A Self‐Evolving Agentic Framework for Metasurface Inverse Design

ABSTRACT 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.