PhysMent: An Interactive Approach For LLM Reasoning In Physics Problems
Large language models (LLMs) perform strongly on static science benchmarks, yet their ability to reason about the physical world through active experimentation remains poorly understood. We introduce PhysMent, a benchmark that evaluates LLM physical reasoning via iterative, toolmediated interaction with a MuJoCo physics simulator. Unlike static benchmarks that supply all quantities upfront, PhysMent requires models to discover information by applying forces, querying object states, advancing time, and modifying scene geometry before answering. The benchmark comprises 105 scenes of classical mechanics, organized across four difficulty regimes (Easy/Hard and Single/Multi), three scene modal...
| Authors | Joseph Chan, Utkarsh Jha, Xiyin Yang et al. |
|---|---|
| Primary category | cs.CL |
| Published | 7 July 2026 |
| Last updated at source | 7 July 2026 |
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