WebChain：一个大规模人工标注的真实网页交互轨迹数据集
WebChain: A Large-Scale Human-Annotated Dataset of Real-World Web Interaction Traces
提供带视觉-结构-动作三重对齐的真实网页交互轨迹，可用于训练和评测 GUI 智能体的空间视觉定位与规划解耦能力，WebChainBench 可作为视觉智能体定位任务的评测协议参考。
原文依据 a core Triple Alignment of visual, structural, and action data to provide rich, multi-modal supervision
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论我们提出 WebChain，这是目前最大的开源真实网站人工标注轨迹数据集，旨在加速网页智能体领域的可复现研究。该数据集包含 31,725 条轨迹和 318k 个步骤，其核心是视觉、结构与动作数据的三重对齐，从而提供丰富的多模态监督。数据通过可扩展的流水线采集，确保覆盖合成方法常常遗漏的复杂、高价值任务。基于该数据集，我们提出一种双阶段中期训练方案，将空间视觉定位与规划解耦，在我们提出的 WebChainBench 及其他公开 GUI 基准上取得了最优性能。我们的工作为构建并严格评测下一代可扩展网页智能体提供了必要的数据与洞见。
英文原文摘要 805 字符
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.