Hierarchical Adaptive Consensus Network: A Dynamic Framework for Scalable Consensus in Collaborative Multi-Agent AI Systems

• URL: http://arxiv.org/abs/2511.17586v1
• Date: Sun, 16 Nov 2025 15:09:15 GMT
• Title: Hierarchical Adaptive Consensus Network: A Dynamic Framework for Scalable Consensus in Collaborative Multi-Agent AI Systems
• Authors: Rathin Chandra Shit, Sharmila Subudhi,
• Abstract summary: This article introduces a three-tier architecture for consensus strategies in multi-agent systems.<n>The first layer collects the confidence-based voting outcomes of several local agent clusters.<n>The second level facilitates inter-cluster communication through cross-clustered partial knowledge sharing and dynamic timeouts.<n>The third layer provides system-wide coordination and final arbitration by employing a global orchestration framework.
• Score: 0.5505634045241287
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The consensus strategies used in collaborative multi-agent systems (MAS) face notable challenges related to adaptability, scalability, and convergence certainties. These approaches, including structured workflows, debate models, and iterative voting, often lead to communication bottlenecks, stringent decision-making processes, and delayed responses in solving complex and evolving tasks. This article introduces a three-tier architecture, the Hierarchical Adaptive Consensus Network (\hacn), which suggests various consensus policies based on task characterization and agent performance metrics. The first layer collects the confidence-based voting outcomes of several local agent clusters. In contrast, the second level facilitates inter-cluster communication through cross-clustered partial knowledge sharing and dynamic timeouts. The third layer provides system-wide coordination and final arbitration by employing a global orchestration framework with adaptable decision rules. The proposed model achieves $\bigO(n)$ communication complexity, as opposed to the $\bigO(n^2)$ complexity of the existing fully connected MAS. Experiments performed in a simulated environment yielded a 99.9\% reduction in communication overhead during consensus convergence. Furthermore, the proposed approach ensures consensus convergence through hierarchical escalation and dynamic adaptation for a wide variety of complicated tasks.

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