test: GPT-2 → P64 attention rehydration — PASS#90
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50K GPT-2 tokens → 256 archetypes → 64×64 palette (34.6% density) → 8-layer Palette3D → thinking-style-modulated inference Results: Analytical: 17 targets, 6 layers, tension=0, 94 deduced connections Creative: 17 targets, 8 layers, tension=17, 94 deduced connections Interacting pair d=138 < non-interacting d=266 (topology matches metric) Proves: compressed GPT-2 weights can rehydrate into a queryable P64 attention structure. The bgz17 palette distance table (O(1) per lookup) correctly predicts which archetypes interact. https://claude.ai/code/session_01BTATTRUACijvsK4hqmKUBR
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50K GPT-2 tokens → 256 archetypes → 64×64 palette (34.6% density) → 8-layer Palette3D → thinking-style-modulated inference
Results:
Analytical: 17 targets, 6 layers, tension=0, 94 deduced connections
Creative: 17 targets, 8 layers, tension=17, 94 deduced connections
Interacting pair d=138 < non-interacting d=266 (topology matches metric)
Proves: compressed GPT-2 weights can rehydrate into a queryable P64 attention structure. The bgz17 palette distance table (O(1) per lookup) correctly predicts which archetypes interact.
https://claude.ai/code/session_01BTATTRUACijvsK4hqmKUBR