Consensus Is All You Need: Gossip-Based Reasoning Among Large Language Models

TL;DR

This paper introduces a gossip-based consensus method where large language models exchange information in a peer-to-peer network to collaboratively improve reasoning, robustness, and trustworthiness.

Contribution

It proposes a novel multi-agent reasoning framework inspired by distributed gossip protocols, enhancing collective decision-making among large language models.

Findings

Improved accuracy through collective reasoning

Enhanced robustness against individual model weaknesses

Increased trustworthiness and collaboration in AI systems

Abstract

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.