Enabling Regulatory Multi-Agent Collaboration: Architecture, Challenges, and Solutions
Authors: Qinnan Hu, Yuntao Wang, Yuan Gao, Zhou Su, Linkang Du, Qichao Xu
Organizations: School of Cyber Science and Engineering, Xi’an Jiaotong University, China · School of Mechatronic Engineering and Automation, Shanghai University, China
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, an off-chain computation layer, and an on-chain anchoring 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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Fig. 1: An overview of LLM-based agents.
Fig. 2: Applications of multi-agent cooperation.
Fig. 3: Hierarchical regulatory architecture with off-chain computation and on-chain anchoring. Signed agent records are aggregated into Merkle trees off-chain and periodically committed to a permissioned ledger for verifiable auditing.
Fig. 4: Illustration of regulatory solutions under the proposed hybrid on-chain/off-chain architecture, including: (a) diffusion-based malicious behavior forecasting, (b) agent behavior tracing and arbitration, and (c) dynamic agent reputation evaluation.
Fig. 5: Detection F1-score across the six mainstream multi-agent attack types.
Fig. 6: End-to-end latency under increasing numbers of collaborative agents.
Qinnan Hu is currently pursuing the Ph.D degree with the School of Cyber Science and Engineering of Xi’an Jiaotong University, China. His research interests include blockchain system security and LLM Agents.
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Yuntao Wang is currently an Assistant Professor with the School of Cyber Science and Engineering in Xi’an Jiaotong University, China. His research interests include security and privacy in UAV networks and LLM Agents.
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Yuan Gao is currently pursuing the Ph.D degree with the School of Cyber Science and Engineering of Xi’an Jiaotong University, China. His research interests include blockchain and IoT system.
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Zhou Su is a professor with Xi’an Jiaotong University and his research interests include multimedia communication, wireless communication, network security and network traffic. He is an Associate Editor of IEEE Internet of Things Journal , and IEEE Open Journal of the Computer Society . He is the chair of IEEE VTS Xi’an Chapter Section.
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Linkang Du is currently an Assistant Professor at the School of Cyber Science and Engineering, Xi’an Jiaotong University, Xi’an, China. His research interests include data privacy protection and trustworthy machine learning.
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Qichao Xu is currently an Associate Professor with Shanghai University. His research interests are in trust and security.