@inproceedings{ai-etal-2026-inquiremobile,
    title = "{I}nquire{M}obile: Teaching {VLM}-based Mobile Agent to Request Human Assistance via Reinforcement Fine-Tuning",
    author = "Ai, Qihang  and
      Bu, Pi  and
      Cao, Yue  and
      Wang, Yingyao  and
      Gu, Jihao  and
      Xing, Jingxuan  and
      Zhu, Zekun  and
      Jiang, Wei  and
      Zheng, Zhicheng  and
      Song, Jun  and
      Jiang, Yuning",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.1487/",
    pages = "32241--32255",
    ISBN = "979-8-89176-390-6",
    abstract = "Recent advances in Vision-Language Models (VLMs) have enabled mobile agents to perceive and interact with real-world mobile environments based on human instructions. However, the current fully autonomous paradigm poses potential safety risks when model understanding or reasoning capabilities are insufficient. To address this challenge, we first introduce \textbf{InquireBench}, a comprehensive benchmark specifically designed to evaluate mobile agents' capabilities in safe interaction and proactive inquiry with users, encompassing 5 categories and 22 sub-categories, where most existing VLM-based agents demonstrate near-zero performance. In this paper, we aim to develop an interactive system that actively seeks human confirmation at critical decision points. To achieve this, we propose \textbf{InquireMobile}, a novel model inspired by reinforcement learning, featuring a two-stage training strategy and an interactive pre-action reasoning mechanism. Finally, our model achieves an 46.8{\%} improvement in inquiry success rate and the best overall success rate among existing baselines on InquireBench. The project page is available at \url{https://bit-aqh.github.io/InquireMobile/homepage/}."
}