让智能体辩论更高效：根据任务难易自动调整协作规模
HCP-MAD:Heterogeneous Consensus-Progressive Reasoning for Efficient Multi-Agent Debate
- 用异构代理快速验证共识，简单任务早停节省算力
- 复杂任务自动扩展参与方，准确率提升且令牌消耗减少50%以上
- 适合需要高效推理的AI系统，尤其关注成本与精度平衡的场景
多智能体辩论（MAD）是一种协作框架，多个智能体通过生成推理并交替批判来逐步优化解决方案。现有方法分别优化回合内拓扑和回合间交互，难以根据任务复杂度动态调整令牌开销。本文提出异构共识-渐进式推理框架（HCP-MAD），以共识作为动态信号，实现渐进式推理。首先，异构共识验证使用一对异构代理进行快速共识检测，实现早期停止；其次，异构对等辩论采用自适应停止准则，终止推理轨迹的相互批判；最后，未解决任务通过增强集体投票机制，聚合额外智能体的多元视角。在六个基准测试上，HCP-MAD在显著降低令牌消耗的同时提升了准确率。代码已开源：https://github.com/fuyu66/HCP-MAD。
原文摘要 · Abstract (English)
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.
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