VisBrowse-Bench: Benchmarking Visual-Native Search for Multimodal Browsing Agents
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , , , , , , , , , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2026
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866914406707757056 |
|---|---|
| author | Zhang, Zhengbo Su, Jinbo Zhou, Zhaowen Miao, Changtao Hong, Yuhan Wu, Qimeng Liu, Yumeng Wu, Feier Tian, Yihe Liang, Yuhao Shan, Zitong Xia, Wanke Zhang, Yi-Fan Zhang, Bo Li, Zhe Xiang, Shiming Yan, Ying |
| author_facet | Zhang, Zhengbo Su, Jinbo Zhou, Zhaowen Miao, Changtao Hong, Yuhan Wu, Qimeng Liu, Yumeng Wu, Feier Tian, Yihe Liang, Yuhao Shan, Zitong Xia, Wanke Zhang, Yi-Fan Zhang, Bo Li, Zhe Xiang, Shiming Yan, Ying |
| contents | The rapid advancement of Multimodal Large Language Models (MLLMs) has enabled browsing agents to acquire and reason over multimodal information in the real world. But existing benchmarks suffer from two limitations: insufficient evaluation of visual reasoning ability and the neglect of native visual information of web pages in the reasoning chains. To address these challenges, we introduce a new benchmark for visual-native search, VisBrowse-Bench. It contains 169 VQA instances covering multiple domains and evaluates the models' visual reasoning capabilities during the search process through multimodal evidence cross-validation via text-image retrieval and joint reasoning. These data were constructed by human experts using a multi-stage pipeline and underwent rigorous manual verification. We additionally propose an agent workflow that can effectively drive the browsing agent to actively collect and reason over visual information during the search process. We comprehensively evaluated both open-source and closed-source models in this workflow. Experimental results show that even the best-performing model, Claude-4.6-Opus only achieves an accuracy of 47.6%, while the proprietary Deep Research model, o3-deep-research only achieves an accuracy of 41.1%. The code and data can be accessed at: https://github.com/ZhengboZhang/VisBrowse-Bench |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_16289 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | VisBrowse-Bench: Benchmarking Visual-Native Search for Multimodal Browsing Agents Zhang, Zhengbo Su, Jinbo Zhou, Zhaowen Miao, Changtao Hong, Yuhan Wu, Qimeng Liu, Yumeng Wu, Feier Tian, Yihe Liang, Yuhao Shan, Zitong Xia, Wanke Zhang, Yi-Fan Zhang, Bo Li, Zhe Xiang, Shiming Yan, Ying Computer Vision and Pattern Recognition Artificial Intelligence The rapid advancement of Multimodal Large Language Models (MLLMs) has enabled browsing agents to acquire and reason over multimodal information in the real world. But existing benchmarks suffer from two limitations: insufficient evaluation of visual reasoning ability and the neglect of native visual information of web pages in the reasoning chains. To address these challenges, we introduce a new benchmark for visual-native search, VisBrowse-Bench. It contains 169 VQA instances covering multiple domains and evaluates the models' visual reasoning capabilities during the search process through multimodal evidence cross-validation via text-image retrieval and joint reasoning. These data were constructed by human experts using a multi-stage pipeline and underwent rigorous manual verification. We additionally propose an agent workflow that can effectively drive the browsing agent to actively collect and reason over visual information during the search process. We comprehensively evaluated both open-source and closed-source models in this workflow. Experimental results show that even the best-performing model, Claude-4.6-Opus only achieves an accuracy of 47.6%, while the proprietary Deep Research model, o3-deep-research only achieves an accuracy of 41.1%. The code and data can be accessed at: https://github.com/ZhengboZhang/VisBrowse-Bench |
| title | VisBrowse-Bench: Benchmarking Visual-Native Search for Multimodal Browsing Agents |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2603.16289 |