Pre-work for a writing-curriculum extension to CORE. Two companion
documents, both Proposed status (no code shipped).
docs/decisions/ADR-0087-rhetorical-style-axis.md
Pins rhetorical style as a third selection axis — sibling to anchor
lens (ADR-0073), orthogonal to register (ADR-0070). Substantive
axis: trace_hash DISTINCT across styles (style changes which moves
the composer requires and which frames the realizer emits, which
changes the propositional plan, which changes the trace).
Four anti-patterns explicitly named and rejected:
- style as motor (re-couples realizer to geometry; same shape as
the ADR-0085 fusion-operator rejection)
- style as register variant (conflates substantive with stylistic)
- style as identity axis (bloats identity doctrine)
- style auto-detected from user input (operator-chosen only)
Pack shape mirrors packs/anchor_lens/. default_unstyled_v1 is the
null-lift pack identical to no-style behavior. Three CI invariants
proposed: rhetorical_style_null_lift, schema validation, three-axis
orthogonality.
Substrate-only ADR — no consumer code, no genre packs. Consumer
integration is a follow-up ADR (composer + realizer extensions
that read permitted_frames + required_moves_per_claim +
forbidden_moves).
docs/curriculum/writing-chain-harvester-spec.md
Layer 0 of the writing curriculum. A deterministic tool that
extracts candidate (subject, predicate, object) triples from
reviewed expert prose and surfaces them as proposals to the
existing teaching/review pipeline.
Five stages (segment → classify → extract → propose → audit) —
pure-Python rule-based, no LLM generation, no auto-acceptance.
Trust boundary: reviewer accept/reject via the existing
core teaching propose/review path. No bypass permitted.
The harvester is a proposal PRODUCER, not a proposal CONSUMER.
Plugs into the existing pipeline without inventing a new review
mechanism. Each proposal carries source_id + source_line + the
exact source_clause it came from for reviewer verification.
First-implementation acceptance criteria deliberately tight:
Stage 0+1 with dry-run only. Stages 2-5 are follow-up PRs.
Substrate-first sequencing pattern (ADR-0084 → 0085) reused
throughout. Both documents acknowledge open questions deferred to
implementation phase rather than pre-deciding.
Why now: a writing curriculum is being scoped. Without this ADR,
every downstream PR faces the same "should style be a motor?"
question and the temptation to reach for the geometry will recur
every time the realizer produces a stilted surface. Pinning the
axis up front prevents that recurrence.
ADR-0064 is the corpus-layer sibling of ADR-0063. The teaching-grounded
surface composer was hardcoded to cognition_chains_v1, so kinship CAUSE/
VERIFICATION prompts fell through to the universal disclosure even though
en_core_relations_v1 was mounted on the live runtime (ADR-0063).
Architectural change in chat/teaching_grounding.py:
- New TeachingCorpusSpec dataclass (corpus_id, path, pack_id).
- TEACHING_CORPORA tuple registers every active corpus. Each
corpus is 1:1-bound to one lexicon pack — cross-domain triples
deferred per docs/teaching_order.md §5.
- _load_corpus(spec) loads one corpus with pack-residency scoped
to its declared pack.
- _all_chains_index() aggregates across all registered corpora
(first-match-wins; cognition first preserves byte-identity).
- _pack_for_corpus(corpus_id) → bound pack lexicon.
- clear_teaching_caches() atomic cache invalidation.
- TeachingChain gains corpus_id field → surface tag follows resolving corpus.
Wiring updates:
- teaching_grounded_surface + teaching_grounded_surface_composed
consult _all_chains_index; surface tag follows chain.corpus_id.
- teaching/discovery.py gate uses chat.pack_resolver.is_resolvable
(any mounted pack) + _all_chains_index (any registered corpus).
- teaching/replay.py _swap_corpus_path rewrites the registry path
+ clears all teaching caches during the gate's transient phase.
Active corpus bytes unchanged (replay invariant preserved).
- evals/learning_loop/run_demo.py scene-5 swap mirrors the new
pattern so the demo still grounds against transient corpora.
Back-compat preserved: _corpus_index, _CORPUS_PATH, TEACHING_CORPUS_ID
remain cognition-corpus-specific for audit/replay consumers.
Phase 1.4 — relations_chains_v1 seeded with 7 reviewed kinship chains:
cause_parent_precedes_child
cause_child_follows_parent
cause_ancestor_precedes_descendant
cause_descendant_follows_ancestor
cause_family_grounds_parent
verification_child_requires_parent
verification_descendant_requires_ancestor
5 of 8 relations lemmas covered. All connectives already humanised.
Strict pack-internal to en_core_relations_v1 (no cross-domain in v1).
Seed pattern matches cognition_chains_v1's original pre-ADR-0055 seed.
