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[CS.AI] WebAnything: From Real Web Pages to Scalable Agent Environments

Published at: 2026-10-31 22:00 Last updated: 2026-11-02 00:48
#AI #Machine Learning #Open Source

Web agents need diverse, executable, and resettable website environments for reproducible training and evaluation. Traditional human‑assisted mirrors require per‑site engineering, incurring high cost and maintenance burden, while generative construction often diverges from real sites. Capture‑and‑replay preserves observations but limits interaction coverage to captured content and replay capabilities.\ \ WebAnything introduces a fully automated framework that reconstructs source‑grounded, reusable web environments from real URLs without per‑site manual implementation or post‑deployment repair. The system consists of an Explorer‑Builder and an independent verifier. The Explorer‑Builder gathers browser‑observable structures, resources, and interaction outcomes; the verifier checks frozen candidates and, upon detecting mismatches, triggers local repair or targeted source recapture.\ \ We built 244 website artifacts across 14 application domains and 53 categories, supporting 12,984 executed and accepted tasks. Compared with the human‑assisted WebHarbor, WebAnything achieves higher aggregate scores on three fidelity metrics, recording 12 wins, 2 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, raises Odysseys‑Easy Rubric Avg and Task Avg by 6.25 and 6.32 points respectively, and boosts WebArena‑Verified overall success by 13.33 points under a fixed harness.\ \ The implementation is open‑source at the GitHub repository.\ \ Review

Original Source: https://arxiv.org/abs/2610.37786

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