| title | WebHarbor: Docking Real Websites for Evolving GUI Agent Environments |
|---|---|
| authors | Zhaoyang Wang, Qianhui Wu, Boyu Gou, Demi Wang, Jiacheng Zhang, Peichun Hua, Qize Li, Shi Qiu, Yifan Jiang, Yifan Zhang, Zexu Jin, Baolin Peng, Bolin Shen, Chetan Bansal, Dehan Kong, Deren Lei, Dongshen Peng, Fengqing Jiang, Hao Cheng, Haoqing Wang, Jiaming Shan, Jicheng Wang, Jingguang Zhou, Jixuan He, Kaide Zeng, Kangyu Zhu, Lama Moukheiber, Mufan Qiu, Peng Xia, Qihan Sun, Qingwei Lin, Qiushi Sun, Ruichen Zhang, Shuqing Luo, Si Qin, Sicheng Fan, Tao Xiong, Wenlin Yao, Xinyu Zhang, Xinyue Zeng, Xuanrui Li, Xuchao Zhang, Yian Wang, Yichao Liu, Yiming Liang, Yiyang Zhou, Yizhu Jiao, Yuetai Li, Yurun Chen, Yuxuan Zhang, Zheyuan Yang, Zhiyuan Yao, Zhongyang Li, Zihao Li, Zili Wang, Jianfeng Gao, Huaxiu Yao |
| date | 2026-11-02 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2611.03634 |
| affiliation | Microsoft |
| upvotes | 19 (Hugging Face Daily Papers, 2026-11-05) |
| links | abs pdf hf code |
| cite | |
| abstract | GUI web agents require realistic and interactive environments that remain stable during both evaluation and training. Live website evaluation suffers from changing content, access restrictions, and limited coverage of authenticated workflows, while existing synthetic environments for training often lack functional depth and visual fidelity. We propose WebHarbor, a suite of 153 executable and dockerized website environments that faithfully reconstruct the visual interfaces, content, and functional workflows of real-world web services under deterministic control. We build WebHarbor through task-driven human-agent collaboration, where environments evolve as new tasks expand their functional scope, coding agents implement the required changes, and human contributors audit visual fidelity, functional correctness, and task validity. The resulting suite comprises 3,518 tasks spanning multi-step and post-authentication workflows, where existing works often fail to cover. Beyond reliable evaluation, WebHarbor provides high-fidelity synthetic web environments for cost-efficient reinforcement learning (RL). We demonstrate that a Qwen 4B-level GUI agent trained with RL in WebHarbor generalizes to unseen tasks and performs competitively with leading open-source GUI web agents across four benchmarks. The code and data are publicly available at https://github.com/aiming-lab/WebHarbor. |
bibtex
@misc{wang2026webharbor,
title={WebHarbor: Docking Real Websites for Evolving GUI Agent Environments},
author={Zhaoyang Wang and Qianhui Wu and Boyu Gou and Demi Wang and Jiacheng Zhang and Peichun Hua and Qize Li and Shi Qiu and Yifan Jiang and Yifan Zhang and Zexu Jin and Baolin Peng and Bolin Shen and Chetan Bansal and Dehan Kong and Deren Lei and Dongshen Peng and Fengqing Jiang and Hao Cheng and Haoqing Wang and Jiaming Shan and Jicheng Wang and Jingguang Zhou and Jixuan He and Kaide Zeng and Kangyu Zhu and Lama Moukheiber and Mufan Qiu and Peng Xia and Qihan Sun and Qingwei Lin and Qiushi Sun and Ruichen Zhang and Shuqing Luo and Si Qin and Sicheng Fan and Tao Xiong and Wenlin Yao and Xinyu Zhang and Xinyue Zeng and Xuanrui Li and Xuchao Zhang and Yian Wang and Yichao Liu and Yiming Liang and Yiyang Zhou and Yizhu Jiao and Yuetai Li and Yurun Chen and Yuxuan Zhang and Zheyuan Yang and Zhiyuan Yao and Zhongyang Li and Zihao Li and Zili Wang and Jianfeng Gao and Huaxiu Yao},
year={2026},
eprint={2611.03634},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2611.03634}
}