Live verification:
> Why does parent exist?
parent — teaching-grounded (relations_chains_v1):
kinship.ascendant.direct; kinship.parent.
parent precedes child (kinship.descendant.direct).
grounding_source = teaching
Cognition eval byte-identical to pre-ADR baseline:
public: intent 100% / surface 100% / term 91.7% / closure 100%
holdout: intent 100% / surface 100% / term 83.3% / closure 100%
Lanes green: smoke 67 / cognition 121 / teaching 17 / packs 6 /
runtime 19 / algebra 132 / full 1933 passed.
ADR-0063 closes the ADR-0048/0050/0053/0061 hardcoded-cognition-pack
asymmetry. New chat/pack_resolver.py provides resolve_lemma(lemma,
pack_ids) → (resolving_pack_id, semantic_domains) across an ordered
tuple of mounted lexicon packs (first-match-wins, lru_cache per-pack).
Surface composers in chat/pack_grounding.py now consult the resolver
instead of a hardcoded en_core_cognition_v1. en_core_relations_v1
joins RuntimeConfig.input_packs defaults; kinship lemmas now ground
on the live path:
> What is a parent?
parent — pack-grounded (en_core_relations_v1):
kinship.ascendant.direct; kinship.parent; biology.progenitor.
No session evidence yet.
Cross-pack comparison (knowledge × parent) renders composite tag
(en_core_cognition_v1 × en_core_relations_v1). Cognition lane
remains byte-identical: cognition is resolved first and the surface
format for cognition lemmas is unchanged.
Cognition eval (byte-identical to pre-ADR baseline):
public → intent 100% / surface 100% / term 91.7% / closure 100%
holdout → intent 100% / surface 100% / term 83.3% / closure 100%
Curated lanes green: smoke 67 / cognition 121 / teaching 17 /
packs 6 / runtime 19 / algebra 132.
New tests: test_pack_resolver.py (28) + test_cross_pack_grounding.py
(17). test_en_core_relations_v1_pack.py: default-input-packs guard
inverted. test_pack_grounding.py: two stale ADR-0048 tests rewritten
(premises invalidated by ADR-0052/0061; now use fully-out-of-pack
prompts).
chat/teaching_grounding.py UNCHANGED — cognition_chains_v1 corpus
stays cognition-only. Cross-pack teaching corpora are the natural
ADR-0064.
Per teaching_order.md §5 — pick one commercial domain and run the
full 1→4 progression inside it before opening a second. Kinship is
the doctrinally classic starter: tight DAG, well-bounded primitives,
and orthogonal to the cognition pack.
Lemmas (8): parent, child, sibling, family, ancestor, descendant,
spouse, offspring. Each carries ≥2 semantic_domains under a
deterministic taxonomy (kinship.*, lineage.*, biology.*, social.*).
Deliberate exclusions:
- `person` — lives in en_core_cognition_v1; orthogonality test
pins that boundary.
- Specializations (mother/father/son/daughter/grandparent/...) —
derived from v1 primitives; land in v2 after v1 produces
reviewed chains.
- Quantifiers (one/two/many) — separate domain
(en_core_quantification_v1); cross-domain triples come last.
- Verbs of relation (begets/marries/...) — separate composer
work; no relations_chains_v1.jsonl yet.
Engagement is opt-in:
- Pack is NOT in RuntimeConfig.input_packs defaults.
- Programmatic mount via RuntimeConfig(input_packs=(..., "en_core_relations_v1")).
- CLI: core chat --pack en_core_relations_v1 (existing surface).
- Default-not-mounted preserves the cognition lane unchanged
until cross-pack teaching-grounded composition exists.
- language_packs/data/en_core_relations_v1/lexicon.jsonl
— 8 entries, JSONL format matching en_core_cognition_v1.
- language_packs/data/en_core_relations_v1/manifest.json
— pack_id, language, role=operational_base, checksum
(SHA-256 of lexicon bytes per CLAUDE.md pack-discipline),
version 1.0.0, determinism_class D0, oov_policy tagged_fallback.
- tests/test_en_core_relations_v1_pack.py — 6 tests pin:
checksum-match load, lemma roster, per-lemma primary domain,
≥2 domains/lemma (composer headroom), zero collision with
cognition pack (kinship DAG stays orthogonal), pack-not-in-
default-input-packs (opt-in engagement contract).
- docs/curriculum/relations_pack_v1.md — full pack log:
rationale per included/excluded lemma, opt-in engagement path,
4-step ADR roadmap (cross-pack composition → first kinship
chains → pronoun v2 → cross-domain triples).
Mounted-manifold sanity check (en_core_cognition_v1 +
en_core_relations_v1): 93 lemmas combined, no collisions, both
packs' surfaces individually addressable.
Lanes (regression): smoke 67 / packs 6 / algebra 132 / relations-pack 6.
The non-negotiable field invariant (versor_condition < 1e-6) is
unaffected: this is pure pack data + a contract test.
Second curriculum unit through the production operator surfaces.
