# LLM-Centric Agentic AI for UAV Swarms: Architecture, Enabling Technologies, and Open Problems

arXiv: https://arxiv.org/abs/2607.09756 · HTML: https://mlsanity.com/paper/2607.09756/

## 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.

## Placed in

- LLM agents and tool use / [Multimodal and embodied agents](https://mlsanity.com/agents/multimodal-and-embodied-agents/)

## Builds on (most cited first, 12 of 12)

- [A-MEM: Agentic Memory for LLM Agents](https://mlsanity.com/paper/2502.12110/) (2025)
- [Agent Security Bench (ASB): Formalizing and Benchmarking Attacks and Defenses in LLM-based Agents](https://mlsanity.com/paper/2410.02644/) (2024)
- [The Emerged Security and Privacy of LLM Agent: A Survey with Case Studies](https://mlsanity.com/paper/2407.19354/) (2024)
- [Machine Learning-Aided Operations and Communications of Unmanned Aerial Vehicles: A Contemporary Survey](https://mlsanity.com/paper/2211.04324/) (2022)
- [Agentic AI Security: Threats, Defenses, Evaluation, and Open Challenges](https://mlsanity.com/paper/2510.23883/) (2025)
- [SoK: The Attack Surface of Agentic AI - Tools and Autonomy](https://mlsanity.com/paper/2603.22928/) (2026)
- [UAVs Meet Agentic AI: A Multidomain Survey of Autonomous Aerial Intelligence and Agentic UAVs](https://mlsanity.com/paper/2506.08045/) (2025)
- [LLM-Enabled In-Context Learning for Data Collection Scheduling in UAV-assisted Sensor Networks](https://mlsanity.com/paper/2504.14556/) (2025)
- [From Prompts to Protection: Large Language Model-Enabled In-Context Learning for Smart Public Safety UAV](https://mlsanity.com/paper/2506.02649/) (2025)
- [Agentic UAVs: LLM-Driven Autonomy with Integrated Tool-Calling and Cognitive Reasoning](https://mlsanity.com/paper/2509.13352/) (2025)
- [Agentic AI Meets Edge Computing in Autonomous UAV Swarms](https://mlsanity.com/paper/2601.14437/) (2026)
- [SwarmChat: An LLM-Based, Context-Aware Multimodal Interaction System for Robotic Swarms](https://mlsanity.com/paper/2509.16920/) (2025)

## 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.