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Automated Trajectory Generation: To provide robust tool-use capabilities, we developed an automated pipeline to generate high-quality, multi-step reasoning trajectories. These trajectories, which are grounded in actual tool-use behavior and reflect procedural decision-making, are used for efficient cold-start training and further optimization via reinforcement learning. The agent is equipped with several tools, including Web Image Search, Web Text Search, Webpage Visit, Code Interpreter, and an internal OCR tool.
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Superior Performance: WebWatcher significantly outperforms proprietary baselines, RAG workflows, and other open-source agents across four challenging VQA benchmarks: Humanity's Last Exam (HLE)-VL, BrowseComp-VL, LiveVQA, and MMSearch. The WebWatcher-32B model, in particular, achieves an average score of 18.2% on HLE, surpassing the GPT-4o-based OmniSearch baseline. It also achieves top-tier performance on LiveVQA (58.7%) and MMSearch (55.3%), demonstrating stable and superior results on demanding, real-world visual search benchmarks.
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Complex Reasoning (HLE-VL): On the Human Life Exam (HLE-VL), a benchmark for multi-step complex reasoning, WebWatcher achieved a commanding lead with a Pass@1 score of 13.6%, substantially outperforming representative models including GPT-4o (9.8%), Gemini2.5-flash (9.2%), and Qwen2.5-VL-72B (8.6%).
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Information Retrieval (MMSearch): In the MMSearch evaluation, WebWatcher demonstrated exceptional retrieval accuracy with a Pass@1 score of 55.3%, significantly surpassing Gemini2.5-flash (43.9%) and GPT-4o (24.1%), showcasing superior precision in retrieval tasks and robust information aggregation capabilities in complex scenarios.
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Knowledge-Retrieval Integration (LiveVQA): On the LiveVQA benchmark, WebWatcher achieved a Pass@1 score of 58.7%, outperforming Gemini2.5-flash (41.3%), Qwen2.5-VL-72B (35.7%), and GPT-4o (34.0%).
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Information Optimization and Aggregation (BrowseComp-VL): On BrowseComp-VL, the most comprehensively challenging benchmark, WebWatcher dominated with an average score of 27.0%, more than doubling the performance of mainstream models including GPT-4o (13.4%), Gemini2.5-flash (13.0%), and Claude-3.7 (11.2%).
You can download WebWatcher via Hugging Face
🤗 HuggingFace.
1@article{geng2025webwatcher,
2 title={WebWatcher: Breaking New Frontiers of Vision-Language Deep Research Agent},
3 author={Geng, Xinyu and Xia, Peng and Zhang, Zhen and Wang, Xinyu and Wang, Qiuchen and Ding, Ruixue and Wang, Chenxi and Wu, Jialong and Zhao, Yida and Li, Kuan and others},
4 journal={arXiv preprint arXiv:2508.05748},
5 year={2025}
6}
