用语言模型生成核反应堆新设计，自动突破人类规则限制。
ReactorFold: Generative discovery of nuclear reactor cores via emergent physical reasoning
- 将燃料组件设计转为序列建模，用语言模型生成布局。
- 零样本发现非对称高能效结构，自适应调节吸收棒数量。
- 适合核工程与生成模型交叉研究者阅读。
核反应堆核心设计需在复杂中子物理约束下探索巨大离散设计空间。传统方法受限于人工定义的配置范围，难以发现根本性新拓扑。本文提出ReactorFold，将压水堆燃料组件设计转化为语言模型的序列建模问题。基于蒙特卡洛数据，采用参数高效微调与直接偏好优化（DPO），模型在单次前向传播中生成候选布局。值得注意的是，尽管仅在固定钆吸收棒数量的配置上训练，该模型仍表现出涌现的设计空间扩展能力：自主调整钆含量以满足严格的功率峰因子约束。此外，模型发现了高性能的非对称布局，挑战了传统对称加载范式，进入常规搜索方法无法触及的设计区域。结果表明，语言模型可内化因果物理关系，超越人为设定的设计边界。
原文摘要 · Abstract (English)
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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