Modular Task Decomposition and Dynamic Collaboration in Multi-Agent Systems Driven by Large Language Models

TL;DR

This paper introduces a multi-agent system driven by large language models that decomposes complex tasks into sub-tasks, dynamically schedules collaboration, and maintains efficiency and stability in complex environments.

Contribution

It presents a novel architecture combining language-driven task decomposition with dynamic multi-agent collaboration, improving performance and robustness over existing methods.

Findings

Outperforms existing approaches in success rate and robustness

Achieves better balance between task complexity and communication overhead

Demonstrates effective language-driven task decomposition and coordination

Abstract

This paper addresses the limitations of a single agent in task decomposition and collaboration during complex task execution, and proposes a multi-agent architecture for modular task decomposition and dynamic collaboration based on large language models. The method first converts natural language task descriptions into unified semantic representations through a large language model. On this basis, a modular decomposition mechanism is introduced to break down the overall goal into multiple hierarchical sub-tasks. Then, dynamic scheduling and routing mechanisms enable reasonable division of labor and realtime collaboration among agents, allowing the system to adjust strategies continuously according to environmental feedback, thus maintaining efficiency and stability in complex tasks. Furthermore, a constraint parsing and global consistency mechanism is designed to ensure coherent connections between sub-tasks and balanced workload, preventing performance degradation caused by redundant communication or uneven resource allocation. The experiments validate the architecture across multiple dimensions, including task success rate, decomposition efficiency, sub-task coverage, and collaboration balance. The results show that the proposed method outperforms existing approaches in both overall performance and robustness, achieving a better balance between task complexity and communication overhead. In conclusion, this study demonstrates the effectiveness and feasibility of language-driven task decomposition and dynamic collaboration in multi-agent systems, providing a systematic solution for task execution in complex environments.