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Multi-agent debate systems are designed to derive accurate and consistentconclusions through adversarial interactions among agents. However, thesesystems often encounter challenges due to cognitive constraints, manifesting as(1) agents' obstinate adherence to incorrect viewpoints and (2) theirpropensity to abandon correct viewpoints. These issues are primarilyresponsible for the ineffectiveness of such debates. Addressing the challengeof cognitive constraints, we introduce a novel framework, the Multi-AgentDebate with Retrieval Augmented (MADRA). MADRA incorporates retrieval of priorknowledge into the debate process, effectively breaking cognitive constraintsand enhancing the agents' reasoning capabilities. Furthermore, we havedeveloped a self-selection module within this framework, enabling agents toautonomously select pertinent evidence, thereby minimizing the impact ofirrelevant or noisy data. We have comprehensively tested and analyzed MADRAacross six diverse datasets. The experimental results demonstrate that ourapproach significantly enhances performance across various tasks, proving theeffectiveness of our proposed method.

Apollo's Oracle: Retrieval-Augmented Reasoning in Multi-Agent Debates