用于无人机群的以大语言模型为中心的智能体人工智能：架构、使能技术及开放问题
LLM-Centric Agentic AI for UAV Swarms: Architecture, Enabling Technologies, and Open Problems
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中文总结 AI 辅助
研究针对无人机群实际部署受限问题，提出以大语言模型为中心的智能体人工智能LAUS，回顾相关使能技术，分析威胁，确定包括抗幻觉推理等在内的开放研究挑战。
中文摘要 AI 辅助
无人机群在搜索救援和环境监测等应用中具有巨大潜力，但实际部署受态势感知不足、间歇性连接和重大网络安全风险限制。智能体人工智能从独立大语言模型转向闭环认知架构，集成感知、记忆、推理/规划和行动以实现自适应、目标导向的群行为。本文提出用于无人机群的以大语言模型为中心的智能体人工智能（LAUS），回顾机载和边缘计算、5G/6G连接、多模态智能和网络安全机制等关键使能技术，分析可扭曲决策和降低网络性能的优先级操纵攻击等威胁。最后，确定了开放研究挑战，包括抗幻觉推理、在SWaP约束下的机载大语言模型部署以及智能体无人机系统中感知 - 推理攻击的标准化安全基准。
英文摘要
Uncrewed Aerial Vehicle (UAV) swarms have significant potential for applications such as Search and Rescue (SAR) and environmental monitoring, but their real-world deployment is limited by a lack of situational awareness, intermittent connectivity, and significant cybersecurity risks. Agentic Artificial Intelligence (AI) represents a shift from standalone Large Language Model (LLM) toward closed-loop cognitive architectures that integrate perception, memory, reasoning/planning, and action to enable adaptive, goal-directed swarm behavior. Within this framework, Agentic AI provides a unifying structure for autonomous and adaptive swarm operations while expanding the system attack surface compared to conventional AI systems. This paper proposes LLM-Centric Agentic AI for UAV Swarms (LAUS) and reviews key enabling technologies such as onboard and edge computing, 5G/6G connectivity, multimodal intelligence, and cybersecurity mechanisms, and analyzes threats such as Priority Manipulation Attacks (PMA) that can distort decision-making and degrade network performance. Finally, it identifies open research challenges, including hallucination-resistant reasoning, onboard LLM deployment under SWaP constraints, and standardized security benchmarks for perception-reasoning attacks in agentic UAV systems.
