P hysics A rena: The First Multimodal Physics Reasoning Benchmark Exploring Variable, Process, and Solution Dimensions (2025)

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.

## introduction multimodal large language models (mllms) have recently demonstrated remarkable capabilities across a diverse range of domains @xcite @xcite . their proficiency in processing and integrating information from various modalities has unlocked significant potential @xcite . notably, the reasoning abilities inherent in the underlying llms have fueled advancements in multimodal reasoning tasks. this synergy is particularly beneficial in complex, real-world scenarios such as education, where understanding and reasoning about multimodal information are paramount. areas like mathematical problem-solving and code generation have already seen substantial progress, showcasing the power of mllms in tackling structured reasoning challenges @xcite @xcite . despite these advancements, the domain of physics reasoning remains relatively underexplored within the mllm research landscape. physics presents a unique and arguably more intricate reasoning setting compared to mathematics or coding. effective physics reasoning necessitates not only logical deduction but also a deep understanding grounded in real-world physical laws and theorems. furthermore, the reasoning process is often tightly constrained by objective physical conditions depicted visually or described textually. this inherent complexity, involving the interplay between abstract principles and concrete, often multimodal, scenarios, necessitates a dedicated benchmark capable of rigorously evaluating the physics reasoning capabilities of modern mllms @xcite . current benchmarks designed for physics reasoning suffer from significant limitations as follows. ❶ many existing efforts @xcite primarily focus on text-only settings, failing to capture the crucial interplay with multimodal information that characterizes real-world physics problems (e.g., interpreting diagrams, graphs, or experimental setups), as shown in figure 1 (a) . ❷ other benchmarks @xcite , even if multimodal, tend to concentrate solely on the problem-solving aspect -predicting the final solution or answer, as shown in figure 1 @xcite . this overlooks the critical intermediate steps inherent in physics reasoning: identifying relevant variables from the problem context and formulating the correct physical process or sequence of principles required to reach the solution. a comprehensive evaluation of physics reasoning capabilities, therefore, requires modeling the dynamic reasoning process from its inception, encompassing these vital variable and process stages. to bridge this gap, we introduce physic-sarena, the first benchmark specifically designed to comprehensively evaluate multimodal physics reasoning across three crucial dimensions: variable identification, process formulation, and solution derivation. as illustrated in figure 1 (c), physicsarena provides a structured environment with problems presented multimodally, demanding that models demonstrate understanding throughout the entire reasoning pipeline, not just at the final output stage. by dissecting the reasoning task into these three interconnected dimensions, our benchmark offers a more granular and insightful assessment of mllm capabilities in this challenging domain. we have rigorously evaluated a suite of representative, state-of-the-art mllms using physicsarena. our contributions can be summarized as follows: ## conclusion we introduced physicsarena, the first multimodal physics reasoning benchmark designed to holistically evaluate mllms across three critical dimensions: variable identification, process formulation, and solution derivation, with over 5,000 multimodal instances. our extensive experiments reveal that while progress has been made, current models still exhibit modest performance, esp. process formulation and complex solution derivation, highlighting a significant gap towards agi-level scientific reasoning @xcite .