Personal LLM Agents: Insights and Survey on Capability, Efficiency, and Security Personal LLM Agents are LLM-based agents deeply integrated with personal data and devices, designed to assist users. They aim to provide intelligent, convenient, and rich interaction experiences by reducing repetitive tasks and enhancing user efficiency. This paper explores the architecture, capabilities, efficiency, and security of Personal LLM Agents, addressing key challenges and potential solutions. The study involves insights from domain experts, highlighting the importance of personal data management, resource utilization, and personalized services. The paper discusses the evolution of intelligent personal assistants, from early speech recognition systems to modern LLM-powered agents. It identifies three main technical challenges: fundamental capabilities (task execution, context sensing, memorization), efficiency (inference, customization, memory manipulation), and security & privacy (data confidentiality, decision reliability, system integrity). Experts emphasize the need for edge-cloud collaboration, multimodal support, and secure data handling. The paper concludes that Personal LLM Agents have significant potential to become a major software paradigm for personal computing devices, but further research is needed to address scalability, efficiency, and security issues.Personal LLM Agents: Insights and Survey on Capability, Efficiency, and Security Personal LLM Agents are LLM-based agents deeply integrated with personal data and devices, designed to assist users. They aim to provide intelligent, convenient, and rich interaction experiences by reducing repetitive tasks and enhancing user efficiency. This paper explores the architecture, capabilities, efficiency, and security of Personal LLM Agents, addressing key challenges and potential solutions. The study involves insights from domain experts, highlighting the importance of personal data management, resource utilization, and personalized services. The paper discusses the evolution of intelligent personal assistants, from early speech recognition systems to modern LLM-powered agents. It identifies three main technical challenges: fundamental capabilities (task execution, context sensing, memorization), efficiency (inference, customization, memory manipulation), and security & privacy (data confidentiality, decision reliability, system integrity). Experts emphasize the need for edge-cloud collaboration, multimodal support, and secure data handling. The paper concludes that Personal LLM Agents have significant potential to become a major software paradigm for personal computing devices, but further research is needed to address scalability, efficiency, and security issues.