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arXiv AI By Lijie Ding, Changwoo Do

NeutronGym: Physics-Graded Neutron Instrument Design for LLM Agents

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NeutronGym is the first executable environment that lets language‑model agents design neutron instruments, using tools that validate their builds, McStas ray‑tracing for simulation, and a level‑resolved grading ladder that evaluates syntax, runtime, structure, and science without an LLM judge. The platform provides procedural families of instrument layouts with held‑out parameter regimes and a curated set of 16 tasks from published instruments, called McStasBench, which includes memorization probes and a sandbox. Experiments show that while seven models can reproduce at most seven of the 16 tasks and none reaches a reference design, reinforcement learning can dramatically improve performance—e.g., Qwen3‑8B’s success rate jumps from 11% to 77% on held‑out instances—highlighting the ladder’s importance for partial credit and the potential of RL to match classical optimizers under realistic simulation budgets.

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