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.

翻译：合作长期以来一直是人类社会与人工智能系统中的一个基础性议题。然而，近期研究表明，在由大型语言模型（LLMs）驱动的多智能体系统（MASs）中可能出现合作崩溃。为应对这一挑战，我们探索了声誉系统作为一种解决方案。我们提出了RepuNet，一个动态的双层声誉框架，该框架同时建模了智能体层面的声誉动态与系统层面的网络演化。具体而言，在直接交互和间接流言的驱动下，智能体既为自己也为同伴形成声誉评估，并据此决定是否在未来的交互中连接或断开与其他智能体的联系。通过三个不同的场景，我们证明RepuNet能有效避免合作崩溃，促进并维持基于LLM的MASs中的合作。此外，我们发现声誉系统能在基于LLM的MASs中引发丰富的涌现行为，例如合作集群的形成、剥削型智能体的社会性孤立，以及分享正面流言而非负面流言的偏好。本项目的GitHub仓库可通过以下链接访问：https://github.com/RGB-0000FF/RepuNet。