@inproceedings{lu-etal-2025-axis,
    title = "{AXIS}: Efficient Human-Agent-Computer Interaction with {API}-First {LLM}-Based Agents",
    author = "Lu, Junting  and
      Zhang, Zhiyang  and
      Yang, Fangkai  and
      Zhang, Jue  and
      Wang, Lu  and
      Du, Chao  and
      Lin, Qingwei  and
      Rajmohan, Saravan  and
      Zhang, Dongmei  and
      Zhang, Qi",
    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.381/",
    pages = "7711--7743",
    ISBN = "979-8-89176-251-0",
    abstract = "Multimodal large language models (MLLMs) have enabled LLM-based agents to directly interact with application user interfaces (UIs), enhancing agents' performance in complex tasks. However, these agents often suffer from high latency and low reliability due to the extensive sequential UI interactions. To address this issue, we propose AXIS, a novel LLM-based agents framework that prioritize actions through application programming interfaces (APIs) over UI actions. This framework also facilitates the creation and expansion of APIs through automated exploration of applications. Our experiments on Microsoft Word demonstrate that AXIS reduces task completion time by 65{\%}-70{\%} and cognitive workload by 38{\%}-53{\%}, while maintaining accuracy of 97{\%}-98{\%} compared to humans. Our work contributes to a new human-agent-computer interaction (HACI) framework and explores a fresh UI design principle for application providers to turn applications into agents in the era of LLMs, paving the way towards an agent-centric operating system (Agent OS). The code and dataset will be available at https://aka.ms/haci{\_}axis."
}