Retrieval-Augmented Generation (RAG) systems empower large language models (LLMs) with external knowledge, yet struggle with efficiency-accuracy trade-offs when scaling to large knowledge graphs. Existing approaches often rely on monolithic graph retrieval, incurring unnecessary latency for simple queries and fragmented reasoning for complex multi-hop questions. To address these challenges, this paper propose SPLIT-RAG, a multi-agent RAG framework that addresses these limitations with question-driven semantic graph partitioning and collaborative subgraph retrieval. The innovative framework first create Semantic Partitioning of Linked Information, then use the Type-Specialized knowledge base to achieve Multi-Agent RAG. The attribute-aware graph segmentation manages to divide knowledge graphs into semantically coherent subgraphs, ensuring subgraphs align with different query types, while lightweight LLM agents are assigned to partitioned subgraphs, and only relevant partitions are activated during retrieval, thus reduce search space while enhancing efficiency. Finally, a hierarchical merging module resolves inconsistencies across subgraph-derived answers through logical verifications. Extensive experimental validation demonstrates considerable improvements compared to existing approaches.

翻译：检索增强生成（RAG）系统通过外部知识赋能大语言模型（LLM），但在扩展到大规模知识图谱时面临效率与准确性的权衡困境。现有方法通常依赖整体图检索，导致简单查询产生不必要延迟，而复杂多跳问题则引发碎片化推理。为应对这些挑战，本文提出SPLIT-RAG——一种多智能体RAG框架，通过问题驱动的语义图分割与协作式子图检索解决上述局限。该创新框架首先创建链接信息的语义分割，随后利用类型专精知识库实现多智能体RAG。属性感知图分割技术将知识图谱划分为语义连贯的子图，确保子图与不同查询类型对齐；轻量化LLM智能体被分配至分割后的子图，检索时仅激活相关分区，从而在提升效率的同时缩减搜索空间。最后，层级融合模块通过逻辑验证解决跨子图答案的不一致性问题。大量实验验证表明，该方法相较现有方案取得显著改进。