Consensus Within, Competition Between: Decentralized LLM Teams for Perimeter Defense

Authors: Khawaja Abaid Ullah , Ananya Acharya , Mohammad Javad Khojasteh

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Abstract: The use of large language models (LLMs) to coordinate multi-agent teams raises a fundamental architectural question: whether task allocation should be centralized in a single LLM controller or distributed across autonomous LLM agents, particularly when communication is unreliable. In a deliberately simplified perimeter-defense simulation against LLM-controlled intruders, we compared a central commander, an auction-based consensus team and independent agents under five network conditions. Breach rates were 15% for the consensus team, 32% for the commander and 29% for independent agents, and of the different network conditions, only limited radio range changed a team's breach rate, raising the commander's. In consensus team, giving a low-cost LLM more authority over the bid computation raised breaches by 38 percentage points. With birds and civilian drones in airspace, we added a photograph for each to the input text state. Only the near-frontier model treated them apart from intruders. Intruders disguised as existing taxidermy bird drones were mistook as birds by both low-cost and near-frontier LLMs, going unpursued in 59-61% of near-frontier decisions (1-16% before). However, a one-sentence forewarning in the prompt lowered it to 0-25%. We find that LLM team with a dedicated external conflict resolution protocol results in fewest breaches. Code: this https URL .

Comments:

88 pages (33 main text and references, 55 supplementary material), 7 main figures. Code: this https URL

Subjects:

Artificial Intelligence (cs.AI) ; Machine Learning (cs.LG); Robotics (cs.RO)

Cite as:

arXiv:2610.41044 [cs.AI]

(or

arXiv:2610.41044v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2610.41044

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arXiv-issued DOI via DataCite (pending registration)

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From: Khawaja Abaid Ullah [ view email ]

[v1]

Thu, 29 Oct 2026 22:34:13 UTC (2,771 KB)

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