Agentic Physical AI toward a Domain-Specific Foundation Model for Nuclear Reactor Control

• URL: http://arxiv.org/abs/2512.23292v2
• Date: Tue, 06 Jan 2026 02:29:00 GMT
• Title: Agentic Physical AI toward a Domain-Specific Foundation Model for Nuclear Reactor Control
• Authors: Yoonpyo Lee, Kazuma Kobayashi, Sai Puppala, Sajedul Talukder, Seid Koric, Souvik Chakraborty, Syed Bahauddin Alam,
• Abstract summary: Recent benchmarks show that vision-language models achieve only 50-53% accuracy on basic quantitative physics tasks.<n> Perception-centric architectures optimize parameter-space imitation, whereas safety-critical control demands outcome-space guarantees.<n>We present a fundamentally different pathway toward domain-specific foundation models by introducing compact language models operating as Agentic Physical AI.
• Score: 3.9610256846446554
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The prevailing paradigm in AI for physical systems, scaling general-purpose foundation models toward universal multimodal reasoning, confronts a fundamental barrier at the control interface. Recent benchmarks show that even 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. This input unfaithfulness is not a scaling deficiency but a structural limitation. Perception-centric architectures optimize parameter-space imitation, whereas safety-critical control demands outcome-space guarantees over executed actions. Here, we present a fundamentally different pathway toward domain-specific foundation models by introducing compact language models operating as Agentic Physical AI, in which policy optimization is driven by physics-based validation rather than perceptual inference. We train a 360-million-parameter model on synthetic reactor control scenarios, scaling the dataset from 10^3 to 10^5 examples. This induces a sharp phase transition absent in general-purpose models. Small-scale systems exhibit high-variance imitation with catastrophic tail risk, while large-scale models undergo variance collapse exceeding 500x reduction, stabilizing execution-level behavior. Despite balanced exposure to four actuation families, the model autonomously rejects approximately 70% of the training distribution and concentrates 95% of runtime execution on a single-bank strategy. Learned representations transfer across distinct physics and continuous input modalities without architectural modification.

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