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arXiv · 2509.07553

VeriOS: Query-Driven Proactive Human-Agent-GUI Interaction for Trustworthy OS Agents

Zheng Wu·Heyuan Huang·Xingyu Lou·Xiangmou Qu·Pengzhou Cheng·Zongru Wu·Weiwen Liu·Weinan Zhang·Jun Wang·Zhaoxiang Wang·Zhuosheng Zhang
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Abstract

With the rapid progress of multimodal large language models, operating system (OS) agents become increasingly capable of automating tasks through on-device graphical user interfaces (GUIs). However, most existing OS agents are designed for idealized settings, whereas real-world environments often present untrustworthy conditions. To mitigate risks of over-execution in such scenarios, we propose a query-driven human-agent-GUI interaction framework that enables OS agents to decide when to query humans for more reliable task completion. Built upon this framework, we introduce VeriOS-Agent, a trustworthy OS agent trained with a three-stage learning paradigm that falicitate the decoupling and utilization of meta-knowledge by supervised fine-tuning and group relative policy optimization. Concretely, VeriOS-Agent autonomously executes actions in normal conditions while proactively querying humans in untrustworthy scenarios. Experiments show that VeriOS-Agent improves the average step-wise success rate by 19.72\% in over the strongest baselines, without compromising normal performance. VeriOS-Agent significantly improves performance in untrustworthy scenarios while maintaining comparable performance in trustworthy scenarios. Analysis highlights VeriOS-Agent's rationality, generalizability, and scalability. The codes, datasets and models are available at https://github.com/Wuzheng02/VeriOS.

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Zheng Wu, Heyuan Huang, Xingyu Lou, Xiangmou Qu, Pengzhou Cheng, Zongru Wu, Weiwen Liu, Weinan Zhang, Jun Wang, Zhaoxiang Wang, Zhuosheng Zhang. 2026-04-03. VeriOS: Query-Driven Proactive Human-Agent-GUI Interaction for Trustworthy OS Agents. https://arxiv.org/abs/2509.07553

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