AI 中文总结
本文提出智能体阶段一仿星器优化概念，通过语言模型智能体与DESC执行配合，提升有限β平衡构型的质量与数量，生成结构化决策数据，实现可扩展的多目标优化。
AI 中文摘要
阶段一仿星器设计在高维三维等离子体边界族及固定边界MHD平衡中搜索同时满足约束、场线拓扑、力平衡、稳定性代理及几何要求的构型，但这些规范未提供通用构造性映射以获得经验证的有限β平衡。高质量目标通常通过迭代数值优化开发，其结果依赖初始构型、活跃傅里叶分辨率、目标优先级及局部求解器预算，协调该过程计算成本高且需大量专家参与，限制了设计吞吐量和一致评估数据的生成。本文提出智能体阶段一优化的概念验证：有界语言模型智能体诊断当前平衡并选择下一次局部优化实验，确定性DESC执行负责规定的轮廓、通量、对称性、度量评估、求解器有效性及接受。在不断扩展的有限β计划的公共预算子集上，门控验证构型数量从5个输入增至19个输出；中位Boozer QS RMS从2.39×10⁻⁴降至1.07×10⁻⁴，中位最大主曲率从62.56 m⁻¹降至33.00 m⁻¹。一条补充长路径实现9.10倍QS降低，同时修正磁阱和曲率缺陷。系统还记录所有尝试的局部动作作为过渡证据，在报告实验中生成734条结构化父-动作-结果记录。这些结果表明，智能体外环控制可维持有限β多目标搜索，将重复优化转化为可扩展的改进平衡及可复用决策数据来源。
英文摘要
Stage-one stellarator design searches a high-dimensional family of three-dimensional plasma boundaries and fixed-boundary MHD equilibria for configurations that jointly meet requirements on confinement, field-line topology, force balance, stability proxies, and geometry. These specifications do not provide a general constructive map to a validated finite-beta equilibrium. High-quality targets are commonly developed through iterative numerical optimization whose outcome depends on the initial configuration, active Fourier resolution, objective priorities, and local solver budget. Coordinating this process is computationally costly and expert-intensive, limiting both design throughput and the production of consistently evaluated data. We present a proof of concept for \emph{agentic} stage-one optimization. A bounded language-model agent diagnoses the current equilibrium and selects the next local optimization experiment, while deterministic DESC execution owns prescribed profiles and flux, symmetry, metric evaluation, solver validity, and acceptance. On a common-budget subset from an expanding finite-beta campaign, the number of gate-valid configurations increases from five inputs to nineteen outputs; median Boozer QS RMS decreases from $2.39\times10^{-4}$ to $1.07\times10^{-4}$, and median maximum principal curvature decreases from $62.56$ to $33.00\,\mathrm{m}^{-1}$. A complementary long route achieves a $9.10\times$ QS reduction while repairing magnetic-well and curvature defects. The system also records every attempted local action as transition evidence, yielding 734 structured parent--action--outcome records in the reported experiments. These results show that agentic outer-loop control can sustain finite-beta, multi-objective search and turn repeated optimization into a scalable source of improved equilibria and reusable decision data.
