Belief-Centered Dynamics for Adaptive Non-Equilibrium Intelligence

This paper proposes a belief-centered framework for adaptive intelligence operating under non-equilibrium conditions. Instead of modeling intelligence as optimization over fixed state spaces or objectives, internal belief structures are treated as primary dynamical variables. The framework introduces operational evaluation axes—irreversibility asymmetry, reconstructability, and actionability expansion—to distinguish meaningful adaptation from instability or stagnation. Belief evolution is characterized by irreversible internal transformations, resilience to perturbations, and expansion of controllable future actions, rather than convergence to predefined optima. The formulation is intentionally domain-agnostic and applies across chemical, physical, biological, and artificial systems. It does not prescribe specific learning algorithms, but provides principled criteria for diagnosing and designing adaptive non-equilibrium intelligent systems.