LLM-Centric Agentic AI for UAV Swarms: Architecture, Enabling Technologies, and Open Problems
On the map: LLM agents and tool use → Multimodal and embodied agents
Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Multimodal and embodied agents, one of 16 directions in LLM agents and tool use. Builds on 12 of its 15 references that are in the map. First posted on arXiv 5 Jul 2026.
Builds on
12 of 15 references.
Closest ancestors · 3
Same direction, newest first
- 2026Agentic AI Meets Edge Computing in Autonomous UAV Swarms20 Jan
- 2025Agentic UAVs: LLM-Driven Autonomy with Integrated Tool-Calling and Cognitive Reasoning14 Sep
- 2025UAVs Meet Agentic AI: A Multidomain Survey of Autonomous Aerial Intelligence and Agentic UAVs8 Jun
Other references · 9
- 2025A-MEM: Agentic Memory for LLM Agentscited by 1,063
- 2024Agent Security Bench (ASB): Formalizing and Benchmarking Attacks and Defenses in LLM-based Agentscited by 369
- 2024The Emerged Security and Privacy of LLM Agent: A Survey with Case Studiescited by 131
- 2022Machine Learning-Aided Operations and Communications of Unmanned Aerial Vehicles: A Contemporary Surveycited by 28
- 2025Agentic AI Security: Threats, Defenses, Evaluation, and Open Challengescited by 28
- 2026SoK: The Attack Surface of Agentic AI - Tools and Autonomycited by 16
- 2025LLM-Enabled In-Context Learning for Data Collection Scheduling in UAV-assisted Sensor Networkscited by 12
- 2025From Prompts to Protection: Large Language Model-Enabled In-Context Learning for Smart Public Safety UAVcited by 10
- 2025SwarmChat: An LLM-Based, Context-Aware Multimodal Interaction System for Robotic Swarmscited by 2
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Close in wording, no citation either way
- 2026Say the Mission, Execute the Swarm: Agent-Enhanced LLM Reasoning in the Web-of-Drones
- 2026Large Language Model-Assisted UAV Operations and Communications: A Multifaceted Survey and Tutorial
- 2025UAVs Meet LLMs: Overviews and Perspectives Toward Agentic Low-Altitude Mobility
- 2025A Survey of LLM-Driven AI Agent Communication: Protocols, Security Risks, and Defense Countermeasures
- 2026Neuro-Symbolic Agentic AI for Networked Low-Altitude UAVs
- 2026AeroWeaver: An Embodied-Agent Harness for Weaving Aerial Skills into Distributed, Adaptive Swarm Execution
- 2025Multimodal Large Language Models-Enabled UAV Swarm: Towards Efficient and Intelligent Autonomous Aerial Systems
- 2025CoordField: Coordination Field for Agentic UAV Task Allocation In Low-altitude Urban Scenarios