Since the advent of personal computing devices, intelligent personal assistants (IPAs) have been one of the key technologies that researchers and engineers have focused on, aiming to help users efficiently obtain information and execute tasks, and provide users with more intelligent, convenient, and rich interaction experiences. With the development of smartphones and IoT, computing and sensing devices have become ubiquitous, greatly expanding the boundaries of IPAs. However, due to the lack of capabilities such as user intent understanding, task planning, tool using, and personal data management etc., existing IPAs still have limited practicality and scalability. Recently, the emergence of foundation models, represented by large language models (LLMs), brings new opportunities for the development of IPAs. With the powerful semantic understanding and reasoning capabilities, LLM can enable intelligent agents to solve complex problems autonomously. In this paper, we focus on Personal LLM Agents, which are LLM-based agents that are deeply integrated with personal data and personal devices and used for personal assistance. We envision that Personal LLM Agents will become a major software paradigm for end-users in the upcoming era. To realize this vision, we take the first step to discuss several important questions about Personal LLM Agents, including their architecture, capability, efficiency and security. We start by summarizing the key components and design choices in the architecture of Personal LLM Agents, followed by an in-depth analysis of the opinions collected from domain experts. Next, we discuss several key challenges to achieve intelligent, efficient and secure Personal LLM Agents, followed by a comprehensive survey of representative solutions to address these challenges.

翻译：自个人计算设备问世以来，智能个人助手（IPA）一直是研究人员和工程师关注的关键技术之一，旨在帮助用户高效获取信息并执行任务，为用户提供更智能、便捷、丰富的交互体验。随着智能手机和物联网的发展，计算与传感设备已变得无处不在，极大地拓展了IPA的应用边界。然而，由于缺乏用户意图理解、任务规划、工具使用以及个人数据管理等能力，现有IPA的实用性和可扩展性仍然有限。近期，以大型语言模型（LLM）为代表的基础模型的涌现，为IPA的发展带来了新机遇。凭借强大的语义理解与推理能力，LLM能够使智能体自主解决复杂问题。本文聚焦于个人LLM智能体——即基于LLM、深度整合个人数据与个人设备并用于个人辅助的智能体。我们预见，个人LLM智能体将在即将到来的时代成为面向终端用户的主要软件范式。为实现这一愿景，我们率先迈出第一步，探讨了个人LLM智能体的若干重要问题，包括其架构、能力、效率与安全性。我们首先总结了个人LLM智能体架构中的关键组成部分与设计选择，继而深入分析了从领域专家处收集的观点。随后，我们讨论了实现智能、高效且安全的个人LLM智能体所面临的若干关键挑战，并全面综述了应对这些挑战的代表性解决方案。