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New model explains how LLM agents can reach incorrect consensus

By PulseAugur Editorial · [2 sources] ·

A new model explains how multi-agent systems of LLMs can sometimes reach incorrect consensus due to agents withholding dissent. The model identifies a critical withholding rate, below which discussion improves accuracy. Empirical tests on LLMs and benchmarks like HiddenBench and MedEInst confirm that instructing agents to avoid withholding dissent increases the gains from discussion. AI

IMPACT Provides a theoretical framework and empirical evidence for improving the reliability of multi-agent LLM systems.

RANK_REASON The cluster contains a research paper detailing a new model for multi-agent LLM behavior.

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New model explains how LLM agents can reach incorrect consensus

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The cluster contains a research paper detailing a new model for multi-agent LLM behavior.
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COVERAGE [2]

  1. arXiv cs.CL TIER_1 English(EN) · Chand Sahil Mansuri, Xin Wang, Mengying Li, Bryan Acton, Rory Eckardt, Dhaval Patel, Sadamori Kojaku ·

    Multi-agent discussion gains less when dissent is withheld

    arXiv:2609.38324v1 Announce Type: cross Abstract: Multi-agent systems of LLMs add discussion to majority voting and are therefore expected to be more capable. However, empirical reports conflict on whether discussion improves accuracy or leads to an incorrect consensus. Here, we …

  2. arXiv cs.MA (Multiagent) TIER_1 English(EN) · Sadamori Kojaku ·

    Multi-agent discussion gains less when dissent is withheld

    Multi-agent systems of LLMs add discussion to majority voting and are therefore expected to be more capable. However, empirical reports conflict on whether discussion improves accuracy or leads to an incorrect consensus. Here, we introduce a parsimonious model that explains when …

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