This paper introduces MMMU-Pro, a robust version of the Massive Multi-discipline Multimodal Understanding and Reasoning (MMMU) benchmark. MMMU-Pro rigorously assesses multimodal models' true understanding and reasoning capabilities through a three-step process based on MMMU: (1) filtering out questions answerable by text-only models, (2) augmenting candidate options, and (3) introducing a vision-only input setting where questions are embedded within images. This setting challenges AI to truly "see" and "read" simultaneously, testing a fundamental human cognitive skill of seamlessly integrating visual and textual information. Results show that model performance is substantially lower on MMMU-Pro than on MMMU, ranging from 16.8% to 26.9% across models. We explore the impact of OCR prompts and Chain of Thought (CoT) reasoning, finding that OCR prompts have minimal effect while CoT generally improves performance. MMMU-Pro provides a more rigorous evaluation tool, closely mimicking real-world scenarios and offering valuable directions for future research in multimodal AI.

本文提出了MMMU-Pro，一种强大的多学科多模态理解和推理基准，旨在解决现有基准无法有效评估多模态模型真实理解与推理能力的问题。该研究通过三步流程改进评估标准，发现模型在MMMU-Pro上的表现明显低于MMMU，强调了新基准在现实场景中的应用及其对多模态人工智能未来研究的重要影响。

MMMU-Pro：更强大的多学科多模态理解基准