Multi-agent debate has proven effective in improving large language models
quality for reasoning and factuality tasks. While various role-playing
strategies in multi-agent debates have been explored, in terms of the
communication among agents, existing approaches adopt a brute force algorithm
-- each agent can communicate with all other agents. In this paper, we
systematically investigate the effect of communication connectivity in
multi-agent systems. Our experiments on GPT and Mistral models reveal that
multi-agent debates leveraging sparse communication topology can achieve
comparable or superior performance while significantly reducing computational
costs. Furthermore, we extend the multi-agent debate framework to multimodal
reasoning and alignment labeling tasks, showcasing its broad applicability and
effectiveness. Our findings underscore the importance of communication
connectivity on enhancing the efficiency and effectiveness of the "society of
minds" approach.

多代理辩论通过稀疏通信拓扑可以在大大降低计算成本的同时实现相当或更高的性能，扩展至多模态推理和对齐标注任务，强调了通信连通性对于增强 “心智集合体” 方法的效率和有效性的重要性。