arXiv AI By Chenrui Shi, Yuwei Wu, Yang Liu, Ruining Feng, Zirui Shang, Zhi Gao, Lifeng Fan, Che Sun

Interactive Reward Agent: GUI Task Evaluation via Environment-State Verification

Read the original on arXiv AI →

arXiv:2607. 25904v1 Announce Type: new Abstract: Graphical user interface task evaluation aims to determine whether a GUI agent has successfully completed a user instruction.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv AI
Sep 1

GUI-PRA: Process Reward Agent for GUI Tasks

arXiv:2509.23263v3 Announce Type: replace Abstract: Long-horizon GUI automation remains challenging due to error accumulation over extended interaction sequences. Process Reward Models (PRMs) provide...

By Tao Xiong, Xavier Hu, Yurun Chen, Yuhang Liu, Changqiao Wu, Pengzhi Gao, Wei Liu, Jian Luan, Shengyu Zhang
arXiv AI
Aug 18

UI-Mate: Advancing Open-Weight Foundation GUI Agents with In-Context Demonstrations

arXiv:2608. 15930v1 Announce Type: new Abstract: Foundation GUI agents can automate complex digital tasks, but deployment is hindered by scarce and biased training data, ambiguous prompts, and unreliable execution.

By Zihan Ding, Longxu Dou, Qi Gao, Xiangwu Guo, Shengchao Hu, Zilong Huang, Zihang Jiang, Lei Ke, Mengcheng Lan, Weixian Lei, Hanxuan Li, Honglin Li, Xiyun Li, Zaitang Li, Leowei Liang, Xin Luo, Haozhe Ma, Jiayi Mao, Zhoujie Pan, Can Qin, Tianyuan Qu, Weiqi Wang, Wenkai Wang, Yonglin Wang, Yuxin Wang, Chenxu Wu, Yingchen Yu, Chenyu Zhang, Yuhao Zheng
arXiv AI
Sep 2

UI-Venus-2 Technical Report

UI‑Venus‑2 is a general‑purpose foundation GUI agent that operates across mobile, web, and desktop environments using a unified closed‑loop reasoning‑action framework. The report details how the system expands environment coverage to over 170 multilingual mobile apps and native desktop OSes, scales task generation through a deep‑research pipeline, and enhances verification with trace‑level and sample‑level evaluators that use visual keypoints and multi‑model voting. Safety‑aware mechanisms are also incorporated to control consequential actions, positioning UI‑Venus‑2 as an efficient, open‑source tool for more generalizable, verifiable, and self‑reflective agents in real‑world applications.

By Venus Team, Zhuohan Cai, Haoxing Chen, Jiaxuan Chen, Weizhi Chen, Changlong Gao, Zhangxuan Gu, Yuan Guo, Yusong Hu, Jianrong Jiang, Jianguo Li, Runze Li, Jinzhen Lin, Zhenyu Ma, Changhua Meng, Han Peng, Xinyu Qiu, Shuheng Shen, Zhongyi Shui, Weiqiang Wang, Ming Wen, Zhuoer Xu, Hang Yan, Kaiwen Yang, Ruilin Yao, Nanjun Yu, Zhengwen Zeng, Lianrui Zhang, Yunzhu Zhang, Zhe Zhao, Beitong Zhou