Pure saturation — no cognition-lane lift expected (the eval splits
test fixed 32 cases that don't overlap with this unit's subjects),
but the live-prompt grounding surface expands materially: seven
prompts that previously fell through to disclosure now route to
deterministic teaching-grounded surfaces.
Three coherent clusters:
A. Cognition-source
cause_thought_reveals_meaning
cause_question_reveals_understanding
cause_recall_reveals_memory
B. Conceptual structure (bidirectional)
cause_definition_grounds_concept
verification_concept_requires_definition
C. Semantic content
cause_meaning_grounds_understanding
cause_analogy_reveals_relation
All pack-consistent (subject + object in en_core_cognition_v1),
canonical predicates (reveals / grounds / requires), each opens a
previously-empty (subject, intent) cell.
Replay-equivalence gate reported replay_equivalent=True for all
seven proposals (public cognition lane byte-identical pre/post
every accept).
Cognition lane:
public : intent 100% / surface 100% / term 91.7% / versor 100% (unchanged)
holdout : intent 100% / surface 100% / term 83.3% / versor 100% (unchanged)
Saturation lift is visible at the live-prompt level, not at the
eval level:
Why does thought exist? → [teaching] thought reveals meaning (...)
Why does a question exist? → [teaching] question reveals understanding (...)
Why does definition exist? → [teaching] definition grounds concept (...)
Why does meaning exist? → [teaching] meaning grounds understanding (...)
Why does an analogy exist? → [teaching] analogy reveals relation (...)
Does a concept require definition? → [teaching] concept requires definition (...)
Why does recall exist? → [teaching] recall reveals memory (...)
Why saturation matters: the cognition pack has 78 lemmas; we've
now covered ~21 (subject, intent) cells of the hundreds available.
Without saturation, prompts outside the 32 fixed eval cases are
coin-flips between vault recall and disclosure. Saturation moves
marginal prompts to deterministic teaching-grounded surfaces — the
foundation the composed-surface ADR (next) will compose over.
- teaching/cognition_chains/cognition_chains_v1.jsonl — 15 → 22 lines
(7 appends). Active set: 14 → 21 chains.
- teaching/proposals/proposals.jsonl — 7 new (created → replay →
transition → accepted_corpus_append) event sequences appended.
- docs/curriculum/cognition_saturation_v2.md — full curriculum log:
cluster rationale, live-prompt lift, operator-wall-time profile,
saturation-state-of-the-pack.
Lanes (regression check):
core test --suite smoke 67 passed
core test --suite cognition 121 passed
core test --suite teaching 17 passed
The non-negotiable field invariant (versor_condition < 1e-6) is
unaffected: this is corpus growth only; no code path changed.
First end-to-end curriculum unit through the production
propose / review --accept / supersede operator surfaces against the
active teaching corpus. Replay-equivalence gate passed for every
proposal; public split byte-identical; holdout term_capture lifted
exactly as predicted.
- Supersede `verification_wisdom_grounds_judgment` →
`verification_wisdom_requires_knowledge`. Fixes the only corpus-
fixable holdout miss: `verification_wisdom_036`
("Is wisdom the same as knowledge?") now grounds with both
expected terms. Provenance carries
`:supersede(verification_wisdom_grounds_judgment)`.
- Propose + accept four new chains closing epistemology subgraph
cells:
cause_understanding_requires_knowledge
cause_judgment_requires_wisdom
verification_evidence_grounds_knowledge
cause_inference_requires_evidence
Each chain is pack-consistent, uses canonical predicates, and opens
a previously-empty (subject, intent) cell. Replay gate confirmed
no metric regression on the public split before each accept.
Lift (cognition eval):
public : intent 100% / surface 100% / term 91.7% / versor 100% (unchanged)
holdout : intent 100% / surface 94.7% / term 70.8%→75.0% / versor 100%
The remaining four holdout misses (correction_truth_040,
procedure_define_010, unknown_spirit_041, unknown_word_018) are
architectural — surface-composition gaps in the correction-
acknowledgment template, procedure-intent routing, and unknown-
intent surface — and out of scope for corpus surgery.
- teaching/cognition_chains/cognition_chains_v1.jsonl — 10 → 15 lines
(4 appends + 1 supersession marker; 1 retired chain still on disk
per the audit doctrine of append-only at the file level).
- teaching/proposals/proposals.jsonl — new append-only proposal log
with `created` / `replay` / `transition` / `accepted_corpus_append`
events for every accepted proposal.
- docs/curriculum/epistemology_v1.md — full curriculum log:
rationale per chain, prediction-vs-result on the holdout lift,
reproducibility commands, architectural-gap analysis.
Lanes (regression check):
core test --suite smoke 67 passed
core test --suite cognition 121 passed
core test --suite teaching 17 passed
tests/test_eval_holdout_split 10 passed
The first curriculum unit that *measurably moves a cognition-lane
metric* through the operator surfaces, with full provenance from
operator note back to corpus append.
Shay