OmniPhys: A Unified Multimodal Benchmark for Physics Understanding and Generation

ACL ARR 2026 January Submission3703 Authors

Keywords: Multimodal Large Language Models, Physics Reasoning, Benchmark Dataset of Physics, Multimodal Generation

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 OmniPhys, a large-scale benchmark for multimodal physics understanding and reasoning, covering middle school through university-level problems. OmniPhys consists of 13,146 questions and 17,567 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. Extensive evaluations reveal critical gaps in the capabilities of current MLLMs, especially in complex reasoning and visual generation. To address this, we release OmniPhys to serve as a foundational resource for advancing multimodal intelligence in physics and scientific domains.

Paper Type: Long

Research Area: Multimodality and Language Grounding to Vision, Robotics and Beyond

Research Area Keywords: vision question answering, cross-modal application

Contribution Types: Model analysis & interpretability, Data resources, Data analysis

Languages Studied: Chinese

Submission Number: 3703