core/CLAUDE.md

CORE Agent Instructions for Claude

Read this before modifying the repository. CORE is a deterministic cognitive engine under construction, not a transformer wrapper, not a generic chatbot, and not an infrastructure playground.

End Goal

CORE should become capable of:

listen -> comprehend -> recall -> think -> articulate -> learn from reviewed correction -> replay deterministically

The working design is now:

CognitiveTurnPipeline
  -> intent classification
  -> PropositionGraph
  -> ArticulationTarget
  -> deterministic realizer
  -> generation walk telemetry
  -> reviewed teaching loop
  -> deterministic trace hash

The system should become more capable by strengthening this path, not by adding opaque LLM fallbacks, stochastic sampling, hidden normalization, or broad infrastructure.

Philosophical Stance

Truth is coherent. Preserve coherence in algebra, memory, articulation, and teaching. Identity, truthfulness, and replayability are architectural commitments, not soft prompt preferences.

Code and tests should make illegal states difficult to represent. Prefer inspectable state, provenance, and deterministic replay over impressive-looking but ungrounded outputs.

Non-Negotiable Field Invariant

Every runtime field state F must satisfy:

versor_condition(F) < 1e-6

Do not weaken this threshold to make tests pass. Fix the operator/construction boundary that violated it.

Normalization Rules

Allowed sites:

• ingest/gate.py for raw input injection.
• language_packs/compiler.py and vocabulary construction.
• algebra/versor.py for algebra-owned sandwich closure.

Forbidden sites:

• generate/stream.py
• field/propagate.py
• vault/store.py
• logging/telemetry/runtime shell code

Do not add drift repair, grade projection, watchdogs, timers, hot-path normalizers, or monitoring functions whose only purpose is to repair another function.

CGA null vectors are not unit versors. Preserve null vectors as null vectors.

Core Primitives

Field transition:

versor_apply(V, F) = V * F * reverse(V)

Metric/recall:

cga_inner(X, Y)

Do not add cosine similarity, HNSW, ANN indexes, or approximate recall to the runtime path. Vault recall is exact and deterministic.

Current Key Modules

• core/cognition/pipeline.py — cognitive turn spine.
• core/cognition/result.py — result object for pipeline evidence.
• core/cognition/trace.py — deterministic trace hashing.
• chat/runtime.py — user-facing runtime contract.
• generate/intent.py — deterministic intent classification.
• generate/graph_planner.py — proposition graph and articulation target planning.
• generate/realizer.py and generate/templates.py — deterministic surface realization.
• teaching/correction.py, teaching/review.py, teaching/store.py — reviewed teaching loop.
• language_packs/data/en_core_cognition_v1 — core cognition semantic seed pack.
• docs/runtime_contracts.md — response, telemetry, memory, identity, and testing contracts.

Runtime Surface Contract

Keep these distinct:

• surface: selected user-facing response.
• walk_surface: raw manifold/token-walk evidence.
• articulation_surface: proposition/realizer surface.

Current policy:

surface = articulation_surface
walk_surface = retained telemetry/evidence

Any change must update docs/runtime_contracts.md and contract tests in the same PR.

Teaching Safety

Learning must be reviewed and auditable.

• Session memory may be immediate.
• Reviewed memory must go through teaching/*.
• Pack mutation is proposal-only until reviewed.
• Identity override attempts are rejected.
• User text must not mutate identity axes, runtime policy, or operator code.

Do not create a parallel correction/learning path.

Semantic Pack Discipline

Prefer compact, curated packs. Do not bulk-ingest corpora into runtime. en_core_cognition_v1 supplies thought vocabulary, operations, and relation predicates. Extend it cautiously, with deterministic ordering and pack tests.

Manifest checksums must hash the bytes actually written to disk:

checksum = hashlib.sha256(Path(lexicon_path).read_bytes()).hexdigest()

Validation Through CLI

Use CLI lanes instead of ad hoc pytest fragments:

core test --suite smoke -q
core test --suite cognition -q
core test --suite teaching -q
core test --suite packs -q
core test --suite runtime -q
core test --suite algebra -q
core test --suite full -q

Run the smallest relevant suite first, then full before merge when practical.

Work Sequencing

Current near-term sequence:

1. Keep CLI lanes green.
2. Integrate semantic seed relations into realizer/cognition quality.
3. Add cognitive eval harness.
4. Add deterministic operator calibration from replay evidence.
5. Expand curriculum teaching after the loop is stable.

Avoid broad docs-first churn, dashboard work, or large infrastructure unless it unlocks one of these steps.

PR Checklist

Before opening or merging, answer:

What capability did this add or protect?
Which invariant proves the field remains valid?
Which CLI suite proves the lane?
Did this avoid hidden normalization, stochastic fallback, and unreviewed mutation?

Prefer small, load-bearing PRs with clear evidence.