Multi-agent debate systems are designed to derive accurate and consistent conclusions through adversarial interactions among agents. However, these systems often encounter challenges due to cognitive constraints, manifesting as (1) agents' obstinate adherence to incorrect viewpoints and (2) their propensity to abandon correct viewpoints. These issues are primarily responsible for the ineffectiveness of such debates. Addressing the challenge of cognitive constraints, we introduce a novel framework, the Multi-Agent Debate with Retrieval Augmented (MADRA). MADRA incorporates retrieval of prior knowledge into the debate process, effectively breaking cognitive constraints and enhancing the agents' reasoning capabilities. Furthermore, we have developed a self-selection module within this framework, enabling agents to autonomously select pertinent evidence, thereby minimizing the impact of irrelevant or noisy data. We have comprehensively tested and analyzed MADRA across six diverse datasets. The experimental results demonstrate that our approach significantly enhances performance across various tasks, proving the effectiveness of our proposed method.

翻译：多智能体辩论系统旨在通过智能体间的对抗性交互得出准确且一致的结论。然而，这类系统常因认知约束而面临挑战，表现为：（1）智能体固执地坚持错误观点；（2）智能体易放弃正确观点。这些问题正是此类辩论效果不佳的主因。针对认知约束难题，我们提出一种新颖框架——检索增强多智能体辩论（MADRA）。MADRA将先验知识检索融入辩论过程，有效打破认知约束并提升智能体推理能力。此外，我们在此框架中设计了一个自选择模块，使智能体能够自主筛选相关证据，从而降低无关或噪声数据的干扰。我们基于六个多样化数据集对MADRA进行了全面测试与分析。实验结果表明，该方法能显著提升各类任务的性能，验证了所提方法的有效性。