WebWatcher: Breaking New Frontier of Vision-Language Deep Research Agent
Web agents such as Deep Research have demonstrated superhuman cognitive
abilities, capable of solving highly challenging information-seeking problems.
However, most research remains primarily text-centric, overlooking visual
information in the real world. This makes multimodal Deep Research highly
challenging, as such agents require much stronger reasoning abilities in
perception, logic, knowledge, and the use of more sophisticated tools compared
to text-based agents. To address this limitation, we introduce WebWatcher, a
multi-modal Agent for Deep Research equipped with enhanced visual-language
reasoning capabilities. It leverages high-quality synthetic multimodal
trajectories for efficient cold start training, utilizes various tools for deep
reasoning, and further enhances generalization through reinforcement learning.
To better evaluate the capabilities of multimodal agents, we propose
BrowseComp-VL, a benchmark with BrowseComp-style that requires complex
information retrieval involving both visual and textual information.
Experimental results show that WebWatcher significantly outperforms proprietary
baseline, RAG workflow and open-source agents in four challenging VQA
benchmarks, which paves the way for solving complex multimodal
information-seeking tasks.
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Xinyu Geng
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2025-08-13T07:12:48.684Z
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Geng Xinyu
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Ornamentt
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Peng Xia
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2025-08-13T07:12:38.450Z
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Peng Xia
user
richardxp888
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Zhen Zhang
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2025-08-13T07:20:47.118Z
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ZhenZhang
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zhzhen23
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Xinyu Wang
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2025-08-14T13:38:49.638Z
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Wang Xinyu
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oriuta
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Qiuchen Wang
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2025-08-18T06:59:11.585Z
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QiuchenWang
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autumncc
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Ruixue Ding
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Chenxi Wang
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Jialong Wu
claimed_verified
2025-08-13T07:12:43.643Z
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Jialong Wu
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callanwu
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Yida Zhao
claimed_verified
2025-08-13T07:12:34.963Z
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Yida Zhao
user
zhaoyd
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Kuan Li
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Yong Jiang
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Pengjun Xie
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Fei Huang
689c0152fab6fdd2e52ac86a
Jingren Zhou
WebWatcher: Breaking New Frontier of Vision-Language Deep Research Agent
Web agents such as Deep Research have demonstrated superhuman cognitive
abilities, capable of solving highly challenging information-seeking problems.
However, most research remains primarily text-centric, overlooking visual
information in the real world. This makes multimodal Deep Research highly
challenging, as such agents require much stronger reasoning abilities in
perception, logic, knowledge, and the use of more sophisticated tools compared
to text-based agents. To address this limitation, we introduce WebWatcher, a
multi-modal Agent for Deep Research equipped with enhanced visual-language
reasoning capabilities. It leverages high-quality synthetic multimodal
trajectories for efficient cold start training, utilizes various tools for deep
reasoning, and further enhances generalization through reinforcement learning.
To better evaluate the capabilities of multimodal agents, we propose
BrowseComp-VL, a benchmark with BrowseComp-style that requires complex
information retrieval involving both visual and textual information.
Experimental results show that WebWatcher significantly outperforms proprietary
baseline, RAG workflow and open-source agents in four challenging VQA
benchmarks, which paves the way for solving complex multimodal
information-seeking tasks.
WebWatcher, a multimodal agent with enhanced visual-language reasoning, outperforms existing agents in complex visual and textual information retrieval tasks using synthetic trajectories and reinforcement learning.
multimodal visual-language reasoning high-quality synthetic multimodal trajectories reinforcement learning BrowseComp-VL VQA benchmarks