@inproceedings{xu-etal-2025-androidlab,
    title = "{A}ndroid{L}ab: Training and Systematic Benchmarking of Android Autonomous Agents",
    author = "Xu, Yifan  and
      Liu, Xiao  and
      Sun, Xueqiao  and
      Cheng, Siyi  and
      Yu, Hao  and
      Lai, Hanyu  and
      Zhang, Shudan  and
      Zhang, Dan  and
      Tang, Jie  and
      Dong, Yuxiao",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingestion-acl-25/2025.acl-long.107/",
    pages = "2144--2166",
    ISBN = "979-8-89176-251-0",
    abstract = "Autonomous agents have become increasingly important for interacting with the real world. Android agents, in particular, have been a frequently-mentioned interaction method. However, existing studies for training and evaluating Android agents lack systematic research on both open-source and closed-source models. In this work, we propose AndroidLab as a systematic Android agent framework. It includes an operation environment with different modalities, action space, and a reproducible benchmark. It supports both large language models (LLMs) and multimodal models (LMMs) in the same action space. AndroidLab benchmark includes predefined Android virtual devices and 138 tasks across nine apps built on these devices. By using the AndroidLab environment, we develop an Android Instruction dataset and train six open-source LLMs and LMMs, lifting the average success rates from 4.59{\%} to 21.50{\%} for LLMs and from 1.93{\%} to 13.28{\%} for LMMs. AndroidLab is open-sourced and publicly available at https://github.com/THUDM/Android-Lab."
}