arXiv:2609.15387v3 Announce Type: replace-cross
Abstract: Human evaluation provides a direct measure of the quality of LLM-generated web applications. However, fitting human judgments through automat...
By Chenxu Liu, Zilu Zou, Peizhong Gao, Jiawen Tao, Zhexin Zhang, Guang Chen, Haowei Lin, Ying Zhou, Tianyi Bai, Dolly Deng, Suncong Zheng, Maxm Pan
Wuying-Browser-Agent is a unified framework designed to improve long-horizon browser agents by aligning execution, supervision, optimization, and evaluation. It introduces a structured browser harness, reflection and UI-specialized Curriculum SFT (RUIC‑SFT) for recovery and complex UI interactions, and Divergence‑Aware Online GRPO (DAO‑GRPO) for better credit assignment. The framework is evaluated on BrowserBench—a bilingual real‑web benchmark of 350 tasks—and achieves state‑of‑the‑art results on multiple browser‑use benchmarks, while also transferring well to other agentic tasks.
By AIMAE Team, Tianxiang Chen, Yan Cheng, Zhangye Han, Xiaowei Li, Chang Liu, Cheng Liu, Zhongqiang Ma, Long Peng, Xiaobing Tu, Yinggui Wang, Hongliang Wei, Chen Wu, Daiping Xin, Kunyu Zhou, Pengyang Zhou, Peiyuan Chen, Ziyuan Chen, Yutao Deng, Chunyu Dong, Xiangyu Fu, Yicheng Feng, Ruian He, Haochen Li, Miancan Liu, Zhengqin Liu, Wei Peng, Jinkui Ren, Haoyu Tan, Dong Xiao, Rongkun Xue, Shujian Yang, Xianhang Ye, Ziqi Yuan, Ziyang Yu, Linghan Zhang, Xiantao Zhang, Xuanpu Zhao, Yinan Zhao, Zhenghui Zhao, Bin Zhu, Likai Zou
arXiv:2609.15387v1 Announce Type: cross
Abstract: Human evaluation provides a direct measure of the quality of LLM-generated web applications. However, fitting human judgments through automated evalu...
By Chenxu Liu, Zilu Zou, Peizhong Gao, Jiawen Tao, Zhexin Zhang, Guang Chen, Haowei Lin, Ying Zhou, Tianyi Bai, Dolly Deng, Suncong Zheng, Maxm Pan
arXiv:2606. 01869v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly asked not only to write static interfaces, but to construct executable interactive worlds from natural language.
By Shuo Lu, Yinuo Xu, Kecheng Yu, Siru Jiang, Yongcan Yu, Yubin Wang, Haitao Yang, Yuxiang Zhang, Bin Wang, Ran He, Jian Liang
arXiv:2608. 03689v1 Announce Type: new Abstract: Large language models are increasingly capable of synthesizing executable frontend projects, yet existing benchmarks still treat web generation as a static evaluation problem.
By Yiyao Wang, Zhen Wen, Yinghao Tang, Yixiao Fu, Lin Yuan, Xiaolau Zhang, Jun Zhou, Wei Chen
RILA is an execution‑driven agent that integrates browser rendering into the generation loop for interactive web development. It uses an Action Interaction Verification module to replay reference interactions on generated pages, collecting execution‑aware observations, and an Execution‑aware Rendering Score to jointly assess interaction correctness and visual fidelity during iterative optimization. A data synthesis pipeline further augments training data, enabling RILA to significantly improve interaction and visual quality across foundation models, even outperforming larger one‑shot generators.
By Yilong Guo, Hanqi Chen, Zixiao Ye, Guanzhong Wang, Chen Yu, Zeyu Chen