[2602.02185] Vision-DeepResearch Benchmark: Rethinking Visual and Textual Search for Multimodal Large Language Models

Computer Science > Computer Vision and Pattern Recognition arXiv:2602.02185 (cs) [Submitted on 2 Feb 2026 (v1), last revised 28 Feb 2026 (this version, v2)] Title:Vision-DeepResearch Benchmark: Rethinking Visual and Textual Search for Multimodal Large Language Models Authors:Yu Zeng, Wenxuan Huang, Zhen Fang, Shuang Chen, Yufan Shen, Yishuo Cai, Xiaoman Wang, Zhenfei Yin, Lin Chen, Zehui Chen, Shiting Huang, Yiming Zhao, Xu Tang, Yao Hu, Philip Torr, Wanli Ouyang, Shaosheng Cao View a PDF of the paper titled Vision-DeepResearch Benchmark: Rethinking Visual and Textual Search for Multimodal Large Language Models, by Yu Zeng and 16 other authors View PDF HTML (experimental) Abstract:Multimodal Large Language Models (MLLMs) have advanced VQA and now support Vision-DeepResearch systems that use search engines for complex visual-textual fact-finding. However, evaluating these visual and textual search abilities is still difficult, and existing benchmarks have two major limitations. First, existing benchmarks are not visual search-centric: answers that should require visual search are often leaked through cross-textual cues in the text questions or can be inferred from the prior world knowledge in current MLLMs. Second, overly idealized evaluation scenario: On the image-search side, the required information can often be obtained via near-exact matching against the full image, while the text-search side is overly direct and insufficiently challenging. To address these issues, we ...