LensRSI: Towards Recursive Self-Improving Optical Design Agents

Automatic lens design seeks to construct and optimize optical systems with minimal human intervention. Although numerical optimization and physics-based simulation have automated important parts of this process, agentic systems that can autonomously manage complete optical design workflows remain underexplored, and enabling such systems to improve their own design policies poses a further challenge. We study recursive policy improvement for optical design agents and introduce LensRSI, a verifier-gated framework that converts simulator-measured state–action–outcome trajectories into validated, inheritable search policies. LensRSI couples a task-solving loop with a policy-improvement loop: the agent interacts with a physics-based optical simulator, learns which actions work in different optical states, and proposes an updated design policy. To keep this process reliable, every candidate policy is evaluated independently and inherited only when it improves design quality while preserving held-out performance and physical validity. All policy comparisons restart from the same fixed initial prescriptions, so only verified search knowledge, rather than optimized lenses, is carried across rounds. Experiments on LensArenaEx, our benchmark of extreme lens-design tasks, show that LensRSI achieves smaller root-mean-square spot radii and better generalization to challenging specifications than non-evolving and limited-update alternatives under matched simulation budgets, while maintaining optical validity. Results demonstrate the potential of recursive self-improvement (RSI) to build optical design agents and we hope to inspire further research into RSI for scientific discovery. Codes and datasets will be released on acceptance.