让RAG记住推理经验，提升多跳问答效率与准确率
LivingRAG: Augmenting Graph RAG with Experience
- 将大模型推理过程中的有效信息存入可写可读的经验库
- 在后续相关问题中复用历史实体、图结构和推理模式，准确率提升
- 适合长期在线问答场景，减少重复检索与计算开销
基于图的RAG通过将证据组织为知识图谱，提升了多跳问答能力。然而现有系统对每个查询独立处理，推理后丢弃有用信息，导致后续相关查询仍需从头检索和推理。我们提出LivingRAG，一种具备可写可重用推理经验的图RAG框架。它在图检索主干上添加可写的体验存储，支持两种方式复用已验证的经验：存储的图信号帮助检索先前有用的实体和段落；存储的摘要提供参考推理路径以生成答案。通过对在线QA流分析，发现共享实体、图邻域和问题模板中存在可复用信号。在多跳问答基准上的实验表明，LivingRAG优于强基线模型，在复用相关先验经验时还减少了完成令牌使用量。
原文摘要 · Abstract (English)
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 signals from shared entities, graph neighborhoods, and question templates. Experiments on multi-hop QA benchmarks show that LivingRAG improves accuracy over strong RAG baselines and reduces completion-token use when relevant prior experience is reused.
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