@inproceedings{zhao-etal-2026-mas,
    title = "{MAS}-Bench: A Unified Benchmark for Shortcut-Augmented Hybrid Mobile {GUI} Agents",
    author = "Zhao, Pengxiang  and
      Liu, Guangyi  and
      Liang, Yaozhen  and
      He, Weiqing  and
      Lu, Zhengxi  and
      Wang, WenHao  and
      Huang, Yuehao  and
      Chai, Yuxiang  and
      Kang, Zhaolu  and
      Guo, Yaxuan  and
      Wang, Hao  and
      Zhang, Kexin  and
      Liu, Liang  and
      Liu, Yong",
    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.316/",
    pages = "6960--6985",
    ISBN = "979-8-89176-390-6",
    abstract = "Shortcuts such as APIs and deep-links have emerged as efficient complements to flexible GUI operations, fostering a promising hybrid paradigm for MLLM-based mobile automation. However, systematic evaluation of GUI{--}shortcut hybrid agents remains largely underexplored. To bridge this gap, we introduce **MAS-Bench**, a benchmark that pioneers the evaluation of GUI-shortcut hybrid agents with a specific focus on the mobile domain. Beyond merely using predefined shortcuts, MAS-Bench assesses an agent{'}s capability to *autonomously generate* shortcuts by discovering and creating reusable, low-cost workflows. It features 139 complex tasks across 11 real-world applications, a knowledge base of 88 predefined shortcuts (APIs, deep-links, RPA scripts), and 9 evaluation metrics. Experiments demonstrate that hybrid agents achieve up to 68.3{\%} success rate and 39{\%} greater execution efficiency than GUI-only counterparts. Furthermore, our evaluation framework effectively reveals the quality gap between predefined and agent-generated shortcuts, validating its capability to assess shortcut generation methods. MAS-Bench addresses the lack of systematic benchmarks for GUI-shortcut hybrid mobile agents, providing a foundational platform for future advancements in creating more efficient and robust intelligent agents."
}