Symbolic Cognition and Collapse-Aware Interpretability in Neural Systems: A Formal Framework for Bifractal AI Diagnostics (Draft)

We present a novel framework for interpreting neural network behavior through symbolic cognition collapse analysis, bridging the gap between network transparency and cognitive emergence. Using the Symbolic Collapse Benchmarking Framework (SCBF), we demonstrate how symbolic entropy collapse events correlate with interpretable cognitive processes in artificial neural networks. Our approach enables real-time monitoring of symbolic pattern formation, memory crystallization, and conceptual emergence within neural architectures.

Through extensive validation with TinyCIMM neural networks across mathematical reasoning tasks, we show that symbolic collapse patterns provide direct insight into network decision-making processes. The framework reveals measurable symbolic entropy collapse events that correspond to mathematical insight formation, with >95% activation ancestry stability and quantifiable recursive memory formation. These results suggest that symbolic interpretability can provide a unified approach to understanding both artificial and biological cognitive systems.

Our framework offers practical tools for AI safety, model debugging, and cognitive architecture design while contributing to fundamental understanding of symbolic emergence in neural systems.