Problem
Research questions and friction points this paper is trying to address.
Language Models
Nanophotonic Design
Electromagnetic Analysis
Numerical Feedback
HALO: A Physics-Aware LLM Agent Framework for Nanophotonic Design
🤖 AI Summary
本文提出HALO框架，通过结合语言模型规划器与物理模拟等方法解决纳米光子设计中目标到设计的转换问题，并通过对比实验分析了不同配置下的性能。
📝 Abstract
Language models have recently been applied to nanophotonic design, but it remains unclear whether they can reliably translate optical objectives into simulation-ready designs, execute electromagnetic analysis, and revise decisions from numerical feedback. We introduce HALO, a physics-aware framework that couples language-model planners with typed design specifications, electromagnetic simulation, diagnostic evaluation, and optional reuse of prior failure trajectories in an iterative design loop. We further introduce HALO-Bench, a 52-task benchmark spanning lab-derived, paper-derived, and open-ended nanophotonic design tasks under a shared evaluation protocol. We compare three planner configurations: a Fixed Structured Workflow, an Autonomous Structured Agent using the same simulation interface, and an Autonomous Coding Agent that directly writes and executes simulation code. The Fixed Structured Workflow is the most token-efficient and exhibits no observed code- or path-level failures, while autonomous coding can achieve higher task success with stronger models at the cost of additional operational failures. We also study reuse of prior failed trajectories. On targeted multi-round tasks, retrieved failure feedback reduces both iterations to first success and total token use. These results clarify the tradeoffs between explicit interfaces, autonomous execution, and reusable design experience in scientific agents.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Physics-Aware Framework
Nanophotonic Design
Iterative Design Loop
Simulation-Ready Designs
Failure Trajectories Reuse
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