While Vision-Language Models (VLMs) have significantly advanced Computer-Using Agents (CUAs), current frameworks struggle with robustness in long-horizon workflows and generalization in novel domains. These limitations stem from a lack of granular control over historical visual context curation and the absence of visual-aware tutorial retrieval. 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.
@article{arxiv.2601.07779,
title = {OS-Symphony: A Holistic Framework for Robust and Generalist Computer-Using Agent},
author = {Bowen Yang and Kaiming Jin and Zhenyu Wu and Zhaoyang Liu and Qiushi Sun and Zehao Li and JingJing Xie and Zhoumianze Liu and Fangzhi Xu and Kanzhi Cheng and Qingyun Li and Yian Wang and Yu Qiao and Zun Wang and Zichen Ding},
journal= {arXiv preprint arXiv:2601.07779},
year = {2026}
}