AgentGroupChat-V2: Divide-and-Conquer Is What LLM-Based Multi-Agent System Need

• URL: http://arxiv.org/abs/2506.15451v1
• Date: Wed, 18 Jun 2025 13:24:04 GMT
• Title: AgentGroupChat-V2: Divide-and-Conquer Is What LLM-Based Multi-Agent System Need
• Authors: Zhouhong Gu, Xiaoxuan Zhu, Yin Cai, Hao Shen, Xingzhou Chen, Qingyi Wang, Jialin Li, Xiaoran Shi, Haoran Guo, Wenxuan Huang, Hongwei Feng, Yanghua Xiao, Zheyu Ye, Yao Hu, Shaosheng Cao,
• Abstract summary: Large language model based multi-agent systems have demonstrated significant potential in social simulation and complex task resolution domains.<n>We introduce AgentGroupChat-V2, a novel framework addressing these challenges through three core innovations.<n>Experiments demonstrate AgentGroupChat-V2's superior performance across diverse domains.
• Score: 35.88121318813734
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language model based multi-agent systems have demonstrated significant potential in social simulation and complex task resolution domains. However, current frameworks face critical challenges in system architecture design, cross-domain generalizability, and performance guarantees, particularly as task complexity and number of agents increases. We introduces AgentGroupChat-V2, a novel framework addressing these challenges through three core innovations: (1) a divide-and-conquer fully parallel architecture that decomposes user queries into hierarchical task forest structures enabling dependency management and distributed concurrent processing. (2) an adaptive collaboration engine that dynamically selects heterogeneous LLM combinations and interaction modes based on task characteristics. (3) agent organization optimization strategies combining divide-and-conquer approaches for efficient problem decomposition. Extensive experiments demonstrate AgentGroupChat-V2's superior performance across diverse domains, achieving 91.50% accuracy on GSM8K (exceeding the best baseline by 5.6 percentage points), 30.4% accuracy on competition-level AIME (nearly doubling other methods), and 79.20% pass@1 on HumanEval. Performance advantages become increasingly pronounced with higher task difficulty, particularly on Level 5 MATH problems where improvements exceed 11 percentage points compared to state-of-the-art baselines. These results confirm that AgentGroupChat-V2 provides a comprehensive solution for building efficient, general-purpose LLM multi-agent systems with significant advantages in complex reasoning scenarios. Code is available at https://github.com/MikeGu721/AgentGroupChat-V2.

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