{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/phyx-does-your-model-have-the-wits-for","title":"PhyX: Does Your Model Have the \"Wits\" for Physical Reasoning?","arxiv_id":"2505.15929","date":"2025-05-21","proceeding":null,"authors":["Hui Shen","Taiqiang Wu","Qi Han","Yunta Hsieh","Jizhou Wang","Yuyue Zhang","Yuxin Cheng","Zijian Hao","Yuansheng Ni","Xin Wang","Zhongwei Wan","Kai Zhang","Wendong Xu","Jing Xiong","Ping Luo","Wenhu Chen","Chaofan Tao","Zhuoqing Mao","Ngai Wong"],"abstract":"Existing benchmarks fail to capture a crucial aspect of intelligence: physical reasoning, the integrated ability to combine domain knowledge, symbolic reasoning, and understanding of real-world constraints. To address this gap, we introduce PhyX: the first large-scale benchmark designed to assess models capacity for physics-grounded reasoning in visual scenarios. PhyX includes 3K meticulously curated multimodal questions spanning 6 reasoning types across 25 sub-domains and 6 core physics domains: thermodynamics, electromagnetism, mechanics, modern physics, optics, and wave\\&acoustics. In our comprehensive evaluation, even state-of-the-art models struggle significantly with physical reasoning. GPT-4o, Claude3.7-Sonnet, and GPT-o4-mini achieve only 32.5\\%, 42.2\\%, and 45.8\\% accuracy respectively-performance gaps exceeding 29\\% compared to human experts. Our analysis exposes critical limitations in current models: over-reliance on memorized disciplinary knowledge, excessive dependence on mathematical formulations, and surface-level visual pattern matching rather than genuine physical understanding. We provide in-depth analysis through fine-grained statistics, detailed case studies, and multiple evaluation paradigms to thoroughly examine physical reasoning capabilities. To ensure reproducibility, we implement a compatible evaluation protocol based on widely-used toolkits such as VLMEvalKit, enabling one-click evaluation.","url_abs":"https://arxiv.org/abs/2505.15929v1","url_pdf":"https://arxiv.org/pdf/2505.15929v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"phyx-does-your-model-have-the-wits-for","repo_url":"https://github.com/open-compass/vlmevalkit","is_official":0,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null},{"paper_slug":"phyx-does-your-model-have-the-wits-for","repo_url":"https://github.com/NastyMarcus/PhyX","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"phyx-does-your-model-have-the-wits-for","repo_url":"https://github.com/evolvinglmms-lab/lmms-eval","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"NOASSERTION"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2505.15929","atlas_url":"https://app.syntology.ai/?focus=2505.15929","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2505.15929"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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