WebChain: A Large-Scale Human-Annotated Dataset of Real-World Web Interaction Traces
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866918445031882752 |
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| author | Fan, Sicheng Wan, Rui Leng, Yifei Liang, Gaoning Ling, Li Shang, Yanyi Kong, Dehan |
| author_facet | Fan, Sicheng Wan, Rui Leng, Yifei Liang, Gaoning Ling, Li Shang, Yanyi Kong, Dehan |
| contents | We introduce WebChain, the largest open-source dataset of human-annotated trajectories on real-world websites, designed to accelerate reproducible research in web agents. It contains 31,725 trajectories and 318k steps, featuring a core Triple Alignment of visual, structural, and action data to provide rich, multi-modal supervision. The data is collected via a scalable pipeline that ensures coverage of complex, high-value tasks often missed by synthetic methods. Leveraging this dataset, we propose a Dual Mid-Training recipe that decouples spatial grounding from planning, achieving state-of-the-art performance on our proposed WebChainBench and other public GUI benchmarks. Our work provides the data and insights necessary to build and rigorously evaluate the next generation of scalable web agents. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_05295 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | WebChain: A Large-Scale Human-Annotated Dataset of Real-World Web Interaction Traces Fan, Sicheng Wan, Rui Leng, Yifei Liang, Gaoning Ling, Li Shang, Yanyi Kong, Dehan Artificial Intelligence Computer Vision and Pattern Recognition We introduce WebChain, the largest open-source dataset of human-annotated trajectories on real-world websites, designed to accelerate reproducible research in web agents. It contains 31,725 trajectories and 318k steps, featuring a core Triple Alignment of visual, structural, and action data to provide rich, multi-modal supervision. The data is collected via a scalable pipeline that ensures coverage of complex, high-value tasks often missed by synthetic methods. Leveraging this dataset, we propose a Dual Mid-Training recipe that decouples spatial grounding from planning, achieving state-of-the-art performance on our proposed WebChainBench and other public GUI benchmarks. Our work provides the data and insights necessary to build and rigorously evaluate the next generation of scalable web agents. |
| title | WebChain: A Large-Scale Human-Annotated Dataset of Real-World Web Interaction Traces |
| topic | Artificial Intelligence Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2603.05295 |