最新资讯
bloomberg.com Nvidia 加强东南亚芯片追踪以打击走私 thestack.technology 模型路由需求升温,Agent Router 推出开源替代方案 www.youtube.com Google DeepMind 的 Philipp Schmid:AI 同事需要具备的七个要素 unite.ai Cashfree 为 AI Agent 的托管结账加入 WebMCP 工具 dev.to 我们在 DolphinBench 上测试记忆检索:纯关键词搜索暴露了自身回归 dev.to 别再把 Agent 写成 while 循环,把它建模为状态机 analyticsindiamag.com 小语言模型可能重塑企业 AI 部署方式 bloomberg.com Rightmove 与 Scout24 借 AI 产品应对低迷房市并上调展望 stackoverflow.blog 欢迎来到芯片时代:半导体制造、AI 数据中心与芯片设计的未来 www.youtube.com AI Agent 总犯同样的错:CentralAgent AI 将记忆重构为经验系统 pub.towardsai.net Netflix 科学家建议重采样 LLM 评判:5 次仅将误差棒缩小 13% pub.towardsai.net Jev 作为 LLM 前的静默层:构建提示感知的模型路由器
多元动态
登录 注册
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

原文 我的收藏

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 View a PDF of the paper titled OSWorld-Pro: Process-based Evaluation for Computer Use Agents, by Zhilin Wang and 11 other authors View PDF HTML (experimental) 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. Comments: Subjects: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) Cite as: arXiv:2609.24890 [cs.CL] (or arXiv:2609.24890v1 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2609.24890 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Zhilin Wang [view email] [v1] Mon, 21 Sep 2026 16:55:24 UTC (2,597 KB) Full-text links: Access Paper: View a PDF of the paper titled OSWorld-Pro: Process-based Evaluation for Computer Use Agents, by Zhilin Wang and 11 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.CL new | recent | 2026-09 Change to browse by: cs cs.AI cs.LG References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)

来源报道 · 1 篇 · 以下链接均前往原始媒体

← 返回精选动态 原文

更多内容