用声誉系统防止大模型多智能体合作崩溃
Reputation as a Solution to Cooperation Collapse in LLM-based MASs
- 设计双层动态声誉框架，基于直接互动和间接传言构建信誉
- 三种场景下均避免合作崩溃，维持长期协作
- 能自动生成合作集群与排斥剥削者等涌现行为，适合研究AI协作的学者
合作是人类社会与人工智能系统中的核心议题。然而近期研究发现，基于大语言模型（LLMs）的多智能体系统（MASs）中可能出现合作崩溃。为应对这一挑战，本文探索声誉系统作为解决方案。提出RepuNet——一种动态双层声誉框架，同时建模个体声誉演化与系统网络结构变迁。基于直接交互与间接传言，智能体可为自己及同伴建立声誉，并决定是否连接或断开其他智能体以进行后续互动。通过三种不同场景验证，RepuNet有效防止合作崩溃，促进并维持了大规模语言模型驱动的多智能体系统中的合作。此外，我们发现声誉系统能催生丰富涌现行为，如合作集群形成、剥削性智能体的社会隔离，以及更倾向于传播正面传闻而非负面信息。项目代码已开源：https://github.com/RGB-0000FF/RepuNet。
原文摘要 · Abstract (English)
Cooperation has long been a fundamental topic in both human society and AI systems. However, recent studies indicate that the collapse of cooperation may emerge in multi-agent systems (MASs) driven by large language models (LLMs). To address this challenge, we explore reputation systems as a remedy. We propose RepuNet, a dynamic, dual-level reputation framework that models both agent-level reputation dynamics and system-level network evolution. Specifically, driven by direct interactions and indirect gossip, agents form reputations for both themselves and their peers, and decide whether to connect or disconnect other agents for future interactions. Through three distinct scenarios, we show that RepuNet effectively avoids cooperation collapse, promoting and sustaining cooperation in LLM-based MASs. Moreover, we find that reputation systems can give rise to rich emergent behaviors in LLM-based MASs, such as the formation of cooperative clusters, the social isolation of exploitative agents, and the preference for sharing positive gossip rather than negative ones. The GitHub repository for our project can be accessed via the following link: https://github.com/RGB-0000FF/RepuNet.
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