@inproceedings{yang-etal-2026-os,
    title = "{OS}-Symphony: A Holistic Framework for Robust and Generalist Computer-Using Agents",
    author = "Yang, Bowen  and
      Jin, Kaiming  and
      Wu, Zhenyu  and
      Liu, Zhaoyang  and
      Sun, Qiushi  and
      Li, Zehao  and
      Xie, JingJing  and
      Liu, Zhoumianze  and
      Xu, Fangzhi  and
      Cheng, Kanzhi  and
      Wang, Yian  and
      Li, Qingyun  and
      Qiao, Yu  and
      Wang, Zun  and
      Ding, Zichen",
    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.1021/",
    pages = "22300--22330",
    ISBN = "979-8-89176-390-6",
    abstract = "While Vision-Language Models (VLMs) have significantly advanced Computer-Using Agents (CUAs), current agentic frameworks struggle with robustness in novel domains and long-horizon workflows due to the absence of visual-aware tutorial retrieval and the lack of granular control over historical visual context curation and pruning. To bridge these gaps, we introduce OS-Symphony, a holistic framework that comprises an Orchestrator coordinating two key innovations for robust automation: (1) a Reflection-Memory Agent that utilizes milestone-driven long-term memory to enable trajectory-level self-correction, effectively mitigating visual context loss in long-horizon tasks; (2) Versatile Tool Agents featuring a Multimodal Searcher that adopts a ``SeeAct'' paradigm to navigate a browser-based sandbox to synthesize live, visually aligned tutorials, thereby resolving fidelity issues in unseen scenarios. Experimental results demonstrate that OS-Symphony delivers substantial performance gains across varying model scales, establishing new state-of-the-art results on three online benchmarks, notably achieving 65.84{\%} on OSWorld. All research assets will be made publicly available."
}