LivingRAG: Augmenting Graph RAG with Experience· LivingRAG：用经验增强图谱RAG
Graph-based RAG improves multi-hop question answering by organizing evidence as a knowledge graph. However, most existing RAG systems process each query in isolation and discard useful reasoning from the LLM's response after inference. As a result, later related queries need to retrieve evidence and reason from scratch. We propose LivingRAG, a Graph RAG framework with writable and reusable reasoning experience. LivingRAG adds a writable experience store to a graph-based retrieval backbone, enabling verified experiences to be reused during inference in two ways. Stored graph signals help retrieval find entities and passages that were useful in earlier related queries. Stored summaries provide a reference reasoning pattern for answer generation. We analyze online QA streams and find reusable
利用可写入的经验存储增强图谱RAG，提高多步问答系统的效率。
- 核心方法
- 提出LivingRAG框架，通过添加可写入的经验存储模块，存储并重用先前查询中的有用推理和知识图谱信号，辅助后续相关查询的证据检索和答案生成。
- 适合谁读
- 研究者 / 工程师
- 要解决的问题
- 现有图谱RAG系统处理每个查询时孤立作业，导致相关查询重复推理，效率低下。
- 关键实验
- 分析了在线问答流，验证了可重用的经验能有效提升系统性能（详细实验数据见论文）。
- 主要贡献
- LivingRAG框架通过经验重用机制，提高了多步问答系统的推理效率和准确性。
- 意义与局限
- LivingRAG为图谱RAG系统引入了经验重用机制，有望提高处理相关多步查询的能力，但也需要考虑存储成本和数据隐私问题。