Modeling Self-Adapting AI as a Coherence-Preserving Control System: A Cross-Domain Framework for Adaptive Recursion

This paper models MIT’s Self-Adapting Language Model (SEAL) as a coherence-preserving control system, bridging concepts from adaptive-control theory, artificial intelligence, and generalized system dynamics. SEAL autonomously detects performance drift, generates internal self-edits, and applies reinforcement-guided fine-tuning, achieving over 72% success in abstract-reasoning benchmarks and exhibiting stable, self-correcting behavior. Its recursive feedback loop mirrors a broader coherence-preservation framework, in which systems maintain stability through cycles of detection, collapse, and self-rewrite. By formalizing SEAL’s gradient-update law alongside the discrete-time Model Reference Adaptive Control (MRAC) equation, the paper demonstrates a structural parallel between neural adaptation and classical feedback stabilization. The analysis extends to a proposed hybrid AI–quantum simulation architecture, suggesting testable parameters such as drift frequency, entropy variance, and damping constants. While primarily analytical, this work outlines how self-adapting AI systems instantiate a universal principle of adaptive recursion, stability through continual self-correction, and offers a cross-domain foundation for future research in control engineering, machine learning, and complex-systems theory.