@inproceedings{dai-etal-2025-physicsarena,
    title = "{P}hysics{A}rena: The First Multimodal Physics Reasoning Benchmark Exploring Variable, Process, and Solution Dimensions",
    author = "Dai, Song  and
      Yan, Yibo  and
      Su, Jiamin  and
      Dongfang, Zihao  and
      Gao, Yubo  and
      Hei, Yonghua  and
      Li, Jungang  and
      Zhang, Junyan  and
      Tao, Sicheng  and
      Gao, Zhuoran  and
      Hu, Xuming",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/name-variant-enfa-fane/2025.findings-emnlp.937/",
    doi = "10.18653/v1/2025.findings-emnlp.937",
    pages = "17290--17316",
    ISBN = "979-8-89176-335-7",
    abstract = "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."
}