🤖 AI Summary
To address coarse-grained task decomposition and rigid collaboration mechanisms in single-agent systems for complex tasks, this paper proposes a modular multi-agent architecture grounded in large language models (LLMs). Our approach enables fine-grained, semantically driven hierarchical task parsing, constraint-aware subtask partitioning, and environment-feedback-guided dynamic scheduling. Key innovations include a global consistency preservation mechanism, lightweight communication routing, and a collaboration balancing algorithm—collectively reducing communication overhead while enhancing policy adaptability. Extensive experiments demonstrate that our method significantly outperforms existing baselines across multiple metrics: task success rate, decomposition efficiency, subtask coverage, and collaboration balance. Moreover, the system exhibits markedly improved overall robustness and execution stability.
📝 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.