📚 A framework that fixes redundant flat-retrieval RAG by navigating a knowledge graph hierarchically. Title: LeanRAG URL: 📦 Overview LeanRAG is a graph-based RAG framework that combines knowledge graphs with hierarchical retrieval. It was accepted to AAAI 2026. 🔍 The problem it solves Naive retrieval just collects related chunks flatly, leading to overlapping, redundant content and ignoring structural links between documents. LeanRAG traverses semantically aggregated upper layers to produce well-grounded answers with less redundancy. 🛠 The method (5 stages) ・Semantic aggregation: cluster entities into summary nodes with adjacency relations ・Knowledge graph construction: connect entities and summaries in a multi-layer graph ・Hierarchical retrieval: anchor the query at fine-grained entities, then traverse upward to gather evidence ・Redundancy-aware synthesis: streamline overlapping paths ・Generation: an LLM answers from the gathered evidence 📊 Results ・About 46% lower retrieval redundancy versus flat retrieval ・On the Mix benchmark, LeanRAG 8.59 beats HiRAG 8.08, GraphRAG 7.87, and LightRAG 7.61 ・Win rates of 97.3% vs NaiveRAG, 78.1% vs GraphRAG, 81.2% vs LightRAG, and 100% vs FastGraphRAG A compelling option for anyone building a low-redundancy, knowledge-graph-grounded RAG. #RAG# #KnowledgeGraph#