智能体
arxiv.org ·
@Yi Dong
OSWorld-Pro:面向计算机使用智能体的过程式评测
论文指出Computer-Use Agent(CUA)评测通常只关注数百步后的最终交付物并用功能验证器判定,缺乏对失败原因与过程的透明度,例如键盘输入错误与图形界面点击不精确需要不同改进策略。作者提出OSWorld-Pro,包含300多个任务、2800多个子目标,基于67000多条人工标注,使用与人类对齐的LLM-Judge评估子目标完成情况,从而揭示模型在顺序依赖子目标中的进展。结果显示该基准对当前最先进LLM仍具挑战性。
AI推荐理由 针对Computer-Use Agent评测只看最终交付物、缺乏失败过程透明度的问题,提出300+任务、2800+子目标、基于67000+人工标注的过程式评测集,并用人类对齐的LLM-Judge评估子目标完成情况;对CUA能力诊断有实用价值,但摘要未给出具体分数与完整结果。
76
正文
Computer Science > Computation and Language
arXiv:2609.24890v1 (cs)
[Submitted on 21 Sep 2026]
Title:OSWorld-Pro: Process-based Evaluation for Computer Use Agents
Authors:Zhilin Wang, Shaokun Zhang, Yifan Zhang, Hao Zhang, Jin Xu, Binfeng Xu, Jian Hu, Yunheng Zou, Karan Sapra, Andrew Tao, Jan Kautz, Yi Dong
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Abstract:Evaluation of Computer-Use Agents (CUAs) is often limited to the final deliverables they create (at the end of hundreds of steps) and assessed with functional verifiers, as seen in OSWorld. However, such evaluation of end-state performance lacks transparency into how and why agents fail in various tasks, obfuscating critical insight for subsequent improvement. For instance, agents that err during keyboard inputs would require a different mitigation strategy from those that fail to precisely provide click-based inputs on the graphical UI. We introduce OSWorld-Pro: a set of over 300 tasks containing over 2800 subgoals to enable the procedural evaluation of CUAs grounded in over 67,000 human annotations. We use robust human-aligned LLM-Judges to evaluate the fulfillment of OSWorld-Pro subgoals and thereby reveal the progress that models make throughout a series of sequentially dependent subgoals. Our findings reveal that OSWorld-Pro is challenging even for state-of-the-art LLMs, with top performers like Claude Opus 5 achieving only 75.7% vs. 83.4% on OSWorld. Furthermore, we identify critical process-focused failure modes of various models (e.g. subgoal-irrelevant actions and click-based mistakes) to provide insights to improve performance and efficiency of CUAs.
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Subjects:
Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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arXiv:2609.24890 [cs.CL]
(or
arXiv:2609.24890v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2609.24890
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arXiv-issued DOI via DataCite (pending registration)
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From: Zhilin Wang [view email]
[v1]
Mon, 21 Sep 2026 16:55:24 UTC (2,597 KB)
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