RCP is a new agent-to-agent protocol that reduces nuclear regulatory review costs by 50-77% (to 21-44M USD) and timelines by 65% (to 15 months) versus an 89M USD, 42-month reconstructed baseline.
Integrating LLMs for explainable fault diagnosis in complex systems.arXiv preprint arXiv:2402.06695, 2024
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A compact language model trained on scaled synthetic nuclear reactor control data exhibits variance collapse and emergent concentration on a single actuation strategy driven by physical execution success.
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Overcoming the Regulatory Bottleneck via Agent-to-Agent Protocols: A Nuclear Case Study
RCP is a new agent-to-agent protocol that reduces nuclear regulatory review costs by 50-77% (to 21-44M USD) and timelines by 65% (to 15 months) versus an 89M USD, 42-month reconstructed baseline.
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Agentic Physical AI toward a Domain-Specific Foundation Model for Nuclear Reactor Control
A compact language model trained on scaled synthetic nuclear reactor control data exhibits variance collapse and emergent concentration on a single actuation strategy driven by physical execution success.