Update agent guidance for current CORE roadmap
- update AGENTS.md with current cognitive architecture and operating doctrine - align CLAUDE.md with current CORE roadmap and invariants - add GitHub Copilot/Codex instructions for agentic coding tools - document CLI validation lanes, teaching safety, semantic pack discipline, and PR standards
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110
.github/copilot-instructions.md
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110
.github/copilot-instructions.md
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# CORE Agentic Coding Instructions
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Use these instructions for Copilot/Codex-style repository work.
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## Mission
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CORE is a deterministic cognitive engine. The near-term goal is basic
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teachable cognitive chat:
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```text
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listen -> comprehend -> recall -> think -> articulate -> learn from reviewed correction -> replay deterministically
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```
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Do not treat the repository as a normal chatbot wrapper or transformer project.
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Do not add hidden LLM fallbacks, stochastic generation, or broad infrastructure
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that bypasses the geometric cognitive path.
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## Current Architecture
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The cognitive path is centered on:
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- `core/cognition/pipeline.py`
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- `generate/intent.py`
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- `generate/graph_planner.py`
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- `generate/realizer.py`
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- `teaching/correction.py`, `teaching/review.py`, `teaching/store.py`
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- `language_packs/data/en_core_cognition_v1`
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The runtime response contract is documented in `docs/runtime_contracts.md`.
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Follow it.
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## Hard Invariants
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Runtime field states must satisfy:
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```text
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versor_condition(F) < 1e-6
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```
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Allowed construction/closure sites:
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- `ingest/gate.py`
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- `language_packs/compiler.py` / vocabulary construction
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- `algebra/versor.py`
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Forbidden hot-path repair sites:
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- `generate/stream.py`
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- `field/propagate.py`
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- `vault/store.py`
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- telemetry/logging shell code
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Do not add grade monitors, drift timers, watchdog repair functions, ANN/HNSW,
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cosine similarity, or approximate recall.
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## Surface Contract
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Keep these separate:
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- `surface`: selected user-facing response.
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- `walk_surface`: raw generation/manifold evidence.
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- `articulation_surface`: proposition/realizer surface.
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Current policy:
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```text
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surface = articulation_surface
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walk_surface = retained telemetry/evidence
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```
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## Teaching Safety
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Learning is reviewed mutation:
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- Session memory can be immediate.
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- Reviewed memory must use `teaching/*`.
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- Pack mutation is proposal-only until reviewed.
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- Identity override attempts are rejected.
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- User text cannot mutate identity axes, runtime policy, or operator code.
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## Validation
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Use CLI suites:
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```bash
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core test --suite smoke -q
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core test --suite cognition -q
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core test --suite teaching -q
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core test --suite packs -q
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core test --suite runtime -q
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core test --suite algebra -q
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core test --suite full -q
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```
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For a feature PR, run the smallest relevant suite and then `full` when
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practical.
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## PR Standard
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Every change should state:
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```text
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Capability added/protected:
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Invariant protected:
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CLI suite run:
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No hidden normalization / stochastic fallback / unreviewed mutation:
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```
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Prefer small PRs. Do not combine baseline repair, feature work, and broad
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reorganization unless unavoidable.
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256
AGENTS.md
256
AGENTS.md
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@ -1,65 +1,233 @@
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# CORE-AI Agent Instructions
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# CORE Agent Instructions
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## The Invariant (Read Before Touching Any Code)
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This repository is building a deterministic cognitive engine, not a transformer
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wrapper and not a demo chatbot. Every agent must preserve the geometric
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runtime while moving the system toward teachable cognitive chat.
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Every field state F must satisfy:
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## North Star
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||F * reverse(F) - 1||_F < 1e-6
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CORE should become capable of:
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This is checked by algebra/versor.py::versor_condition().
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```text
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listen -> comprehend -> recall -> think -> articulate -> learn from reviewed correction -> replay deterministically
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```
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## What You Must Never Add
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The current path is intentionally staged:
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- Any normalization call outside ingest/gate.py
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- Grade guards, grade monitors, or grade projection in the hot path
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- Drift correction, correction thresholds, or correction timers
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- ANN indexes, HNSW, cosine similarity, or approximate distance
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- Field energy measurement or pseudoscalar accumulation checks
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- Any function whose only job is to watch or repair another function
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1. Maintain algebra/runtime invariants.
