[Submitted on 7 Jul 2026]
Title:PhysMent: An Interactive Approach For LLM Reasoning In Physics Problems
View a PDF of the paper titled PhysMent: An Interactive Approach For LLM Reasoning In Physics Problems, by Joseph Chan and 8 other authors
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Abstract: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 modalities (standard, object creation, hidden objects), and a scene-manipulation category, evaluated with a six-dimensional scoring framework. Results show that current models perform reasonably well on qualitative single-concept tasks (up to 80% accuracy) but degrade substantially on quantitative tasks that demand precise, multi-step experimental procedures: most models fall below 30% on the hardest single-concept category, where the bottleneck is procedural (adaptive multi-step tool use) rather than conceptual load. Across the seven models, accuracy ranges from 25% to 67%, with failures due to premature answer submission, inefficient exploration, and inconsistent grounding in simulator feedback rather than conceptual gaps.
Subjects:
Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2609.13152 [cs.CL]
(or arXiv:2609.13152v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2609.13152
arXiv-issued DOI via DataCite
Submission history
From: Stefano Saravalle [view email] [v1] Tue, 7 Jul 2026 10:48:11 UTC (979 KB)
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