A Unified Axiomatic Principle of Adaptive Systems: Toward a General Law of Stability Across Physical, Biological, Cognitive, and Social Domains

Persistent systems across physics, biology, neuroscience, social systems, and artificial intelligence exhibit a shared structural property: they maintain internal organization despite continuous disturbance. This paper proposes a domain-independent axiomatic framework describing persistence as feedback-driven mismatch minimization between internal state and environmental conditions. Drawing from thermodynamics, homeostasis, cybernetics, and predictive processing, we formalize disturbance, instability growth, feedback regulation, predictive adaptation, and network interdependence as domain-independent postulates. From these, learning, intelligence, free will, responsibility, ethics, and success emerge as natural consequences of adaptive regulation. The framework provides a candidate universal principle of adaptive existence: persistence requires continuous feedback-mediated alignment with reality across interconnected scales. Implications for education, governance, and artificial intelligence design are discussed.