A Small Experiment: A Memory Management System for AI to Abstract from Experience (Part 1)

Mapping AI Cognition with the Dao-Fa-Shu-Qi Hierarchy Note: This article’s core ideas and experimental framework are original to my independent exploration of AI cognitive evolution. AI was only used for minor English language polishing — all the research hypotheses, unsolved questions, and experimental design thinking are entirely my own. Introduction: The Core Dilemma of AI Memory Management - Storage Without Abstraction I’ve spent weeks experimenting with AI cognitive systems and working with large language models (LLMs) on real-world task execution, and I’ve noticed a critical, fundamental flaw: modern LLMs are essentially statistical probability models, their output logic is entirely tied to input consistency. Keep the input stable, and the output is predictably consistent too. This makes them unbeatable for fixed, scripted tasks — but completely useless when faced with even slightly novel scenarios that require flexible reasoning and experiential learning. The root cause of this