A 4-Quadrant Meta-Cognition Framework for Internalizing AI Personalization via QLoRA

Short summary

The author proposes a 4-quadrant meta-cognition framework for AI personalization that trains thinking patterns into model weights via QLoRA, rather than relying on external crutches like RAG and system prompts. Using Qwen2.5-1.5B-Instruct fine-tuned on 253 instruction-response pairs from 50+ agent sessions on a 6GB laptop GPU, the resulting model demonstrated cross-domain transfer of structured reasoning behaviors across six untrained domains. The framework cycles through rules, blind spots, hidden patterns, and unknown assumptions, converting implicit knowledge into explicit rules over repeated sessions.

• •4-quadrant meta-cognition framework maps AI knowledge states: rules, blind spots, hidden patterns, and unknowns
• •QLoRA fine-tuning of Qwen2.5-1.5B on 253 pairs from agent logs showed cross-domain reasoning transfer to 6 untrained domains
• •Full pipeline runs on 6GB VRAM with 12 Python files; GitHub repo forthcoming

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