The Cost of Consensus: Malignant Epistemic Herding and Adaptive Gating in Distributed Multi-Agent Search

2026-05-07

AI Summary

In bandwidth-constrained distributed multi-agent systems, inadequate communication can induce maladaptive cognitive convergence, impairing collective reasoning capabilities. This work formally introduces the notion of “cognitive alignment” and demonstrates that conventional coordination metrics often fail to detect such erroneous consensus. To address this issue, the authors propose an adaptive communication gating mechanism that integrates information-theoretic measures with multi-agent reinforcement learning to dynamically regulate belief sharing. This approach effectively balances communication overhead against collective belief quality in partially observable environments. Experimental results show that the method significantly reduces the rate of false consensus and enhances decision accuracy and robustness under low-bandwidth conditions in distributed perception and cooperative reconnaissance tasks.

Abstract

Distributed agents in real-world settings frequently must coordinate under uncertainty with only partial observations. Coordination is necessary to share beliefs to aid in task completion, but communication costs bandwidth, introduces latency, and if done poorly, can degrade collective reasoning. This tension is especially acute in bandwidth-constrained deployments such as distributed sensing networks, autonomous reconnaissance, and collaborative cyber defense, where excessive transmission carries direct operational costs. Existing work has focused on multi-agent exploration and communication strategies, but not on how communication frequency and content jointly shape the collective belief state. Central to this challenge is the degree to which agents maintain compatible internal beliefs about the environment, a property we term \textit{epistemic alignment}. When agents share beliefs effectively, they converge on correct hypotheses; when communication is poorly designed, agents may converge confidently on wrong ones. We formalize this distinction and show it is not detectable from coordination metrics alone such as Jensen-Shannon Divergence or rate to consensus.

Problem

• epistemic alignment
• multi-agent coordination
• communication cost
• collective belief
• consensus

Innovation

• epistemic alignment
• malignant epistemic herding
• adaptive gating
• distributed multi-agent systems
• collective belief state