让智能体自我进化，自动设计超表面光学结构。
A Self-Evolving Agentic Framework for Metasurface Inverse Design
- 用可读技能文件+物理仿真器，让系统自修正设计流程。
- 同类任务成功率从38%升至74%，物理符合率提升至87%。
- 无需重训练模型，适合光学设计初学者与自动化研发。
超表面逆向设计能实现复杂光学功能，但将目标光学响应转化为可执行的优化代码仍需深厚的计算电磁学与求解器软件工程知识。我们提出一种自演化智能体框架，通过耦合编码智能体、可读技能文件和确定性物理评估器，降低这一门槛。该框架不更新模型权重，而是根据求解器反馈修订技能文件，同时保持基础模型与可微分求解器不变。在多类型基准测试中，技能演化使同类任务成功率从38%提升至74%，物理准则满足率从0.51增至0.87，平均尝试次数从4.10降至2.30。在两个新类型家族中，成功率分别维持在0.92→0.90和0.20→0.90。技能演化为自主化、可及的逆向设计工作流提供了可行路径。
原文摘要 · Abstract (English)
Metasurface inverse design can realize complex optical functionality, but turning a target optical response into executable optimization code still requires substantial expertise in computational electromagnetics and solver-specific software engineering. We present a self-evolving agentic framework that lowers this barrier by coupling a coding agent, explicit human-readable skill files, and a deterministic physics-based evaluator. Rather than updating model weights, it revises the skill files from solver-grounded feedback, while the base model and differentiable solver, which provides the physics simulation and gradients, stay fixed. On a multi-type benchmark, skill evolution raises same-type task success from 38\% to 74\%, the fraction of physical criteria met from 0.51 to 0.87, and reduces average attempts from 4.10 to 2.30. On two new-type families, success holds near ceiling on one (0.92 to 0.90) and rises from 0.20 to 0.90 on the other. Skill evolution offers a practical path toward autonomous and accessible inverse-design workflows.
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