This paper presents a synthetic validation of the Universal Risk Cascade Theory (URCT) applied to agentic nuclear systems. Rather than modeling a specific plant, the study introduces an abstract dynamic framework capturing the interaction of physical, informational, and coordination-based risk components. A discrete-time simulation environment is constructed to represent coupled processes including physical degradation, AI-driven decision layers, persistent memory drift, and multi-agent coordination dynamics. Within this framework, human arbitration (HAP) and shared-state coordination architecture (MASP) are operationalized as control mechanisms. The central result demonstrates that persistent memory drift and coordination divergence, when considered independently, may remain within subcritical regimes. However, their interaction produces a supercritical composite cascade, leading to rapid regime transition and instability. This effect emerges consistently across multiple synthetic scenarios and Monte Carlo stress tests. The findings suggest that future nuclear risk models must explicitly incorporate agentic memory dynamics and coordination structure, as their interaction constitutes a distinct and currently underexplored class of systemic risk. This work is interpretive and synthetic in nature and does not represent a plant-specific safety model. Its purpose is to provide a formal and reproducible framework for analyzing cascade behavior in hybrid human–AI nuclear systems.
Oleg Zmiievskyi (Sat,) studied this question.