Rethinking the Reliability of Multi-agent System: A Perspective from Byzantine Fault Tolerance

TL;DR

This paper explores the reliability of LLM-based agents in multi-agent systems from a Byzantine fault tolerance perspective, proposing a novel consensus mechanism that significantly improves stability and fault tolerance in diverse network topologies.

Contribution

It introduces CP-WBFT, a confidence probe-based weighted Byzantine Fault Tolerant consensus mechanism leveraging LLMs' capabilities to enhance MAS reliability under extreme fault conditions.

Findings

LLM-based agents show stronger skepticism to erroneous messages.

CP-WBFT outperforms traditional methods in accuracy and fault tolerance.

Achieves 85.7% fault tolerance in extreme Byzantine scenarios.

Abstract

Ensuring the reliability of agent architectures and effectively identifying problematic agents when failures occur are crucial challenges in multi-agent systems (MAS). Advances in large language models (LLMs) have established LLM-based agents as a major branch of MAS, enabling major breakthroughs in complex problem solving and world modeling. However, the reliability implications of this shift remain largely unexplored. i.e., whether substituting traditional agents with LLM-based agents can effectively enhance the reliability of MAS. In this work, we investigate and quantify the reliability of LLM-based agents from the perspective of Byzantine fault tolerance. We observe that LLM-based agents demonstrate stronger skepticism when processing erroneous message flows, a characteristic that enables them to outperform traditional agents across different topological structures. Motivated by the results of the pilot experiment, we design CP-WBFT, a confidence probe-based weighted Byzantine Fault Tolerant consensus mechanism to enhance the stability of MAS with different topologies. It capitalizes on the intrinsic reflective and discriminative capabilities of LLMs by employing a probe-based, weighted information flow transmission method to improve the reliability of LLM-based agents. Extensive experiments demonstrate that CP-WBFT achieves superior performance across diverse network topologies under extreme Byzantine conditions (85.7\% fault rate). Notably, our approach surpasses traditional methods by attaining remarkable accuracy on various topologies and maintaining strong reliability in both mathematical reasoning and safety assessment tasks.