综述智能代理在普适计算中的应用与挑战，提出高效部署框架。
Towards Pervasive Distributed Agentic Generative AI -- A State of The Art
- 构建基于感知、记忆、规划与行动的LLM代理架构
- 实现资源受限设备上的分布式智能代理部署
- 适合研究普适智能与边缘AI的开发者与学者
智能代理与大语言模型（LLMs）的快速发展正在重塑普适计算领域。它们通过自然语言理解实现感知、推理与自主行动，在异构传感器、设备和数据管理等复杂环境中展现出自主解决问题的能力。本文梳理了LLM代理的核心架构组件（画像、记忆、规划与行动），并分析其在不同场景下的部署与评估方式。同时回顾了从云到边缘的计算与基础设施进展，探讨了人工智能在该领域的演进路径。文章重点总结了当前最先进的代理部署策略与应用场景，涵盖本地及分布式执行于资源受限设备的情形。进一步指出了代理在普适计算中面临的关键挑战，包括架构、能耗与隐私限制。最后提出“智能体即工具”（Agent as a Tool）的概念框架，强调上下文感知、模块化、安全性、效率与有效性。
原文摘要 · Abstract (English)
The rapid advancement of intelligent agents and Large Language Models (LLMs) is reshaping the pervasive computing field. Their ability to perceive, reason, and act through natural language understanding enables autonomous problem-solving in complex pervasive environments, including the management of heterogeneous sensors, devices, and data. This survey outlines the architectural components of LLM agents (profiling, memory, planning, and action) and examines their deployment and evaluation across various scenarios. Than it reviews computational and infrastructural advancements (cloud to edge) in pervasive computing and how AI is moving in this field. It highlights state-of-the-art agent deployment strategies and applications, including local and distributed execution on resource-constrained devices. This survey identifies key challenges of these agents in pervasive computing such as architectural, energetic and privacy limitations. It finally proposes what we called "Agent as a Tool", a conceptual framework for pervasive agentic AI, emphasizing context awareness, modularity, security, efficiency and effectiveness.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。