VisSearch-Bench: Evaluating Long Horizon Visual Search in Multimodal Agents

Wu Zhengxian, Qianhao Nie, Zhuohong Chen, Junjie Gao, Yuyao Zhang, Yunyao Yu, Hangrui Xu, Cheng Cheng, Guiyu Ma, Jin Zhang, Xingzhong Xu, Kai Yang, Haoqian Wang

VisSearch-Bench: Evaluating Long Horizon Visual Search in Multimodal Agents: 22 upvotes on Hugging Face Daily Papers, #11 of 32 papers on 2026-11-06. Day-by-day upvote history.

Upvotes over time

It had 17 upvotes by its first day, 20 upvotes by the next day, 22 upvotes by day 3, 22 upvotes by day 7, and has 22 now. It ranked #62 of 207 papers in its week and #96 of 340 in its month. Its code repository has 112 GitHub stars.

Multimodal agents use web and image tools to answer questions beyond their internal knowledge. Some search tasks use images mainly to identify an initial entity, then rely on text retrieval. These tasks offer limited insight into how agents connect information across images, objects, and search steps. We therefore focus on visual context management. This involves organizing and using visual information and linking it to other findings during search. Visually intensive tasks require agents to combine object appearance, local details, or spatial relations with external information. We introduce VisSearch-Bench, with 204 human-written tasks across 7 main domains and 25 subdomains. It covers Set-wise Visual Search, Sequential Visual Search, Hybrid Wide-Deep Search, Competitive Visual Disambiguation, and Visual-Structure-Guided Search. Each task provides a reference answer, traceable evidence, and scoring rules. Accuracy measures complete task success, while Process Score measures the correctness and completeness of individual results and subquestion answers. Across 16 multimodal models, the best system fully solves only 26.47% of tasks, even with search tools. These results highlight the challenge of linking visual evidence with search results to produce complete answers.

Paper page on Hugging Face · arXiv

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