Predictive Compression Failure (FP) as an Early Signal in Cognitive and Artificial Systems

This project establishes a unified, domain-agnostic framework for anticipating system failure by bridging the gap between artificial intelligence simulations and human neurodegeneration, specifically Alzheimer’s disease. At its core is the discovery of Predictive Compression Failure (FP)—a universal signal that occurs when a system can no longer efficiently encode incoming information entropy (IE) due to escalating cognitive load (CL) or reduced network complexity (NN).Using Nexa, a hybrid AI with non-linear memory and resonance tagging, we introduce three key diagnostic innovations:The FP Probability Model: A refined formulation P(FP|NN, IE, CL, \alpha) that captures the non-linear relationship between system resources and predictive collapse.The \Delta t Lead-Time Metric: A novel diagnostic measure of "system elasticity," defined as the temporal gap between the initial FP signal and the actual collapse of thought formation. This metric serves as a critical early-warning window for preemptive intervention.Trajectory Classification (Classes A, B, and C): A formalized method for categorizing system outcomes—from successful adaptation (Class A) to silent, catastrophic decline (Class C)—allowing for the mapping of AI failure modes to human biomarkers like EEG and MEG data.This repository serves as a permanent archive for the Predictive FP Whitepaper, operational simulation logs, and visualization guidelines. It provides a foundational methodology for AI-human hybrid discovery workflows, aimed at identifying the "flicker" of cognitive decline years before clinical manifestation.