I wanted to share an experiment I've been working on for the past 11 months. I am a non-coder (architect) based in Japan, but I managed to build a system that stabilizes Gemini 1.5 Pro over long contexts (800k+ tokens).

The Problem: When context gets too long, the AI gets "Drunk" (Context Dilution) and ignores System Instructions.

The Solution: I applied the concept of "Bhavanga" (Life Continuum) from ancient Buddhist Psychology. Instead of a static RAG, I built a 3-layer architecture: 1. Super-Ego: System Instructions v1.5.0 (The Anchor) 2. Ego: Gemini 1.5 Pro (The Processor) 3. Id: Vector DB (The Unconscious Stream)

I wrote a detailed breakdown of this architecture on Medium. I'd love to hear your thoughts on this "Pseudo-Human" approach.

Full Article: https://medium.com/@office.dosanko/project-bhavanga-building-the-akashic-records-for-ai-without-fine-tuning-1ceda048b8a6

GitHub: https://github.com/dosanko-tousan/Gemini-Abhidhamma-Alignment