用AI自动发现加速器调束算法，提升效率与可重复性。
Autonomous discovery of accelerator commissioning algorithms
- 语言模型自主编写、测试并优化调束代码，形成闭环迭代。
- 在ALS-U模拟中显著改进专家经验算法，从零代码起步也可生成有效方案。
- 可同时优化多目标，生成16种不同权衡的高效算法，适合加速器设计者使用。
模拟调束已成为降低现代光源设计与调试风险的关键手段，但现有流程仍完全依赖人工专家设计。每次光栅结构变更后需重新手工开发，导致研究难以复现，且限制了早期设计阶段的应用。本文展示了一个闭环研究流程：语言模型代理自主编写调束代码，在仿真中测试，并根据结果改进算法。应用于ALS-U对撞机环的射频束捕获任务时，该流程不仅显著优化了现有专家算法，还能从极简初始代码构建出有效算法，且更强模型可在更少初始代码下成功。将同一框架扩展至多目标优化，生成了16个非支配解，覆盖快速束捕获与机器误差校正之间的多种物理上不同的权衡。这标志着调束研究从评估人工设计流程，转向由智能体直接参与发现加速器算法的新范式。
原文摘要 · Abstract (English)
Simulated commissioning has become essential for de-risking modern light-source design and commissioning, but the procedures being simulated are still designed entirely by human experts. Their labor-intensive redevelopment after lattice changes makes such studies hard to repeat and limits their use during early design iteration. This Letter demonstrates a closed research loop in which a language-model agent writes commissioning code, tests it in simulation, and improves the algorithm from the results. Applied to RF beam capture in the ALS-U accumulator-ring model, the loop substantially improves a working expert procedure and can construct a working one from a minimal starting point, with more capable models succeeding from less initial code. Extending the same framework to multiple objectives produces 16 non-dominated algorithms spanning physically distinct trade-offs between rapid beam capture and correction of seeded machine errors. This reframes commissioning studies from evaluating human-designed procedures toward a mode in which agents participate directly in discovering accelerator algorithms.
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