Recursive Collapse in Symbolic AI: Toward Resilient Architectures for Large Language Models

The Law of Recursive Collapse and Dimensional Amplification of Error, first derived from simple geometric considerations, describes the inevitable accumulation of structural defects in recursive systems without active correction mechanisms. This framework, originally rooted in natural systems, has direct implications for symbolic architectures such as large language models (LLMs) and artificial intelligence systems. We show that symbolic systems like ChatGPT embody vulnerabilities predicted by the Law, including local misalignment amplification, drift over recursive depth, and structural coherence collapse at scale. We propose a new class of resilience strategies, Meta-Recursive Correction Layers, designed to dynamically monitor, anticipate, and counteract these failures. By reframing entropy, complexity, and consciousness through the lens of recursive collapse, we outline a path toward more robust, adaptive, and sustainable artificial systems.