Agents centered around Large Language Models (LLMs) are now capable of automating mobile device operations for users. After fine-tuning to learn a user's mobile operations, these agents can adhere to high-level user instructions online. They execute tasks such as goal decomposition, sequencing of sub-goals, and interactive environmental exploration, until the final objective is achieved. However, privacy concerns related to personalized user data arise during mobile operations, requiring user confirmation. Moreover, users' real-world operations are exploratory, with action data being complex and redundant, posing challenges for agent learning. To address these issues, in our practical application, we have designed interactive tasks between agents and humans to identify sensitive information and align with personalized user needs. Additionally, we integrated Standard Operating Procedure (SOP) information within the model's in-context learning to enhance the agent's comprehension of complex task execution. Our approach is evaluated on the new device control benchmark AitW, which encompasses 30K unique instructions across multi-step tasks, including application operation, web searching, and web shopping. Experimental results show that the SOP-based agent achieves state-of-the-art performance without incurring additional inference costs, boasting an overall action success rate of 66.92%.

翻译：以大型语言模型（LLMs）为核心的智能体现已能够自动化用户移动设备的操作。经过微调学习用户移动操作后，这些智能体可在线遵循高级用户指令，执行目标分解、子目标排序及交互式环境探索等任务，直至最终目标达成。然而，移动操作中涉及个性化用户数据的隐私问题需要用户确认；此外，用户真实操作具有探索性特征，动作数据复杂且冗余，为智能体学习带来挑战。针对这些问题，我们在实际应用中设计了智能体与人类之间的交互式任务，以识别敏感信息并适配个性化用户需求。同时，我们在模型的上下文学习中集成标准操作流程（SOP）信息，以增强智能体对复杂任务执行的理解。该方法在新设备控制基准AitW上进行了评估，该基准涵盖跨多步骤任务的30K条独特指令，包括应用操作、网页搜索与网上购物。实验结果表明，基于SOP的智能体在未增加推理成本的情况下实现了最优性能，整体动作成功率达66.92%。