发表机构
University of Luxembourg; CSIRO; Qassim University; Edith Cowan University（卢森堡大学; 联邦科学与工业研究组织; 卡西姆大学; 伊迪丝·考恩大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本综述提出智能体AI与集成感知通信结合的AISAC范式，构建六阶段闭环框架与五成熟度等级，梳理相关领域进展，分析交叉需求，发现现有系统智能体成熟度不足，并指出多方面开放挑战。
AI 中文摘要
智能体人工智能（AI）正将集成感知与通信（ISAC）从面向功能的物理层技术转变为目标驱动的闭环智能系统，我们将这一范式称为AISAC。现有关于基于学习的感知、资源分配、可重构智能表面（RIS）、边缘智能、多智能体协调及弹性网络的研究大多各自独立发展。本综述将相关文献统一纳入包含感知、情境化、推理与预测、规划与编排、执行与协作、反馈与弹性六个阶段的闭环框架中，还引入了从物理层原语到全闭环智能体ISAC的五个智能体成熟度等级。我们利用该框架回顾多模态智能、大语言模型、强化学习、联邦学习、RIS辅助控制、无人机（UAV）与车载网络、AI原生网络管理等领域的进展，并将隐私、安全、弹性、可持续性作为感知-推理-行动全链路的交叉需求进行分析。对代表性研究按九项智能体特定评估标准的核查显示，没有系统报告满足其中两项以上标准，暴露了宣称的智能体成熟度与实际表现之间的差距。我们还确定了从物理到语义的 grounding、预测世界模型、智能体-物理层实时交互、安全工具使用、异构多智能体协作、基准测试及资源高效型自主等方面的开放挑战。
英文摘要
Agentic artificial intelligence (AI) is transforming Integrated Sensing and Communication (ISAC) from a function-oriented physical-layer technology into a goal-driven, closed-loop intelligent system, a paradigm we term AISAC. Existing work on learning-based sensing, resource allocation, reconfigurable intelligent surfaces (RIS), edge intelligence, multi-agent coordination, and resilient networking has developed largely in isolation. This survey unifies the literature within a six-stage closed-loop framework comprising observation, contextualization, reasoning and prediction, planning and orchestration, execution and collaboration, and feedback and resilience. It also introduces five levels of agentic maturity, ranging from physical-layer primitives to fully closed-loop agentic ISAC. We use this framework to review advances in multimodal intelligence, large language models, reinforcement learning, federated learning, RIS-assisted control, Unmanned Aerial Vehicle (UAV) and vehicular networks, and AI-native network management, and analyze privacy, security, resilience, and sustainability as cross-cutting requirements of the full perception-reasoning-action loop. An audit of representative studies against nine agentic-specific evaluation criteria shows that no system reports more than one or two of them, exposing a gap between claimed and demonstrated agentic maturity. We identify open challenges in physical-to-semantic grounding, predictive world models, real-time agent-PHY interaction, safe tool use, heterogeneous multi-agent collaboration, benchmarking, and resource-efficient autonomy.
Comments35 pages, 132 references, 10 tables, 9 figures
