
  load-bearing proposal's epistemic_status into the trace hash so
  replay detects different epistemic frames.

Non-hardening invariant (ADR-0021 §2): tests/test_epistemic_invariants.py
asserts no final/frozen/axiom/permanent flag on PackMutationProposal or
ReviewedTeachingExample, and EpistemicStatus contains no source-trust
tier names.

Docs: docs/runtime_contracts.md gains an Epistemic surface section.

Lanes green: smoke 27/27, teaching 10/10, packs 6/6, runtime 19/19,
cognition eval 100%.
2026-05-16 20:20:35 -07:00

179 lines
5.8 KiB
Python

"""
Schemas for CORE compiled linguistic manifolds.
A language pack is not a dataset. It is a deterministic, checksummed,
compiled linguistic manifold: lexical surfaces, morphology, grammar
attractors, cross-language resonances, and holonomy-level proof cases.
These schemas intentionally do not load corpora. They define the contract the
Supervised Seeding Epoch must satisfy before Hebrew and Koine Greek gates can
engage.
"""
from __future__ import annotations
from dataclasses import dataclass, field
from enum import Enum
from typing import Mapping, Sequence
class LanguageRole(str, Enum):
"""Architectural role of a language pack in CORE-Logos."""
OPERATIONAL_BASE = "operational_base"
ARTICULATION_SURFACE = "articulation_surface"
DEPTH_ROOT = "depth_root"
DEPTH_RELATION = "depth_relation"
class OOVPolicy(str, Enum):
"""Out-of-vocabulary behavior for a pack."""
FAIL_CLOSED = "fail_closed"
TAGGED_FALLBACK = "tagged_fallback"
PROPOSE_VOCAB_EXPANSION = "propose_vocab_expansion"
@dataclass(frozen=True, slots=True)
class LanguagePackManifest:
"""Pinned manifest for one compiled language pack."""
pack_id: str
language: str
role: LanguageRole
script: str
normalization_policy: str
source_manifest: str
determinism_class: str
checksum: str
version: str = "1.0.0"
gate_engaged: bool = False
oov_policy: OOVPolicy = OOVPolicy.FAIL_CLOSED
def __post_init__(self) -> None:
if not self.pack_id:
raise ValueError("LanguagePackManifest.pack_id is required.")
if not self.language:
raise ValueError("LanguagePackManifest.language is required.")
if not self.checksum:
raise ValueError("LanguagePackManifest.checksum is required.")
if self.role in {LanguageRole.DEPTH_ROOT, LanguageRole.DEPTH_RELATION}:
if self.gate_engaged and self.oov_policy is not OOVPolicy.FAIL_CLOSED:
raise ValueError(
"Depth packs must fail closed while gate_engaged=True; "
"unknown Hebrew/Greek surfaces must not collapse to a fallback point."
)
@dataclass(frozen=True, slots=True)
class MorphologyEntry:
"""
Morphological decomposition for a surface form.
Ordering is load-bearing. For Semitic root morphology and Koine grammar,
non-commutative composition means prefix/stem/inflection/suffix order must
be preserved exactly.
"""
morphology_id: str
surface: str
lemma: str
language: str
root: str | None = None
prefix_chain: tuple[str, ...] = field(default_factory=tuple)
stem: str | None = None
inflection: Mapping[str, str] = field(default_factory=dict)
suffix_chain: tuple[str, ...] = field(default_factory=tuple)
def __post_init__(self) -> None:
if not self.morphology_id:
raise ValueError("MorphologyEntry.morphology_id is required.")
if not self.surface:
raise ValueError("MorphologyEntry.surface is required.")
if not self.lemma:
raise ValueError("MorphologyEntry.lemma is required.")
if not self.language:
raise ValueError("MorphologyEntry.language is required.")
@dataclass(frozen=True, slots=True)
class LexicalEntry:
"""One surface/lemma entry in a compiled linguistic manifold.
`epistemic_status` follows ADR-0021: it is a *position in the
revision graph*, not a source-trust tier. Seed vocabulary defaults
to ``"coherent"`` per ADR §Schema impact; bumps to other statuses
require a deliberate curator review at pack version bumps. Absent
from the JSONL is treated as the seed default — no retroactive
tagging without review.
"""
entry_id: str
surface: str
lemma: str
language: str
part_of_speech: str | None = None
pos: str | None = None
morphology_id: str | None = None
morphology_tags: tuple[str, ...] = field(default_factory=tuple)
semantic_domains: tuple[str, ...] = field(default_factory=tuple)
manifold_point_checksum: str | None = None
provenance_ids: tuple[str, ...] = field(default_factory=tuple)
epistemic_status: str = "coherent"
@dataclass(frozen=True, slots=True)
class GrammarAttractor:
"""
Structural grammar attractor seeded into the shared manifold.
Morphology is operator composition. Semantic domain is attractor geometry.
Alignment is resonance. This class represents the attractor layer only.
"""
attractor_id: str
language: str
role: str
description: str
operator_order: tuple[str, ...] = field(default_factory=tuple)
checksum: str | None = None
@dataclass(frozen=True, slots=True)
class AlignmentEdge:
"""Weighted directional resonance between entries or concepts."""
source_id: str
target_id: str
relation: str
weight: float
evidence_ids: tuple[str, ...] = field(default_factory=tuple)
def __post_init__(self) -> None:
if not 0.0 <= self.weight <= 1.0:
raise ValueError("AlignmentEdge.weight must be in [0, 1].")
@dataclass(frozen=True, slots=True)
class HolonomyAlignmentCase:
"""
Crown proof case for the three-language design.
The language system succeeds when aligned canonical clauses produce nearby
holonomies without flattening their distinctions. This is not token-level
translation; it is dynamic field-path resonance.
"""
case_id: str
description: str
source_refs: tuple[str, ...]
pack_ids: tuple[str, ...]
expected_relation: str
negative_source_refs: tuple[str, ...] = field(default_factory=tuple)
tolerance: float | None = None
def __post_init__(self) -> None:
if len(self.source_refs) < 2:
raise ValueError("HolonomyAlignmentCase requires at least two source_refs.")
if len(self.pack_ids) < 2:
raise ValueError("HolonomyAlignmentCase requires at least two pack_ids.")