Speaker
Description
Instrument design requires searching a large space of discrete and continuous choices under hard resource constraints, and the effort of setting up such optimizations increasingly limits how far they can scale. We investigate the use of large language models (LLMs) for physics instrument design and compare their performance with reinforcement learning (RL). Using only prompting, the models receive the design constraints and summaries of previously evaluated configurations, then propose complete detector layouts that are assessed with the same simulators and reward functions used for RL optimization. We evaluate four open-weight and proprietary LLMs on two detector-design benchmarks: longitudinal segmentation of a sampling calorimeter and placement and granularity optimization of tracking stations in a magnetic spectrometer. Although RL achieves the strongest final designs, the LLMs consistently generate valid, resource-aware and physically meaningful configurations without task-specific training. We also investigate a hybrid approach in which LLM proposals are refined using a trust-region optimizer, which essentially closes the performance gap with RL on the spectrometer benchmark. These results suggest a role for LLMs as meta-planners in automated instrument-design workflows, generating and organizing design hypotheses while dedicated reward-driven optimizers carry out detailed refinement.