Large language models (LLMs)-empowered autonomous agents are transforming both digital and physical environments by enabling adaptive, multi-agent collaboration. While these agents offer significant opportunities across domains such as finance, healthcare, and smart manufacturing, their unpredictable behaviors and heterogeneous capabilities pose substantial governance and accountability challenges. In this paper, we propose a blockchain-enabled layered architecture for regulatory agent collaboration, comprising an agent layer, a blockchain data layer, and a regulatory application layer. Within this framework, we design three key modules: (i) an agent behavior tracing and arbitration module for automated accountability, (ii) a dynamic reputation evaluation module for trust assessment in collaborative scenarios, and (iii) a malicious behavior forecasting module for early detection of adversarial activities. Our approach establishes a systematic foundation for trustworthy, resilient, and scalable regulatory mechanisms in large-scale agent ecosystems. Finally, we discuss the future research directions for blockchain-enabled regulatory frameworks in multi-agent systems.

翻译：大型语言模型（LLMs）赋能的自主智能体通过实现自适应的多智能体协作，正在改变数字与物理环境。尽管这些智能体在金融、医疗和智能制造等领域带来重大机遇，但其不可预测的行为和异构能力也引发了治理与问责方面的重大挑战。本文提出一种基于区块链的监管智能体协作分层架构，该架构包括智能体层、区块链数据层和监管应用层。在此框架内，我们设计了三个关键模块：（i）用于自动化问责的智能体行为追踪与仲裁模块，（ii）用于协作场景中信任评估的动态声誉评估模块，以及（iii）用于早期检测对抗性活动的恶意行为预测模块。我们的方法为大规模智能体生态系统中可信、弹性且可扩展的监管机制奠定了系统性基础。最后，我们探讨了多智能体系统中基于区块链的监管框架的未来研究方向。