Academic

OPTIAGENT: A Physics-Driven Agentic Framework for Automated Optical Design

arXiv:2602.23761v1 Announce Type: new Abstract: Optical design is the process of configuring optical elements to precisely manipulate light for high-fidelity imaging. It is inherently a highly non-convex optimization problem that relies heavily on human heuristic expertise and domain-specific knowledge. While Large Language Models (LLMs) possess extensive optical knowledge, their capabilities in leveraging the knowledge in designing lens system remain significantly constrained. This work represents the first attempt to employ LLMs in the field of optical design. We bridge the expertise gap by enabling users without formal optical training to successfully develop functional lens systems. Concretely, we curate a comprehensive dataset, named OptiDesignQA, which encompasses both classical lens systems sourced from standard optical textbooks and novel configurations generated by automated design algorithms for training and evaluation. Furthermore, we inject domain-specific optical expertis

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Yuyu Geng, Lei Sun, Yao Gao, Xinxin Hu, Zhonghua Yi, Xiaolong Qian, Weijian Hu, Jian Bai, Kaiwei Wang · · 1 min read · 12 views
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arXiv:2602.23761v1 Announce Type: new Abstract: Optical design is the process of configuring optical elements to precisely manipulate light for high-fidelity imaging. It is inherently a highly non-convex optimization problem that relies heavily on human heuristic expertise and domain-specific knowledge. While Large Language Models (LLMs) possess extensive optical knowledge, their capabilities in leveraging the knowledge in designing lens system remain significantly constrained. This work represents the first attempt to employ LLMs in the field of optical design. We bridge the expertise gap by enabling users without formal optical training to successfully develop functional lens systems. Concretely, we curate a comprehensive dataset, named OptiDesignQA, which encompasses both classical lens systems sourced from standard optical textbooks and novel configurations generated by automated design algorithms for training and evaluation. Furthermore, we inject domain-specific optical expertise into the LLM through a hybrid objective of full-system synthesis and lens completion. To align the model with optical principles, we employ Group Relative Policy Optimization Done Right (DrGRPO) guided by Optical Lexicographic Reward for physics-driven policy alignment. This reward system incorporates structural format rewards, physical feasibility rewards, light-manipulation accuracy, and LLM-based heuristics. Finally, our model integrates with specialized optical optimization routines for end-to-end fine-tuning and precision refinement. We benchmark our proposed method against both traditional optimization-based automated design algorithms and LLM counterparts, and experimental results show the superiority of our method.

Executive Summary

The article presents OptiAgent, a physics-driven agentic framework for automated optical design. By leveraging Large Language Models (LLMs) and curating a comprehensive dataset, OptiAgent enables users without formal optical training to develop functional lens systems. The framework employs a hybrid objective of full-system synthesis and lens completion, guided by a Group Relative Policy Optimization Done Right (DrGRPO) method that incorporates optical lexicon rewards. Experimental results demonstrate the superiority of OptiAgent over traditional optimization-based automated design algorithms and LLM counterparts. This innovation has significant implications for the field of optical design, particularly in the development of novel lens systems and the democratization of optical expertise.

Key Points

  • ▸ OptiAgent employs LLMs to bridge the expertise gap in optical design
  • ▸ The framework utilizes a comprehensive dataset, OptiDesignQA, for training and evaluation
  • ▸ DrGRPO and optical lexicon rewards facilitate physics-driven policy alignment

Merits

Strength

The article presents a novel and effective approach to automated optical design, leveraging LLMs and domain-specific knowledge. The framework's ability to enable users without formal optical training to develop functional lens systems is a significant advantage.

Demerits

Limitation

The article does not extensively discuss the potential limitations and risks associated with the use of LLMs in optical design, particularly in terms of error propagation and the reliability of the generated lens systems.

Expert Commentary

The article presents a significant innovation in the field of optical design, leveraging LLMs and domain-specific knowledge to enable users without formal training to develop functional lens systems. The framework's ability to facilitate physics-driven policy alignment is a notable strength. However, the article's lack of extensive discussion on the limitations and risks associated with LLMs in optical design is a notable omission. Furthermore, the article's findings raise important questions about the interpretability and transparency of the OptiAgent framework, which will require further investigation.

Recommendations

  • ✓ Future research should focus on developing more robust and transparent methods for LLM-driven optical design, including techniques for error propagation and reliability analysis.
  • ✓ The article's findings have significant implications for the development of AI-powered design tools in sensitive fields, and policymakers should take note of the potential risks and benefits associated with these technologies.

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