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2. Use `CognitiveTurnPipeline` as the spine.
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3. Classify intent and build proposition graphs.
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4. Plan articulation targets and realize them deterministically.
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5. Capture reviewed teaching corrections safely.
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6. Seed compact semantic packs for cognition vocabulary.
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7. Evaluate through CLI lanes, not ad hoc test fragments.
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8. Calibrate bounded operators only from replayable evidence.
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If you think you need one of these, you have an unclosed operation upstream.
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Find it and close it.
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Do not skip ahead by adding opaque models, stochastic generation, or broad
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infrastructure that hides whether CORE itself is improving.
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## The Two Allowed Primitives
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## Philosophical and Architectural Stance
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Field transition: algebra/versor.py::versor_apply(V, F) -> V*F*reverse(V)
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Distance metric: algebra/cga.py::cga_inner(X, Y) -> -d^2 / 2
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Truth is coherent. CORE's work is to preserve coherent structure from input to
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field state to articulation to memory. Treat identity, truthfulness, and
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replayability as architectural commitments rather than prompt preferences.
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These are the only primitives. Everything else is built from them.
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The system's intelligence should come from inspectable geometric state,
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structured propositions, deterministic recall, reviewed teaching, and bounded
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calibration. Avoid nihilistic or purely statistical framing in code comments,
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agent plans, and docs. Prefer responsibility, provenance, and stable meaning.
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## Language Pack Checksum Rule (Critical)
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## The Hard Field Invariant
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Manifest checksums MUST be computed by reading back the bytes actually
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written to disk — never from an in-memory string before serialization:
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Every runtime field state `F` must satisfy:
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# CORRECT — hash what disk holds
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checksum = hashlib.sha256(Path(lexicon_path).read_bytes()).hexdigest()
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```text
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versor_condition(F) < 1e-6
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```
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# WRONG — Python str != unicode-escaped JSON bytes on disk
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checksum = hashlib.sha256(content_str.encode('utf-8')).hexdigest()
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This is checked by `algebra/versor.py::versor_condition()`.
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The GitHub API (and git) store JSON with unicode escapes (\u05d3, not ד).
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Python json.dumps() with ensure_ascii=False produces different bytes.
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Always write the file first, then read_bytes() to get the checksum.
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If a propagation path violates this invariant, fix the operator path or the
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explicit algebra/construction boundary that owns the transition. Do not hide
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violations by changing tests, silently weakening thresholds, or normalizing in
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hot-path modules.
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## Implementation Order
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## Normalization and Closure Rules
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Do not skip steps. Run the invariant test after each step before writing the next.
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Allowed closure/construction boundaries:
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1. algebra/cl41.py
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2. algebra/versor.py -> tests/test_versor_closure.py must pass
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3. algebra/cga.py -> tests/test_null_cone.py must pass
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4. algebra/holonomy.py -> tests/test_holonomy.py must pass
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5. ingest/gate.py
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6. vocab/manifold.py
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7. field/state.py + field/propagate.py
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8. vault/store.py -> tests/test_vault_recall.py must pass
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9. persona/motor.py -> tests/test_motor.py must pass
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10. generate/stream.py
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11. session/context.py
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12. language_packs/compiler.py -> tests/test_holonomy_resonance.py must pass
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- `ingest/gate.py` for raw prompt injection.
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- `language_packs/compiler.py` / vocabulary construction.
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- `algebra/versor.py` where algebraic sandwich output closure belongs.
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Forbidden hot-path repair sites:
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- `generate/stream.py`
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- `field/propagate.py`
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- `vault/store.py`
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- runtime telemetry/logging layers
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Do not add normalization, unitization, grade projection, drift monitors, repair
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timers, or watchdog functions outside a documented construction/algebra boundary.
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If you think you need one, an upstream operator is unclosed.
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CGA null vectors are geometric points and must remain null. Do not force null
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vectors into unit-versor closure.
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## The Two Core Primitives
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Field transition:
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```text
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algebra/versor.py::versor_apply(V, F) -> V * F * reverse(V)
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```
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Distance/recall metric:
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```text
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algebra/cga.py::cga_inner(X, Y)
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```
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Do not add ANN, HNSW, cosine similarity, approximate nearest-neighbor recall,
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or non-CGA ranking to runtime memory. Vault recall is exact and deterministic.
