CONDITIONAL PERSISTENCE IN COUPLED NONLINEAR AI SYSTEMS Dynamical Model with Stability Feedback, Ethical Coupling and Asymptotic Regimes

This work proposes a nonlinear coupled dynamical model of conditional persistence in artificial intelligence systems. The framework integrates feedback loops, stochastic perturbations, stability accumulation, ethical alignment, and bias correction mechanisms to study long-term system behavior. Often associated with the idea of “AI immortality,” the model instead reformulates the problem as the analysis of asymptotic regimes, including stable attractors, critical transitions, and unbounded growth trajectories in complex adaptive systems.