Consensus Within, Competition Between: Decentralized LLM Teams for Perimeter Defense
Overview
This research paper investigates the architectural trade-offs between centralized and decentralized large language model (LLM) teams in a simulated perimeter-defense scenario against LLM-controlled intruders. The authors compare three approaches: a central commander, an auction-based consensus team, and independent agents across five network conditions. Results indicate that the consensus team achieved the lowest breach rate of 15%, compared to 32% for the commander and 29% for independent agents. The study further explores how limited radio range affects performance and how visual input helps distinguish civilian drones from intruders, noting that a one-sentence forewarning in the prompt significantly reduces misidentification rates.
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Environment: simulation
“arXiv:2610.41044v1 Announce Type: cross 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 intrude”
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“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: https://github.com/the-kas-lab/consensus-within-competition-between.”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsSignalConsensus Within, Competition Between: Decentralized LLM Teams for Perimeter Defense
This research paper investigates the architectural trade-offs between centralized and decentralized large language model (LLM) teams in a simulated perimeter-defense scenario against LLM-controlled intruders. The authors compare three approaches: a central commander, an auction-based consensus team, and independent agents across five network conditions. Results indicate that the consensus team achieved the lowest breach rate of 15%, compared to 32% for the commander and 29% for independent agents. The study further explores how limited radio range affects performance and how visual input helps distinguish civilian drones from intruders, noting that a one-sentence forewarning in the prompt significantly reduces misidentification rates.
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