ReactorFold: Generative discovery of nuclear reactor cores via emergent physical reasoning

• URL: http://arxiv.org/abs/2512.15756v1
• Date: Fri, 12 Dec 2025 02:26:19 GMT
• Title: ReactorFold: Generative discovery of nuclear reactor cores via emergent physical reasoning
• Authors: Yoonpyo Lee,
• Abstract summary: ReactorFold is a generative framework that reformulates fuel-assembly design as a sequence modeling problem for language models.<n>The model learns the latent structure of a pressurized-water-reactor assembly and generates candidate layouts in a single forward pass.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Designing nuclear reactor cores requires navigating large discrete design spaces governed by complex neutronic interactions. Traditional deterministic, metaheuristic, and machine-learning-assisted methods search within fixed, human-defined configuration spaces, limiting their ability to discover fundamentally new design topologies. Here we introduce ReactorFold, a generative framework that reformulates fuel-assembly design as a sequence modeling problem for language models. Using Monte Carlo data, parameter-efficient fine-tuning, and Direct Preference Optimization (DPO), the model learns the latent structure of a pressurized-water-reactor assembly and generates candidate layouts in a single forward pass. Notably, the DPO-aligned model exhibits emergent design-space expansion: despite being trained exclusively on configurations with a fixed number of gadolinium burnable absorber (Gd) rods, it autonomously adjusts Gd inventory to satisfy strict power-peaking constraints. The model also discovers high-performing asymmetric configurations that challenge conventional symmetric loading heuristics, accessing design regimes inaccessible to conventional search methods and demonstrating that language models can internalize causal physical relationships and transcend human-imposed design constraints.

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