Group Perspective Matters: Regulating Debate Relationships Can Mitigate Blind Conformity in Multi-Agent Debate
3.20T1 sourcearXiv cs.MA
arXiv cs.MAoriginal source ↗
Source record
Published by arXiv cs.MA (T1 source). The original is at https://arxiv.org/abs/2608.03648.
Pipeline notes
The summary and note below are generated by the signal pipeline — they are Beyond Desk’s reading, not quotations from the source.
SummaryPaper proposes DEAR, a framework using two reinforcement learning agents to dynamically regulate debate relationships among LLMs in multi-agent debate. It quantifies group consensus and divergence to mitigate blind conformity, with experiments showing improved reasoning performance and lower token consumption.
Why it mattersTargets a known failure mode in multi-agent LLM debate with a group-level mechanism instead of per-agent confidence tweaks, and reports concrete efficiency gains.
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