Multi-Agent Debate (MAD) is a collaborative framework in which multiple agents iteratively refine solutions through the generation of reasoning and alternating critique cycles. Current work primarily optimizes intra-round topologies and inter-round interactions separately, limiting the adaptation of token costs to task complexity. This work introduces Heterogeneous Consensus-Progressive Reasoning for Efficient Multi-Agent Debate (HCP-MAD), leveraging consensus as a dynamic signal to facilitate progressive reasoning. The core motivation is that a majority of straightforward tasks can be effectively resolved via lightweight pair-agent debates, while complex tasks require expanded collaboration. Firstly, Heterogeneous Consensus Verification conducts rapid consensus verification using a pair of heterogeneous agents for early stopping. Next, Heterogeneous Pair-Agent Debate applies an adaptive stopping criterion to terminate mutual critique of reasoning traces. Finally, the unresolved tasks are addressed through Escalated Collective Voting by aggregating diverse perspectives from additional agents. Experiments across six benchmarks show that HCP-MAD enhances accuracy while substantially reducing token costs. Code is https://github.com/fuyu66/HCP-MAD.

翻译：多智能体辩论是一种协作框架，其中多个智能体通过生成推理和交替批评循环迭代优化解决方案。当前研究主要分别优化轮内拓扑结构和轮间交互，限制了令牌成本对任务复杂度的适应性。本文提出面向高效多智能体辩论的异构共识渐进推理方法，利用共识作为动态信号促进渐进推理。核心动机在于：大多数简单任务可通过轻量级双智能体辩论有效解决，而复杂任务则需要扩展协作。首先，异构共识验证采用一对异构智能体进行快速共识验证以实现早期停止。其次，异构双智能体辩论应用自适应停止准则终止推理轨迹的相互批评。最后，未解决的任务通过升级式集体投票聚合额外智能体的多元视角处理。在六个基准上的实验表明，HCP-MAD在显著降低令牌成本的同时提升了准确率。代码地址：https://github.com/fuyu66/HCP-MAD。