Phase Intelligence in Sequential AI Systems: A Regime-Transition Framework for Early Detection of Latent Behavioral Shifts

This work introduces Phase Intelligence, a framework for modeling sequential AI systems as partially observable dynamical systems evolving through latent behavioral regimes. The central claim is that transitions into harmful or irreversible states occur in latent space before they become detectable at the output level, creating a structural detection latency. The framework proposes trajectory-based monitoring using early warning signals such as variance, autocorrelation, and instability patterns to detect regime shifts under partial observability. This is a conceptual and theoretical research note focused on structural failure modes in AI monitoring systems.