基于好奇心的知识检索用于移动代理
Curiosity Driven Knowledge Retrieval for Mobile Agents
AI总结：
本研究提出一种基于好奇心的知识检索框架，通过结构化AppCards提升移动代理在复杂任务中的规划可靠性与执行稳定性。
摘要：
移动代理在智能手机自动化方面取得了进展，但在复杂应用中的性能仍受到知识不完整和对未见环境的弱泛化能力的限制。我们介绍了一种由好奇心驱动的知识检索框架，该框架将执行过程中不确定性和好奇心评分相结合。当此评分超过阈值时，系统会从文档、代码库和历史轨迹中检索外部信息。检索到的内容被组织成结构化的AppCards，这些卡片编码了功能语义、参数约定、界面映射和交互模式。在执行过程中，增强的代理会选择性地将相关的AppCards整合到其推理过程中，从而弥补知识盲区并提高规划可靠性。在AndroidWorld基准测试中的评估显示，在所有backbone上都实现了持续的改进，与GPT-5结合时，平均提升6个百分点，新的最先进的成功率为88.8%。分析表明，AppCards在多步骤和跨应用任务中特别有效，而改进取决于backbone模型。案例研究进一步确认，AppCards减少了歧义，缩短了探索时间，并支持稳定的执行轨迹。任务轨迹在https://lisalsj.github.io/Droidrun-appcard/上公开。
英文摘要：
Mobile agents have made progress toward reliable smartphone automation, yet performance in complex applications remains limited by incomplete knowledge and weak generalization to unseen environments. We introduce a curiosity driven knowledge retrieval framework that formalizes uncertainty during execution as a curiosity score. When this score exceeds a threshold, the system retrieves external information from documentation, code repositories, and historical trajectories. Retrieved content is organized into structured AppCards, which encode functional semantics, parameter conventions, interface mappings, and interaction patterns. During execution, an enhanced agent selectively integrates relevant AppCards into its reasoning process, thereby compensating for knowledge blind spots and improving planning reliability. Evaluation on the AndroidWorld benchmark shows consistent improvements across backbones, with an average gain of six percentage points and a new state of the art success rate of 88.8\% when combined with GPT-5. Analysis indicates that AppCards are particularly effective for multi step and cross application tasks, while improvements depend on the backbone model. Case studies further confirm that AppCards reduce ambiguity, shorten exploration, and support stable execution trajectories. Task trajectories are publicly available at https://lisalsj.github.io/Droidrun-appcard/.
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。
