Multimodal Large Language Models (MLLMs) have demonstrated remarkable capabilities in diverse reasoning tasks, yet their application to complex physics reasoning remains underexplored. Physics reasoning presents unique challenges, requiring grounding in physical conditions and the interpretation of multimodal information. Current physics benchmarks are limited, often focusing on text-only inputs or solely on problem-solving, thereby overlooking the critical intermediate steps of variable identification and process formulation. To address these limitations, we introduce PhysicsArena, the first multimodal physics reasoning benchmark designed to holistically evaluate MLLMs across three critical dimensions: variable identification, physical process formulation, and solution derivation. PhysicsArena aims to provide a comprehensive platform for assessing and advancing the multimodal physics reasoning abilities of MLLMs.
View on arXiv@article{dai2025_2505.15472,
title={ PhysicsArena: The First Multimodal Physics Reasoning Benchmark Exploring Variable, Process, and Solution Dimensions },
author={ Song Dai and Yibo Yan and Jiamin Su and Dongfang Zihao and Yubo Gao and Yonghua Hei and Jungang Li and Junyan Zhang and Sicheng Tao and Zhuoran Gao and Xuming Hu },
journal={arXiv preprint arXiv:2505.15472},
year={ 2025 }
}