LLM Nexus: A Framework for Multi-Agent Collaboration in Data Modeling and Analytics using Large Language Models

Abstract

This paper introduces LLM Nexus, a novel framework designed to enable and enhance multi-agent collaboration using Large Language Models (LLMs). Unlike traditional multi-agent systems that rely on symbolic reasoning or predefined communication patterns, LLM Nexus leverages the emergent reasoning capabilities and contextual understanding of LLMs to facilitate decentralized decision-making, context-aware dialogue management, and dynamic role assignment among intelligent agents. The paper describes the key architectural components of LLM Nexus, including its communication protocols, adaptive role specialization mechanisms, and shared knowledge infrastructure. It also presents practical use cases in research automation, educational systems, and business process optimization, demonstrating how the framework improves efficiency, scalability, collaboration, and system robustness. Furthermore, the study examines the limitations of LLM-based multi-agent systems, including computational costs, ethical considerations, and challenges in performance evaluation. It concludes by proposing future research directions focused on integrating symbolic reasoning, reinforcement learning, and explainable AI to enhance transparency, interpretability, and control within collaborative AI ecosystems.