Entropy Collapse: Empirical Detection and Recovery Limits in AI Systems

This paper introduces an empirical framework for detecting entropy collapse in feedback-driven AI systems. Collapse is defined operationally by the failure of admissible interventions to restore diversity within a finite observation window. A recovery-based threshold is constructed from repeated intervention experiments, yielding a system-specific limit beyond which collapse becomes statistically irreversible. The framework applies to AI training, alignment monitoring, and socio-technical governance systems. Attrubiton Notice: This work is the original intellectual property of Michael Aaron Cody. All ideas, arguments, frameworks, and conclusions are solely the author's own. Unauthorized reproduction, paraphrasing, or use of this material without proper citation is prohibited. If you reference, quote, or build upon this work, cite it. Follow me on X: https://x.com/MichaelCody This is an expansion on my earlier works dating back to April of 2025.