TGR-Ω and Persistent Agent Architectures: OMEGA-2B and the Structural Characterization of Dynamic Regimes in Artificial Systems

OMEGA-2B introduces a regime-centered architecture for persistent artificial agents, extending OMEGA-2A from the detection of dynamic regime transitions toward the structural characterization, validation, and recovery of post-transition regimes. The model incorporates geometric instrumentation, structural regime memory, transition entropy, regime inertia, and post-EXPAND validation mechanisms in order to distinguish genuine structural transitions from noise, silent reorganizations, and representational degeneration. The central hypothesis of OMEGA-2B is that structural change cannot be reduced to instantaneous error magnitude or signal amplitude. Instead, regime transition should be understood as a temporally coherent pattern of divergence, persistence, and corrective failure. To formalize this view, the framework introduces metrics such as the Regime Stability Index (RSI), Regime Path Entropy (RPE), and the Transition Validation Criterion (TVC), together with rollback mechanisms for recovering from degraded internal configurations. OMEGA-2B is presented as a dynamical architecture in which an agent continuously monitors structural divergence between fast and slow attractor dynamics, modulates internal drives, activates EXPAND events under persistent instability, and consolidates only those regimes that satisfy structural and functional validity conditions. The proposed experimental protocol evaluates the model through drift, shock, persistent noise, false-drift, degeneration, and recovery scenarios. The framework contributes to the study of adaptive artificial agency by shifting the focus from reactive error detection to regime-based structural regulation. It suggests that persistent artificial agents should not merely select actions under changing conditions, but actively monitor, validate, reorganize, and recover their internal dynamic regimes over time.