How can LLMs reach unanimous consensus in multi-agent systems?
Achieving Unanimous Consensus in Decision Making Using Multi-Agents
This paper proposes a novel blockchain consensus mechanism using Large Language Models (LLMs) as multi-agents that engage in structured deliberations to reach unanimous agreement. Unlike traditional Proof-of-Work or Proof-of-Stake, this approach prioritizes individual opinions and nuanced discussion.
Key points for LLM-based multi-agent systems: LLMs act as deliberating agents, exchanging and refining arguments through iterative rounds (turns) to achieve consensus. The framework uses a hybrid of zero-shot and chain-of-thought prompting, and the number of agents and turns influences convergence speed and accuracy. Challenges like LLM hallucinations, resource consumption, and security risks are acknowledged, and potential solutions like judge systems, reputation-based incentives, and watermarking are proposed. The system is implemented on the Nimiq blockchain as a proof of concept.