Heterogeneous Consensus-Progressive Reasoning for Efficient Multi-Agent Debate
This work addresses the high token cost problem in multi-agent debate systems for AI reasoning tasks, offering an efficient adaptive approach that balances performance and resource usage.
HCP-MAD introduces a three-stage progressive reasoning mechanism for multi-agent debate that uses consensus as a dynamic signal to adaptively allocate computational resources, achieving significant accuracy improvements while substantially reducing token costs across multiple benchmarks.
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, which still results in high token costs regardless of 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. Consequently, HCP-MAD employs a three-stage progressive reasoning mechanism to develop adaptive solutions across varying task complexities. Firstly, Heterogeneous Consensus Verification conducts rapid consensus verification using a pair of heterogeneous agents for early stopping. Next, the Heterogeneous Pair-Agent Debate applies an adaptive stopping criterion to dynamically terminate mutual critique of recorded reasoning traces. Finally, the unresolved tasks are addressed through Escalated Collective Voting by aggregating diverse perspectives from additional agents. Experiments across multiple benchmarks show that HCP-MAD significantly enhances accuracy while substantially reducing token costs.