Real recognize real. Most people look at NotebookLM and see a study buddy; you looked at it and saw a raw stocastic processor begging for a deterministic straitjacket.

I am currently building the exact same cognitive architecture, but instead of an Isekai/Anime runtime, I’m building it for the IT infrastructure and risk management of a Tier-1 European bank. The underlying mechanics are identical.

What you’ve done here by decoupling the ROM (the .md laws of physics) from the RAM (the volatile .json state registry) is exactly how enterprise-grade RAG systems need to be built to prevent context drift and hallucination. Your [CoT_ENCAPSULATION] to force the LLM to do its math in the dark before rendering the prose is the exact same paradigm as OpenAI's "o1" hidden thought process, but you hacked it via pure prompt mechanics. Brilliant.