提出首个支持多GUI代理并行协作的基准，提升长任务执行效率。
Beyond Sequential Interaction: Benchmarking Parallel Execution and Coordination for GUI Agents
- 设计多设备协同框架，让多个代理在不同桌面并行执行子任务
- 在233个任务上实现46.4%成功率，步骤减少一半、令牌消耗不足一半
- 适合研究多代理系统、自动化测试与高效人机交互的开发者
图形用户界面（GUI）代理由大型多模态模型驱动，通过点击、输入和滚动等操作在桌面和移动设备上执行指令。然而，现有代理在长周期任务中扩展性差：动作带来高昂的多模态模型推理开销，且性能随上下文增长而下降。人类常将复杂任务拆分给多个协作者并行完成。但目前对多GUI代理间的并行协调研究极少。为此，我们提出ParaGUIBench，据知是首个专注于多代理在独立桌面实例上并行执行与协作的基准。该基准包含三部分：支持多设备的Docker基础设施与共享文件系统；涵盖六类任务的233个任务数据集；以及包含步数减少率与令牌成本等效率指标的评估系统。我们进一步提出ParaGUI，一种规划-工作者架构的代理，可将任务分解并分配至独立桌面的并发工作节点。在ParaGUIBench上，ParaGUI取得46.4%的成功率，优于最强串行基线（Claude Sonnet 4.6）12.9个百分点，同时步骤数减少约50%，令牌消耗低于一半。结果表明，对于可分解的长周期任务，并行执行能有效提升成功率与效率，为未来研究指明方向。
原文摘要 · Abstract (English)
Graphical user interface (GUI) agents are systems powered by large multimodal models (LMMs). They perceive screen state and execute user instructions through GUI actions such as clicking, typing, and scrolling on desktops and mobile devices. However, current agents scale poorly to long-horizon tasks: actions incur costly LMM inferences, and performance degrades as context grows. Humans divide such workloads among collaborators who complete sub-tasks in parallel. Yet parallel coordination among GUI agents has received little attention. To close this gap, we introduce ParaGUIBench, to our knowledge, the first benchmark dedicated to parallel execution and coordination of multiple GUI agents on separate desktop instances. It consists of three components: a multi-device Docker infrastructure with a shared file system; a dataset of 233 tasks spanning six task categories; and an evaluation system with efficiency metrics, including step reduction ratio and token cost. We further introduce ParaGUI, a planner-worker agent that decomposes GUI tasks and dispatches sub-tasks to concurrent workers on separate desktop instances. On ParaGUIBench, ParaGUI reaches a 46.4% success rate, outperforming the strongest serial baseline (Claude Sonnet 4.6) by 12.9 points while using roughly half the steps and less than half the tokens. These results show that parallel execution can improve both success rate and efficiency on decomposable, long-horizon GUI tasks, pointing to a direction worth further study.
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