WebWatcher: Breaking New Frontier of Vision-Language Deep Research Agent
Authors: name: Xinyu Geng, name: Peng Xia, name: Zhen Zhang, name: Xinyu Wang, name: Qiuchen Wang, name: Ruixue Ding, name: Chenxi Wang, name: Jialong Wu, name: Yida Zhao, name: Kuan Li, name: Yong Jiang, name: Pengjun Xie, name: Fei Huang, name: Jingren Zhou
Organization: Alibaba-NLP

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.