Honest heterogeneous peers in LLM debates lower harmful revision rates (e.g., 89% to 35%), while adversarial peers raise them (to 90%), and provide defense even against same-family adversaries.
arXiv preprint arXiv:2505.22960 , year=
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A derived scaling law R(N) = 1/(1 + c(N-1)N^{-β}) fits answer diversity and correctness across 44 LLM multi-agent conditions with R² > 0.99, classifying regimes by β and showing only heterogeneous teams escape hard-ceiling saturation.
Multi-agent debate degrades generation but boosts error detection in data cleaning; a derived benefit condition predicts outcomes across tasks and generalizes to other domains.
The paper introduces a three-source decomposition showing that answer flips in multi-agent LLM debate include 37% spontaneous instability and 29% harmful conformity, with even vacuous reasoning persuading 20-39% of resistant agents and interventions reducing harmful conformity by 13.6 points.
Closed-system multi-step LLM reasoning is subject to an information-theoretic bound where mutual information with evidence decreases, preserving accuracy while eroding faithfulness, with EGSR recovering it on SciFact and FEVER.
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Heterogeneous LLM Debate Under Adversarial Peers: Honest Gains, Replacement Costs, and Resilience
Honest heterogeneous peers in LLM debates lower harmful revision rates (e.g., 89% to 35%), while adversarial peers raise them (to 90%), and provide defense even against same-family adversaries.
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The Ringelmann Effect in Multi-Agent LLM Systems: A Scaling Law for Effective Team Size
A derived scaling law R(N) = 1/(1 + c(N-1)N^{-β}) fits answer diversity and correctness across 44 LLM multi-agent conditions with R² > 0.99, classifying regimes by β and showing only heterogeneous teams escape hard-ceiling saturation.
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When Helping Hurts and How to Fix It: Multi-Agent Debate for Data Cleaning
Multi-agent debate degrades generation but boosts error detection in data cleaning; a derived benefit condition predicts outcomes across tasks and generalizes to other domains.
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Not All Flips Are Conformity: Decomposing Stance Convergence in Multi-Agent LLM Debate
The paper introduces a three-source decomposition showing that answer flips in multi-agent LLM debate include 37% spontaneous instability and 29% harmful conformity, with even vacuous reasoning persuading 20-39% of resistant agents and interventions reducing harmful conformity by 13.6 points.
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The Reasoning Trap: An Information-Theoretic Bound on Closed-System Multi-Step LLM Reasoning
Closed-system multi-step LLM reasoning is subject to an information-theoretic bound where mutual information with evidence decreases, preserving accuracy while eroding faithfulness, with EGSR recovering it on SciFact and FEVER.