用区块链构建可监管的智能体协作系统，解决行为不可控问题。
Enabling Regulatory Multi-Agent Collaboration: Architecture, Challenges, and Solutions
- 三层架构：智能体层、链上数据层、监管应用层协同运行。
- 三模块保障可信：行为追溯、动态信誉评估、恶意行为预测。
- 适合研究监管科技与多智能体系统的学者或工程师。
大语言模型赋能的自主智能体正在改变数字与物理环境，推动多智能体协作。尽管在金融、医疗、智能制造等领域带来巨大机遇，其不可预测的行为和异质能力仍带来治理与责任难题。本文提出一种基于区块链的分层监管架构，包含智能体层、区块链数据层与监管应用层。设计三个核心模块：(i) 智能体行为追踪与仲裁模块，实现自动化问责；(ii) 动态信誉评估模块，用于协作场景中的信任判断；(iii) 恶意行为预测模块，实现对对抗性活动的早期识别。该方法为大规模智能体生态系统的可信、鲁棒与可扩展监管机制奠定系统基础。最后，探讨了区块链赋能的多智能体系统监管框架未来研究方向。
原文摘要 · Abstract (English)
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.
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