The Persistence Equation as a Thermodynamic Self-Regulation Framework for Humanised AI

This paper proposes a novel application of the Persistence Equation as a self-regulatory framework for artificial intelligence systems. By embedding thermodynamic and informational constraints within learning agents, the normalized Persistence Equation enables real-time assessment of reversibility, fragility, entropy accumulation, and environmental adaptability. This transforms the equation into a dynamic meta-policy that governs how an AI system sustains its internal coherence over time. Beyond technical robustness, this approach lays the foundation for more human-like behavior in AI by prioritizing flexibility, memory, restraint, and self-awareness. The framework supports lifelong learning, conversational continuity, system-wide integrity, and a shift from exploitative to resilient machine reasoning.