PhysicsArena: The First Multimodal Physics Reasoning Benchmark Exploring Variable, Process, and Solution Dimensions

Fuente: arXiv
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Hauptverfasser: Dai, Song, Yan, Yibo, Su, Jiamin, Zihao, Dongfang, Gao, Yubo, Hei, Yonghua, Li, Jungang, Zhang, Junyan, Tao, Sicheng, Gao, Zhuoran, Hu, Xuming
Format: Preprint
Veröffentlicht: 2025
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author Dai, Song
Yan, Yibo
Su, Jiamin
Zihao, Dongfang
Gao, Yubo
Hei, Yonghua
Li, Jungang
Zhang, Junyan
Tao, Sicheng
Gao, Zhuoran
Hu, Xuming
author_facet Dai, Song
Yan, Yibo
Su, Jiamin
Zihao, Dongfang
Gao, Yubo
Hei, Yonghua
Li, Jungang
Zhang, Junyan
Tao, Sicheng
Gao, Zhuoran
Hu, Xuming
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2505_15472
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle PhysicsArena: The First Multimodal Physics Reasoning Benchmark Exploring Variable, Process, and Solution Dimensions
Dai, Song
Yan, Yibo
Su, Jiamin
Zihao, Dongfang
Gao, Yubo
Hei, Yonghua
Li, Jungang
Zhang, Junyan
Tao, Sicheng
Gao, Zhuoran
Hu, Xuming
Computation and Language
I.2.7; I.2.10
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.
title PhysicsArena: The First Multimodal Physics Reasoning Benchmark Exploring Variable, Process, and Solution Dimensions
topic Computation and Language
I.2.7; I.2.10
url https://arxiv.org/abs/2505.15472