让多个大模型像聊天一样互相讨论，达成更可靠的共识决策。
Consensus Is All You Need: Gossip-Based Reasoning Among Large Language Models
- 多个大模型通过互相交流答案和思路，逐步达成一致
- 相比单个模型，整体推理准确率显著提升且更抗错误
- 适合需要高可信度决策的场景，如医疗、金融
大语言模型虽快速发展，但无一模型在所有领域皆优，各有强弱。我们受分布式系统中八卦协议启发，让模型以点对点网络形式交换回答与思考过程，直至达成一致。每个大模型作为节点参与协作，通过持续交流逐步形成共享解决方案。实验表明，这种‘基于八卦的共识’机制能实现稳健、鲁棒且精准的多智能体推理，有效弥补个体模型缺陷，发挥集体优势。该方式类似人类协商共识，使AI更具协作性与可信任感，而非单一黑箱程序。
原文摘要 · Abstract (English)
Large language models have advanced rapidly, but no single model excels in every area -- each has its strengths and weaknesses. Instead of relying on one model alone, we take inspiration from gossip protocols in distributed systems, where information is exchanged with peers until they all come to an agreement. In this setup, models exchange answers and gradually work toward a shared solution. Each LLM acts as a node in a peer-to-peer network, sharing responses and thought processes to reach a collective decision. Our results show that this "gossip-based consensus" leads to robust, resilient, and accurate multi-agent AI reasoning. It helps overcome the weaknesses of individual models and brings out their collective strengths. This approach is similar to how humans build consensus, making AI seem more collaborative and trustworthy instead of just a black-box program.
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