Managing Context and Communication in Distributed Agentic UAV Swarms

Abstract:Unmanned aerial vehicle (UAV) swarms increasingly rely on language-model agents to provide adaptive mission-level reasoning in uncertain environments. Fully distributed control, in which each UAV hosts an independent Small Language Model (SLM), removes reliance on a centralized coordinator but introduces an information-management problem: long-running interaction histories can degrade the reasoning context, while indiscriminate information dissemination increases communication and inference overhead. We address these challenges with a distributed UAV-agent architecture that enables continuous local SLM control through an event-driven reason-act-observe lifecycle. Runtime knowledge is represented as structured atomic notes and organized into core, local, and peer-specific memory. A deterministic interest-aware gossip engine selectively disseminates these notes according to recipient-specific semantic novelty and recency. We evaluate the architecture using ten UAVs in a simulated search-and-rescue mission. Our approach completes all experimental runs, whereas unrestricted flooding messages completes only 70-85\%, and delegating forwarding decisions to the SLM prevents mission completion in every run. Compared with unrestricted flooding, our approach approximately halves inference-token consumption, reduces transmitted data, and achieves lower survivor-count error.

12 pages, 4 figures. This paper has been accepted for presentation at the 24th IEEE Consumer Communications & Networking Conference 2027 (CCNC 2027)

Multiagent Systems (cs.MA); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Networking and Internet Architecture (cs.NI); Robotics (cs.RO)

I.2.11; C.3; C.2.1; I.2.8; H.5.2

arXiv:2610.01569 [cs.MA]

(or arXiv:2610.01569v1 [cs.MA] for this version)

https://doi.org/10.48550/arXiv.2610.01569 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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