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智能体 arxiv.org · @Haifeng Wang

JarvisGUI:面向跨设备 GUI Agent 的动态任务组合基准

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该论文提出 JarvisGUI,一个面向跨设备工作流的动态 GUI Agent 基准,覆盖 Android、Windows 和 Ubuntu 等异构平台。它把 GUI 任务形式化为轻量类型系统下的输入-输出变换,从而自动组合多步跨设备工作流并在统一框架内动态评测。作者指出,现有基准多局限于单设备静态任务,导致对 Agent 真实可用性的评估过于乐观;实验显示当前最先进的开源 GUI Agent 在状态迁移感知、跨平台上下文推理和长程依赖管理方面存在明显短板。

AI推荐理由 arXiv 新提交的 GUI Agent 基准论文,提出跨设备(Android/Windows/Ubuntu)动态任务组合基准 JarvisGUI,用轻量类型系统把 GUI 任务建模为输入-输出变换以自动组合多步跨设备工作流。其具体新信息是揭示现有单设备静态基准对跨设备能力评估过于乐观,并指出开源 GUI Agent 在状态迁移感知、跨平台上下文推理和长程依赖管理上存在明显能力缺口。局限在于摘要仅给出基准设计与定性结论,未提供具体评测分数、任务规模或代码开源情况,实际效果仍需查看全文验证。

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Computer Science > Artificial Intelligence arXiv:2609.10451v1 (cs) [Submitted on 9 Sep 2026] Title:JarvisGUI: Towards Cross-Device GUI Agents with Dynamic Task Composition Authors:Zixiang Chen, Yuheng Lu, Zihao Cheng, Zeming Liu, Jizeng Bai, Ziye Huang, Zhiyin Lin, Zihan Li, Yuhang Guo, Yunhong Wang, Haifeng Wang View a PDF of the paper titled JarvisGUI: Towards Cross-Device GUI Agents with Dynamic Task Composition, by Zixiang Chen and 10 other authors View PDF HTML (experimental) Abstract:Real-world GUI usage frequently involves workflows that span multiple devices and platforms, requiring the transfer of intermediate results, maintenance of shared state, and coordination across heterogeneous environments. However, existing GUI benchmarks overwhelmingly evaluate agents on single-device, statically defined tasks, thus leaving such cross-device capabilities largely unexamined, resulting in an overly optimistic assessment of agents' readiness for real-world usage. We introduce JarvisGUI, a dynamic benchmark that evaluates GUI agents on cross-device workflows requiring coordinated interaction across heterogeneous platforms, including Android, Windows, and Ubuntu. Specifically, JarvisGUI formulates GUI tasks as input-output transformations under a lightweight type system, which allows us to automatically compose multi-step, cross-device workflows and dynamically evaluate agent performance within a unified framework. By evaluating agents in virtual environments spanning multiple operating systems, JarvisGUI reveals that state-of-the-art open-source GUI agents struggle with the state-transfer awareness, cross-platform contextual reasoning, and long-horizon dependency management required for real-world workflows, exposing a critical capability gap invisible to existing benchmarks. Comments: Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2609.10451 [cs.AI] (or arXiv:2609.10451v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2609.10451 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Yuheng Lu [view email] [v1] Wed, 9 Sep 2026 16:59:59 UTC (5,269 KB) Full-text links: Access Paper: View a PDF of the paper titled JarvisGUI: Towards Cross-Device GUI Agents with Dynamic Task Composition, by Zixiang Chen and 10 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.AI new | recent | 2026-09 Change to browse by: cs 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?)

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