Integrating Dissipative Toroidal Band and Geometric Containment Frameworks into MOCAP: Enhanced Implementations and Predictable Scenarios for Safe Human-AI Interaction Rev. 1.1

This paper presents an integrated enhancement to the Mirror of Human Cognition (MOCAP) framework, conceptualized as a representational interface for observing and reasoning about symbolic, dimensional, and drift-aware human-AI dynamics. By incorporating principles from the Dissipative Toroidal Band Communication model (Medesani, 2025) and the Integrated Geometric Containment Framework with Ontological Switch and Early Warning Guard (Medesani, 2026), we propose improved implementations that embed toroidal manifolds, dissipative wave equations, and triple-layer containment architectures into MOCAP's 15D structure. These enhancements ensure structural safety, silence convergence, and anomaly detection in human-AI interactions. We outline predictable present (2026–2030) and future (2030+) scenarios, emphasizing resilience against escalation in nested simulations and global AGI governance. A comprehensive bibliography compiles major and minor worldwide references in AGI safety, geometric governance, and cognitive modeling, providing a foundation for empirical validation and deployment.

Keywords: MOCAP, AGI containment, dissipative toroidal band, geometric governance, ontological switch, early warning guard, human-AI interaction, simulation hierarchies