SVR-MAD: A Bayesian-Inspired Framework for Posterior-Guided Multi-Agent Debate
For LLM-based multi-agent systems, SVR-MAD addresses the scalability bottleneck of context growth without sacrificing accuracy.
SVR-MAD reduces token cost by up to 61% while matching or improving accuracy compared to existing multi-agent debate methods by using Bayesian-inspired posterior-guided pruning of communication.
Multi-Agent Debate (MAD) improves LLM-agent accuracy but suffers from rapid context growth, limiting scalability in larger multi-agent settings. Existing methods prune low-utility communications using prior signals, such as token-level log-likelihoods or LLM self-reported confidence. However, these signals become unreliable under hallucination, degrading the accuracy of MAD methods that rely on them. We propose SVR-MAD, a Bayesian-inspired MAD framework that treats pre-debate signals as priors and debate outcomes as posterior-style evidence for estimating agent correctness. SVR-MAD uses this evidence to incrementally construct the communication graph, prioritizing agents whose answers survive peer challenges. Experiments across multiple LLMs and benchmarks show that SVR-MAD reduces token cost by up to 61% while matching or improving accuracy relative to the most accurate competing MAD baseline.