Jev-Mobile introduces a new approach for mobile GUI agents by separating high‑frequency lightweight execution from low‑frequency vision‑language model (VLM) planning. The VLM sets local goals, the accessibility tree provides a structured action space, and Jev, a fast typed decision model, repeatedly selects actions within this space, allowing multiple GUI actions per VLM decision. On the AndroidWorld task suite, Jev-Mobile achieves 79% task success, reduces mean end‑to‑end execution time by 32.7%, and cuts model API cost by 73.4% compared to a step‑wise VLM baseline.
By Linghua Zhang
Hybrid Computer Use (HCU) is a unified interface that enables general agents to interact with computers through three complementary channels: programmatic code and command-line tools, semantic accessibility (AX) features, and visual graphical user interfaces (GUI). The design emphasizes efficient AX interaction, batch execution, and AX diffs to reduce round trips, while a supplementary skill guides agents in selecting and coordinating these channels. On the OSWorld 2.0 benchmark, HCU outperforms other systems in both efficiency and task success, demonstrating the benefits of flexible, multi‑channel computer‑use interfaces.
By Haoqing Wang, Xiaoyi Zhang, Sicheng Fan, Zhexin Zhang, Xinjie Zhang, Jiahao Li, Bin Li, Yan Lu
arXiv:2607. 13027v1 Announce Type: cross Abstract: Large Language Model (LLM) agents have moved beyond generating responses to executing multi-step tasks by calling tools, observing the results, and iteratively deciding the next action.
By Hongru Cai, Yongqi Li, Ran Wei, Wenjie Li
GUI-CC is a benchmark designed to assess the contextual consistency of GUI world models when used as agent environments, rather than just one‑step next‑screen predictors. It includes two tracks: an offline reference‑action track that rolls models along real mobile GUI trajectories, and an online agent‑loop track where fixed probing agents interact with model‑generated UIs. The benchmark evaluates transition fidelity, plausibility, contextual consistency, and task progress across 500 offline trajectory tasks and 200 online tasks spanning 30 mobile apps.
By Lin Fu, Zheyuan Yang, Tianhui Zhang, Jinbiao Wei, Guo Gan, Boxu Liu, Yilun Zhao, Yu Rong
arXiv:2512. 12634v4 Announce Type: replace Abstract: Mobile GUI Agents, AI agents capable of interacting with mobile applications on behalf of users, have the potential to transform human computer interaction.
By Youngmin Im, Byeongung Jo, Jaeyoung Wi, Seungwoo Baek, Tae Hoon Min, Joo Hyung Lee, Sangeun Oh, Insik Shin, Sunjae Lee
arXiv:2610.01215v1 Announce Type: new
Abstract: GUI agents require high-quality interaction trajectories to learn how software environments respond to actions, maintain state, and support multi-step...
By Cheng Yang, Yifan Wu, Yutao Huang, Zhaohua Zhang, Beiduo Chen, Muxi Chen, Chenchen Zhao, Hexuan Deng, Haolin Yang, Geyuan Zhu, Sa Zhu, Jianhuan Zhuo, Qiuyong Xiao, Jianhao Ruan, Yiran Peng, Jiayi Zhang, Tian Ye, Xinlei Yu, Tianwen Jiang, Jihong Zhang, Yuyu Luo