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Chris4K/Proto-Cognitive-Architecture-v2

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Proto-Cognitive Architecture v5

A hybrid AI system combining a Hebbian neural attractor field, a pinned episodic memory store, and Qwen2.5-0.5B-Instruct — exploring how continuous neural dynamics can augment frozen language models.

What's new in v5

v4 proved (via Suite 9 ablation) that the Hebbian field alone cannot recall facts — it scores 0.000 on all recall benchmarks. The field's real value is as a familiarity detector that routes queries via the CognitiveRouter.

v5 makes the field actually learn structure:

Mechanism	What it does	Why it matters
Hebbian W_local learning	Co-activation updates to the region-to-region connection matrix	Regions that fire together wire together — real associative structure forms
Memory consolidation (S→M)	Stable, protected patterns slowly transfer to long-term memory tensor M	Knowledge persists beyond decay — M influences field dynamics continuously
Replay / dreaming	Self-training loop without external input: noise → field dynamics → Hebbian reinforcement	Reinforces learned patterns, strengthens weak connections, consolidates after
Competition	Winner-take-most suppression of weak activations	Promotes specialization — regions either contribute or go quiet

Architecture

Query → Tokenize → Per-token field update → Think loop:
  ┌─────────────────────────────────────────────────┐
  │  field_step()        — W_local + M influence    │
  │  competition()       — suppress weak regions    │
  │  attend(x_embed)     — input projection + top-K │
  │  update(attn)        — state update + decay     │
  │  hebbian_update()    — learn W_local structure  │
  │  apply_attractors()  — pull toward stored states│
  │  ↻ repeat N steps                               │
  └─────────────────────────────────────────────────┘
  → if teach: consolidate(S → M) + auto-dream
  → CognitiveRouter: resonance × retrieval → route
  → Build prompt (system facts + soft prefix for CONFIDENT/CAUTIOUS)
  → Generate with Qwen2.5-0.5B-Instruct

Route decisions

	Retrieval HIGH	Retrieval LOW
Resonance HIGH	🟢 CONFIDENT	🟡 CAUTIOUS
Resonance LOW	🟠 UNCERTAIN	🔴 DEFER

Key findings

—The Hebbian field is not a memory — it's a familiarity/routing signal
—Episodic store (PinnedEpisodicStore) handles all reliable fact recall
—Field value: cognitive resonance measurement → query routing → confidence calibration
—v5: W_local now forms learned structure (was random in v4) — this is the field's real potential

How to use

1.Teach facts using the teach panel or batch import
2.Ask questions — observe the route decision (🟢🟡🟠🔴)
3.Dream — run replay cycles to reinforce structure
4.Watch W_local connections form in the diagnostics panel

Paper

This architecture is being prepared for arXiv submission. The honest negative result (FieldOnly ≈ 0.000) reframed into the CognitiveRouter is the core contribution.

Credits

Built by Chris4K at ki-fusion-labs MCP Innovation Award 2025