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

Rathin Chandra Shit, Sharmila Subudhi

TL;DR

This work tackles the challenge of reliable, scalable consensus in collaborative multi-agent AI systems, especially under dynamic task requirements and heterogeneous agent capabilities. It introduces HACN, a three-tier hierarchical framework with dynamic clustering, cross-cluster debates, and global arbitration to achieve consensus with reduced communication overhead. The key contributions include reducing communication complexity from $O(n^2)$ to $O(n)$, employing confidence-weighted voting, and providing hierarchical escalation guarantees of convergence, validated by simulation and benchmarking. The approach enables large-scale, adaptive consensus suitable for distributed AI, robotics, and related domains, with practical implications for scalable cooperative intelligence.

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.

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

TL;DR

This work tackles the challenge of reliable, scalable consensus in collaborative multi-agent AI systems, especially under dynamic task requirements and heterogeneous agent capabilities. It introduces HACN, a three-tier hierarchical framework with dynamic clustering, cross-cluster debates, and global arbitration to achieve consensus with reduced communication overhead. The key contributions include reducing communication complexity from to , employing confidence-weighted voting, and providing hierarchical escalation guarantees of convergence, validated by simulation and benchmarking. The approach enables large-scale, adaptive consensus suitable for distributed AI, robotics, and related domains, with practical implications for scalable cooperative intelligence.

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 communication complexity, as opposed to the 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.

Paper Structure

This paper contains 25 sections, 1 equation, 5 figures, 2 algorithms.

Table of Contents

  1. Introduction
  2. Suggested Framework Design
  3. Three-Tier Architecture
  4. Local Consensus Clusters (Tier 1)
  5. Inter-Cluster Coordination (Tier 2)
  6. Global Orchestration Engine (Tier 3)
  7. Proposed Hierarchical Adaptive Consensus Network
  8. Dynamic Cluster Formation
  9. Multi-Level Consensus Process
  10. Adaptive Decision Rules
  11. Evaluation Methodology
  12. Simulation Environment Setup
  13. Result Analysis
  14. Comparative Baseline
  15. Implementation Considerations
  16. ...and 10 more sections

Figures (5)

  • Figure 1: Proposed Three-Tier Architecture showing hierarchical consensus flow from local clusters (Tier 1) through inter-cluster coordination (Tier 2) to global orchestration (Tier 3)
  • Figure 2: Dynamic agent clustering visualization showing agents grouped by capability similarity in a two-dimensional expertise space
  • Figure 3: Multi-level consensus process flow showing hierarchical escalation with confidence-weighted voting and adaptive thresholds
  • Figure 4: Performance of Proposed $\mathcal{HACN}$ Architecture
  • Figure 5: Comparison between $\mathcal{HACN}$ and existing consensus frameworks