arXiv:2605. 29486v2 Announce Type: replace-cross Abstract: A central bottleneck for phone-use agents is that controllable, reproducible environments covering real mobile behavior are hard to build at scale.
By Yuxuan Liu, Xin Lai, Junyi Li, Pengyuan Lyu, Jason, Yiduo Guo, Zhengyao Fang, Yang Ding, Yi Zhang, Weinong Wang, Huawen Shen, Xingran Zhou, Liang Wu, Fei Tang, Sunqi Fan, Shangpin Peng, Zheng Ruan, Anran Zhang, Chengquan Zhang, Han Hu, Benyou Wang, Ji-Rong Wen, Rui Yan, Zhengyang Tang
MobileWorldSafety is a benchmark that evaluates the safety of large language model–powered GUI agents on Android by exposing them to 142 real-world risk tasks involving environmental injection attacks. The benchmark uses a two‑stage verification pipeline—rule‑based checks for clear cases and an LLM judge for ambiguous ones—to distinguish safety failures from capability failures. Experiments on six agents show high vulnerability, with attack success rates between 40.4% and 66.9%, highlighting that current agents often fail to remain safe when faced with adversarial content presented as normal mobile context.
By Sujin Chen, Lijun Li, Tianyi Du, Jing Shao
arXiv:2608. 11434v1 Announce Type: new Abstract: Mobile agent benchmarks increasingly rely on LLM-based judges to evaluate task completion, yet the reliability of these judges on mobile agent trajectories remains largely unexamined.
By Ziqiang Wan, Li Gu, Zhixiang Chi, Zhi Liu, Seyed Mehdi Ayyoubzadeh, Yuanhao Yu, Yang Wang
The paper introduces AnTrap, a benchmark that injects dynamic perturbations into Android GUI agent execution to evaluate robustness against runtime anomalies. It presents a taxonomy of anomalies across four layers—State, Thinking, Action, and Round—with ten subcategories, and a pipeline that maintains task solvability while adding realistic adversarial conditions. Experiments on 16 leading GUI models show universal vulnerability, and reinforcement learning can mitigate some traps but not deep contextual ones like state deadlock.
By Guo Gan, Yilun Zhao, Cong Chen, Jinbiao Wei, Tingyu Song, Zheyuan Yang, Lin Fu, Hong Zhou
arXiv:2609.12394v2 Announce Type: replace
Abstract: Mobile GUI agents are shifting from multi-module frameworks to native models trained end-to-end, yet industrial deployment faces three persistent g...
By Tong Ye, Kunyang Han, Guozhi Wang, Longqiang Luo, Zhifeng Ding, Yongxiang Zhang, Xiaolei Shen, Yuxuan Zhang, Zhuping Zhang, Tao Xu, Yue Pan, Yucheng Zhao, Yupei Hu, Yuanjiang Ouyang, Danfeng Shen, Runqi Lin, Hongda Cai, Zhaoxiong Wang, Mengjia Yan, Yingjie Zhong, Chen Zhou, Zeyu Zhang, Xuwen Zhu, Penggang Shi, Mingcheng Luo, Ziyang Wu, Min Jin, Mingfu Shen, Zairong Xu, Fan Zhang, Hao Wang, Liang Liu, Zhulin Xie, Lijun Yao, Xiao Liang, Liangmin Wen, Liqiang Feng, Feilong Wu, Min Hu, Min Chen, Guanjing Xiong, Xiaohu Ruan, Xiaoxin Chen
The paper introduces GMA, a new benchmark for evaluating general mobile assistants in realistic, challenging scenarios. GMA expands on existing benchmarks by offering seven open‑source applications across diverse domains and 300 tasks organized into four difficulty tiers, ranging from simple actions to complex multi‑step workflows. The authors evaluate eight state‑of‑the‑art models, showing that performance drops sharply with task complexity, and conduct ablation studies on harness design—such as context retention and state tracking—to demonstrate how these choices can improve outcomes, especially for demanding workflows.
By Yiqi Zhu, Feiyu Gao, Jiaxing Fan, Jiahui Zeng, Minggang Wu, Chenliang Li, Haiyang Xu, Peng Li, Ming Yan, Yang Liu