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@Haifeng Wang
JarvisGUI:面向跨设备 GUI Agent 的动态任务组合基准
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。
78
正文
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
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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.
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Artificial Intelligence (cs.AI)
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arXiv:2609.10451 [cs.AI]
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arXiv:2609.10451v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2609.10451
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From: Yuheng Lu [view email]
[v1]
Wed, 9 Sep 2026 16:59:59 UTC (5,269 KB)
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