RBench-V: A Primary Assessment for Visual Reasoning Models with Multi-modal Outputs

• URL: http://arxiv.org/abs/2505.16770v2
• Date: Fri, 23 May 2025 15:40:22 GMT
• Title: RBench-V: A Primary Assessment for Visual Reasoning Models with Multi-modal Outputs
• Authors: Meng-Hao Guo, Xuanyu Chu, Qianrui Yang, Zhe-Han Mo, Yiqing Shen, Pei-lin Li, Xinjie Lin, Jinnian Zhang, Xin-Sheng Chen, Yi Zhang, Kiyohiro Nakayama, Zhengyang Geng, Houwen Peng, Han Hu, Shi-Min Hu,
• Abstract summary: We present a benchmark, dubbed RBench-V, designed to assess models' vision-indispensable reasoning abilities.<n>Unlike previous benchmarks that typically specify certain input modalities, RBench-V presents problems centered on multi-modal outputs.<n>Even the best-performing model, o3, achieves only 25.8% accuracy on RBench-V, far below the human score of 82.3%.
• Score: 40.96433915889983
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The rapid advancement of native multi-modal models and omni-models, exemplified by GPT-4o, Gemini, and o3, with their capability to process and generate content across modalities such as text and images, marks a significant milestone in the evolution of intelligence. Systematic evaluation of their multi-modal output capabilities in visual thinking processes (also known as multi-modal chain of thought, M-CoT) becomes critically important. However, existing benchmarks for evaluating multi-modal models primarily focus on assessing multi-modal inputs and text-only reasoning while neglecting the importance of reasoning through multi-modal outputs. In this paper, we present a benchmark, dubbed RBench-V, designed to assess models' vision-indispensable reasoning abilities. To construct RBench-V, we carefully hand-pick 803 questions covering math, physics, counting, and games. Unlike previous benchmarks that typically specify certain input modalities, RBench-V presents problems centered on multi-modal outputs, which require image manipulation such as generating novel images and constructing auxiliary lines to support the reasoning process. We evaluate numerous open- and closed-source models on RBench-V, including o3, Gemini 2.5 Pro, Qwen2.5-VL, etc. Even the best-performing model, o3, achieves only 25.8% accuracy on RBench-V, far below the human score of 82.3%, highlighting that current models struggle to leverage multi-modal reasoning. Data and code are available at https://evalmodels.github.io/rbenchv

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