把真实网站变成AI训练场，不用人盯
训练AI上网办事，最缺的不是模型，是场地：得有一堆真实、能重置、能反复考的网站。以前要么每个站人工搭镜像，要么让AI凭空生成——但凭空生成的网站和真站长得不像，练了也白练。这篇把流程全自动化：让一个「探索-构建」程序去真实URL上抓页面结构、资源和交互结果，再让另一个独立程序当考官，检查抓回来的东西像不像原站，不像就自动补抓或修。他们用这套流程造了244个网站环境，覆盖14个领域，跑通近1.3万个任务；拿这些数据微调一个9B小模型，在三个评测基准上成功率分别涨了9.9、6.3和13.3个百分点。它不是你明天能用上的工具，但「AI上网办事」这个方向，从此有了不用人盯的造场地流水线。
📄 原文摘要(英文)
Web agents require diverse, executable, and resettable website environments for reproducible training and evaluation. Human-assisted mirrors demand per-site engineering, while generative construction can diverge from specified real websites. Capture-and-replay preserves observations, with interaction coverage constrained by captured content and replay capabilities. We present WebAnything, a fully automated framework that reconstructs source-grounded, reusable Web environments from real URLs without per-site manual implementation or repair after initial setup and authorization. An Explorer-Builder collects browser-observable structures, resources, and interaction outcomes; an independent verifier checks frozen candidates and guides local repair or targeted source recapture. We construct 244 website artifacts across 14 application domains and 53 categories, supporting 12,984 executed and accepted tasks. Compared with human-assisted WebHarbor, WebAnything achieves higher aggregate scores on three fidelity metrics, with 12 wins, two ties, and no losses in a blind audit. Fine-tuning Qwen3.5-9B on 5,856 interaction trajectories improves REAL success by 9.9 percentage points, Odysseys-Easy Rubric Avg and Task Avg by 6.25 and 6.32 percentage points, and WebArena-Verified Overall success by 13.33 percentage points under the fixed harness. Code: https://github.com/china-qijizhifeng/Web-Anything