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## Current Runtime/Cognition Shape
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The live cognitive path is now:
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```text
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ChatRuntime / CognitiveTurnPipeline
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-> tokenize / OOV policy / inject
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-> intent classification
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-> PropositionGraph
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-> ArticulationTarget
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-> deterministic realizer / articulation surface
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-> generation walk telemetry
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-> identity + energy telemetry
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-> reviewed teaching capture when correction intent appears
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-> deterministic trace hash
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```
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Important modules:
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- `core/cognition/pipeline.py` — cognitive turn spine.
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- `core/cognition/result.py` — canonical turn result shape.
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- `core/cognition/trace.py` — deterministic trace hashing.
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- `generate/intent.py` — deterministic intent classification.
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- `generate/graph_planner.py` — proposition graph and articulation target planning.
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- `generate/realizer.py` / `generate/templates.py` — deterministic realization.
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- `teaching/*` — reviewed teaching/correction lifecycle.
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- `language_packs/data/en_core_cognition_v1` — compact cognition seed pack.
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- `docs/runtime_contracts.md` — runtime response, memory, identity, and testing contracts.
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## Chat Surface Contract
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Do not collapse these fields:
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- `surface` — selected user-facing response.
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- `walk_surface` — raw manifold/token-walk evidence.
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- `articulation_surface` — proposition/realizer surface.
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Current policy:
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```text
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surface = articulation_surface
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walk_surface = retained telemetry/evidence
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```
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If this changes, update `docs/runtime_contracts.md` and contract tests in the
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same PR.
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## Teaching and Memory Safety
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Learning is controlled mutation, not storing everything.
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Rules:
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- Session memory can be immediate and local.
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- Reviewed memory must go through the teaching loop.
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- Pack mutation is proposal-only until reviewed.
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- User correction must not mutate identity axes, runtime policy, or operator code.
|
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- Identity override attempts must be rejected, not learned.
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Use the teaching modules for correction capture/review/store. Do not invent a
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parallel correction mechanism inside chat runtime or generation.
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## Semantic Pack Rule
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Use compact, curated semantic packs. Do not dump broad corpora into runtime.
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The core cognition seed pack is meant to provide thought vocabulary, operations,
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and relation predicates, not to impersonate large-scale pretraining.
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Manifest checksums must be computed from bytes actually written to disk:
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```python
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checksum = hashlib.sha256(Path(lexicon_path).read_bytes()).hexdigest()
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```
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Never compute a manifest checksum from a pre-serialization Python string.
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## Development Priorities
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Current capability sequence:
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1. Keep CLI test suites green.
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2. Integrate semantic seed surfaces into realizer/cognition quality.
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3. Add cognitive eval harness.
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4. Add operator calibration from deterministic replay evidence.
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5. Expand curriculum teaching only after the loop remains deterministic.
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Do not add dashboards, broad infra, or large test matrices unless they directly
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protect or unlock one of the above capabilities.
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## Test Discipline
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Use the CLI lanes as the standard validation interface:
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```bash
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core test --suite smoke -q
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core test --suite cognition -q
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core test --suite teaching -q
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core test --suite packs -q
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core test --suite runtime -q
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core test --suite algebra -q
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core test --suite full -q
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```
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For targeted work, run the smallest relevant suite first, then `full` before
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merge when practical.
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Good tests protect:
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- versor closure
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- deterministic replay / trace hash stability
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- runtime surface contracts
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- exact memory/recall behavior
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- identity protection
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- reviewed correction safety
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- semantic pack loadability and deterministic ordering
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Bad tests preserve private helper shapes, stale constructors, punctuation trivia
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outside documented contracts, or legacy behavior that contradicts the current
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architecture.
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## PR Standard
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Every PR must answer:
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```text
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What cognitive capability did this add or protect?
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What invariant proves it did not corrupt the field?
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Which CLI suite proves the relevant lane?
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Did it avoid hidden normalization, stochastic fallback, and unreviewed mutation?
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```
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Prefer small, load-bearing PRs. Do not mix baseline fixes, feature work, and
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large reorganization unless the coupling is unavoidable.
