@inproceedings{chen-etal-2026-omniphys,
title = "{O}mni{P}hys: A Unified Multimodal Benchmark for Physics Understanding and Generation from {C}hinese Educational Corpora",
author = "Chen, Hao and
Lin, Yumin and
Yushanjiang, Nadila and
Lin, Xin and
Zhang, Min",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.1801/",
pages = "38481--38498",
ISBN = "979-8-89176-462-0",
abstract = "Multimodal Large Language Models (MLLMs) have demonstrated strong abilities in solving diverse visual and textual reasoning tasks. However, their development in the physics domain is significantly hindered by the lack of a comprehensive benchmark. To fill this gap, we introduce \textbf{OmniPhys}, a large-scale benchmark for multimodal physics understanding and reasoning, covering middle school through university-level problems from Chinese Educational Corpora. OmniPhys consists of \textbf{15,246} questions and \textbf{19,850} images, accompanied by detailed annotations that support fine-grained analysis of reasoning processes and knowledge usage. Beyond conventional evaluation, OmniPhys is a benchmark that systematically evaluates multimodal outputs in physics domain, including models' ability to generate structured physics diagrams, which constitute a fundamental component of authentic physics problem solving."
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%0 Conference Proceedings
%T OmniPhys: A Unified Multimodal Benchmark for Physics Understanding and Generation from Chinese Educational Corpora
%A Chen, Hao
%A Lin, Yumin
%A Yushanjiang, Nadila
%A Lin, Xin
%A Zhang, Min
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F chen-etal-2026-omniphys
%X Multimodal Large Language Models (MLLMs) have demonstrated strong abilities in solving diverse visual and textual reasoning tasks. However, their development in the physics domain is significantly hindered by the lack of a comprehensive benchmark. To fill this gap, we introduce OmniPhys, a large-scale benchmark for multimodal physics understanding and reasoning, covering middle school through university-level problems from Chinese Educational Corpora. OmniPhys consists of 15,246 questions and 19,850 images, accompanied by detailed annotations that support fine-grained analysis of reasoning processes and knowledge usage. Beyond conventional evaluation, OmniPhys is a benchmark that systematically evaluates multimodal outputs in physics domain, including models’ ability to generate structured physics diagrams, which constitute a fundamental component of authentic physics problem solving.
%U https://aclanthology.org/2026.findings-emnlp.1801/
%P 38481-38498
Markdown (Informal)
[OmniPhys: A Unified Multimodal Benchmark for Physics Understanding and Generation from Chinese Educational Corpora](https://aclanthology.org/2026.findings-emnlp.1801/) (Chen et al., Findings 2026)
ACL