OS-Symphony: A Holistic Framework for Robust and Generalist Computer-Using Agent
Bowen Yang, Kaiming Jin, Zhenyu Wu, Zhaoyang Liu, Qiushi Sun, Zehao Li, JingJing Xie, Zhoumianze Liu, Fangzhi Xu, Kanzhi Cheng, Qingyun Li, Yian Wang, Yu Qiao, Zun Wang, Zichen Ding

TL;DR
OS-Symphony introduces a comprehensive framework that enhances the robustness and generalization of computer-using agents through long-term memory and visual-aware tutorial retrieval, achieving state-of-the-art results.
Contribution
The paper presents OS-Symphony, a holistic framework combining a reflection-memory agent and versatile tool agents with multimodal search, addressing robustness and generalization in CUAs.
Findings
Achieves 65.84% on OSWorld benchmark.
Significant performance improvements across various model scales.
Sets new state-of-the-art results on three online benchmarks.
Abstract
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…
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Taxonomy
TopicsMultimodal Machine Learning Applications · Domain Adaptation and Few-Shot Learning · Advanced Image and Video Retrieval Techniques