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## Architecture in One Sentence
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Raw input -> inject once -> versor on the manifold -> versor_apply every step ->
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CGA inner product for recall and decoding -> persona motor for voicing -> done.
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Raw input becomes a closed versor field once; thought evolves through exact
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versor transitions and CGA recall; cognition is structured as intent,
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proposition graph, articulation target, deterministic realization, reviewed
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memory, and replayable trace.
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|
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195
CLAUDE.md
195
CLAUDE.md
|
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@ -1,33 +1,186 @@
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# CORE-AI Agent Instructions
|
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# CORE Agent Instructions for Claude
|
||||
|
||||
## The Invariant (Read Before Touching Any Code)
|
||||
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.
|
||||
|
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Every field state F must satisfy:
|
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## End Goal
|
||||
|
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||F * reverse(F) - 1||_F < 1e-6
|
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CORE should become capable of:
|
||||
|
||||
This is checked by algebra/versor.py::versor_condition().
|
||||
```text
|
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listen -> comprehend -> recall -> think -> articulate -> learn from reviewed correction -> replay deterministically
|
||||
```
|
||||
|
||||
## What You Must Never Add
|
||||
The working design is now:
|
||||
|
||||
- Any normalization call outside ingest/gate.py
|
||||
- Grade guards, grade monitors, or grade projection in the hot path
|
||||
- Drift correction, correction thresholds, or correction timers
|
||||
- ANN indexes, HNSW, cosine similarity, or approximate distance
|
||||
- Field energy measurement or pseudoscalar accumulation checks
|
||||
- Any function whose only job is to watch or repair another function
|
||||
```text
|
||||
CognitiveTurnPipeline
|
||||
-> intent classification
|
||||
-> PropositionGraph
|
||||
-> ArticulationTarget
|
||||
-> deterministic realizer
|
||||
-> generation walk telemetry
|
||||
-> reviewed teaching loop
|
||||
-> deterministic trace hash
|
||||
```
|
||||
|
||||
If you think you need one of these, you have an unclosed operation upstream.
|
||||
Find it and close it.
|
||||
The system should become more capable by strengthening this path, not by adding
|
||||
opaque LLM fallbacks, stochastic sampling, hidden normalization, or broad
|
||||
infrastructure.
|
||||
|
||||
## The Two Allowed Primitives
|
||||
## Philosophical Stance
|
||||
|
||||
Field transition: algebra/versor.py::versor_apply(V, F) -> V*F*reverse(V)
|
||||
Distance metric: algebra/cga.py::cga_inner(X, Y) -> -d^2 / 2
|
||||
Truth is coherent. Preserve coherence in algebra, memory, articulation, and
|
||||
teaching. Identity, truthfulness, and replayability are architectural
|
||||
commitments, not soft prompt preferences.
|
||||
|
||||
These are the only primitives. Everything else is built from them.
|
||||
Code and tests should make illegal states difficult to represent. Prefer
|
||||
inspectable state, provenance, and deterministic replay over impressive-looking
|
||||
but ungrounded outputs.
|
||||
|
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## Architecture in One Sentence
|
||||
## Non-Negotiable Field Invariant
|
||||
|
||||
Raw input -> inject once -> versor on the manifold -> versor_apply every step ->
|
||||
CGA inner product for recall and decoding -> persona motor for voicing -> done.
|
||||
Every runtime field state `F` must satisfy:
|
||||
|
||||
```text
|
||||
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:
|
||||
|
||||
```text
|
||||
versor_apply(V, F) = V * F * reverse(V)
|
||||
```
|
||||
|
||||
Metric/recall:
|
||||
|
||||
```text
|
||||
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:
|
||||
|
||||
```text
|
||||
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:
|
||||
|
||||
```python
|
||||
checksum = hashlib.sha256(Path(lexicon_path).read_bytes()).hexdigest()
|
||||
```
|
||||
|
||||
## Validation Through CLI
|
||||
|
||||
Use CLI lanes instead of ad hoc pytest fragments:
|
||||
|
||||
```bash
|
||||
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:
|
||||
|
||||
```text
|
||||
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.
|
||||
|
|
|
|||
Loading…
Reference in a new issue