Agentic physical AI toward a domain-specific foundation model for energy systems: a case study on nuclear reactor control

The prevailing paradigm in AI for physical systems: scaling general-purpose foundation models toward universal multimodal reasoning, confronts a barrier at the control interface. Frontier vision–language models achieve only 50–53% accuracy on basic quantitative physics tasks, behaving as approximate guessers that preserve semantic plausibility while violating physical constraints. Safety-critical control demands outcome-space guarantees over executed actions, not parameter-space imitation. Here, we present what is, to our knowledge, the first demonstration of Agentic Physical AI for nuclear reactor control: a domain-specific foundation model in which a compact language model learns control policies through physics-based simulator validation rather than perceptual inference. The framework unifies three capabilities: Agentic AI generates and selects among admissible control strategies; Physical AI evaluates those strategies through closed-loop execution in a reactor simulator; and a domain-specific foundation model acquires reusable control priors through data scaling. Under nominal simulated conditions, scaling yields regime-dependent gains in reliability, including an approximately 500-fold reduction in outcome variance and elimination of terminal-power excursions above 10% on the sampled distribution. Although trained with balanced exposure to four actuation families, the model concentrates 95% of runtime execution on a single-bank strategy without reinforcement learning or reward engineering. The learning framework transfers across reactor simulators without architectural redesign. These results establish a pathway toward reusable, physics-validated foundation-model intelligence for safety-critical control.