多智能体辩论何时优于多数投票：LVD 框架给出两个前提条件
When Debate Helps: Proposal Supply and Verification-Aware Readout in Multi-Agent Reasoning
这篇论文解释了为啥多智能体辩论经常跑不过简单多数投票，还给出了 LVD 方法和可复现代码，做 agent 推理的可以试试。
arXiv 论文提出辩论要胜过多数投票需满足两个机制：提案供给能浮出正确答案，以及读出阶段能识别它。作者用 recoverable headroom 度量"正确提案存在但多数答案错误"的情况，并构建 Latent Verification Debate（LVD）模型，让候选提案在最终生成前获得针对答案的验证证据。实验在两个 backbone 和匹配预算的推理基准上进行，基于 coverage 目标选出的神经-thicket 智能体社会提升了互补提案供给与总体准确率。代码开源在 Wang-ML-Lab 的 GitHub 仓库。
When Debate Helps: Proposal Supply and Verification-Aware Readout in Multi-Agent Reasoning
Multi-agent debate can improve reasoning, yet often fails to beat simple majority voting. We argue that successful debate requires two distinct mechanisms: proposal supply must surface a correct answer, and readout must identify that answer when voting misses it. We formalize the first requirement through recoverable headroom, which measures cases where a correct proposal is available but the majority answer is wrong. For the second, we develop Latent Verification Debate (LVD), an accounting model in which candidate proposals receive answer-specific verification evidence before final generation. Controlled fixed-proposal interventions estimate this latent effect in equivalent peer-support units and show that correct evidence changes answer probabilities and generated decisions while proposal supply remains fixed. To improve proposal supply, we construct societies from neural-thicket agents using labeled and label-free coverage objectives. Across two backbones and matched-budget reasoning benchmarks, coverage-selected societies increase complementary proposal supply and improve aggregate accuracy in repeated stochastic evaluations. Round-level controls further show that interaction provides gains beyond applying the same finalizer directly to the initial proposals. These results identify proposal coverage and truth-sensitive evidence use as complementary conditions for debate to outperform voting. Code is available at https://github.com/Wang-ML-Lab/when-debate-helps.
