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MobiFlow: 通过轨迹融合实现真实世界移动代理基准测试

MobiFlow: Real-World Mobile Agent Benchmarking through Trajectory Fusion

Institute of Parallel and Distributed Systems (IPADS), Shanghai Jiao Tong University(上海交通大学并行与分布式系统研究所(IPADS));Department of Precision Instrument, Tsinghua University(清华大学精密仪器系);National Innovation Institute of High-end Smart Appliances(国家高端智能家电创新研究院)

Yunfei Feng, Xi Zhao, Cheng Zhang, Dahu Feng, Daolin Cheng, Jianqi Yu, Yubin Xia, Erhu Feng

AI总结:

MobiFlow通过轨迹融合算法构建任务,覆盖20个常用第三方应用,提供240种真实任务,提升模型在真实负载下的评估精度。

摘要:

移动代理可通过GUI交互自主完成用户分配的任务。然而,现有主流评估基准,如AndroidWorld,通过连接系统级Android模拟器,基于系统资源状态提供评估信号。在真实世界移动代理场景中,许多第三方应用不提供系统级API来确定任务是否成功,导致基准与真实使用场景不匹配,难以准确评估模型性能。为解决这些问题,我们提出了MobiFlow,一个基于任意第三方应用任务的评估框架。通过基于多轨迹融合的高效图构建算法,MobiFlow可以有效压缩状态空间,支持动态交互,并更好地匹配真实世界第三方应用场景。MobiFlow覆盖20个广泛使用的第三方应用,包含240种多样的真实世界任务,具有丰富的评估指标。与AndroidWorld相比,MobiFlow的评估结果更符合人类评估,并可指导未来基于GUI的模型在真实负载下的训练。

英文摘要:

Mobile agents can autonomously complete user-assigned tasks through GUI interactions. However, existing mainstream evaluation benchmarks, such as AndroidWorld, operate by connecting to a system-level Android emulator and provide evaluation signals based on the state of system resources. In real-world mobile-agent scenarios, however, many third-party applications do not expose system-level APIs to determine whether a task has succeeded, leading to a mismatch between benchmarks and real-world usage and making it difficult to evaluate model performance accurately. To address these issues, we propose MobiFlow, an evaluation framework built on tasks drawn from arbitrary third-party applications. Using an efficient graph-construction algorithm based on multi-trajectory fusion, MobiFlow can effectively compress the state space, support dynamic interaction, and better align with real-world third-party application scenarios. MobiFlow covers 20 widely used third-party applications and comprises 240 diverse real-world tasks, with enriched evaluation metrics. Compared with AndroidWorld, MobiFlow's evaluation results show higher alignment with human assessments and can guide the training of future GUI-based models under real workloads.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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