TGR-Ω and Persistent Agent Architectures: OMEGA-2A and the Monitoring of Dynamic Regimes in Artificial Systems

This paper presents OMEGA-2A, an experimental architecture for artificial agents that extends the line of investigation introduced in the earlier OMEGA-1 system within the conceptual framework of TGR-Ω (Theory of Regime Dynamics). While OMEGA-1 explored the basic implementation of persistent agent architectures, OMEGA-2A focuses on mechanisms that allow an agent to monitor the stability of its own internal dynamic regime and to reorganize its structure when coherence between its internal model and the observed environment begins to deteriorate. Rather than relying solely on prediction-error correction, the proposed architecture introduces internal mechanisms that track the evolving geometry of the agent’s state space through attractor-based representations operating at multiple temporal scales. The divergence between fast and slow attractor estimates provides a geometric signal of regime deformation, which is transformed into a continuous measure of proximity to structural transition. A regulatory integrator accumulates evidence of instability over time and may trigger a structural reorganization process when critical conditions are reached. The paper describes the progressive construction of the architecture, the instrumentation of its internal signals, and experimental observations obtained from multi-seed simulations. The results suggest that transitions between internal regimes are preceded by coordinated patterns in several signals that reflect a progressive loss of structural coherence. More broadly, the architecture continues the exploration initiated in OMEGA-1 toward artificial systems capable of monitoring and regulating their own dynamic regimes. This perspective points toward a class of adaptive agents in which the persistence of internal organization, rather than prediction accuracy alone, may become a central dimension of long-term adaptation.