MM-BrowseComp: A Comprehensive Benchmark for Multimodal Browsing Agents
Authors: name: Shilong Li, name: Xingyuan Bu, name: Wenjie Wang, name: Jiaheng Liu, name: Jun Dong, name: Haoyang He, name: Hao Lu, name: Haozhe Zhang, name: Chenchen Jing, name: Zhen Li, name: Chuanhao Li, name: Jiayi Tian, name: Chenchen Zhang, name: Tianhao Peng, name: Yancheng He, name: Jihao Gu, name: Yuanxing Zhang, name: Jian Yang, name: Ge Zhang, name: Wenhao Huang, name: Wangchunshu Zhou, name: Zhaoxiang Zhang, name: Ruizhe Ding, name: Shilei Wen

AI agents with advanced reasoning and tool use capabilities have demonstrated
impressive performance in web browsing for deep search. While existing
benchmarks such as BrowseComp evaluate these browsing abilities, they primarily
focus on textual information, overlooking the prevalence of multimodal content.
To bridge this gap, we introduce MM-BrowseComp, a novel benchmark comprising
224 challenging, hand-crafted questions specifically designed to assess agents'
multimodal retrieval and reasoning capabilities. These questions often
incorporate images in prompts, and crucial information encountered during the
search and reasoning process may also be embedded within images or videos on
webpages. Consequently, methods relying solely on text prove insufficient for
our benchmark. Additionally, we provide a verified checklist for each question,
enabling fine-grained analysis of multimodal dependencies and reasoning paths.
Our comprehensive evaluation of state-of-the-art models on MM-BrowseComp
reveals that even top models like OpenAI o3 with tools achieve only 29.02\%
accuracy, highlighting the suboptimal multimodal capabilities and lack of
native multimodal reasoning in current models.