用人类联想记忆启发，实现高效多跳知识检索。
EcphoryRAG: Re-Imagining Knowledge-Graph RAG via Human Associative Memory
- 基于实体线索进行多跳关联搜索，重构知识图谱检索
- 索引仅存核心实体，降低94%令牌消耗，性能超越现有方法
- 动态推断隐含关系，适合复杂问答场景
认知神经科学研究表明，人类通过提示激活以实体为中心的记忆痕迹（印记）来完成复杂的多跳回忆。受此机制启发，我们提出EcphoryRAG，一种以实体为中心的知识图谱RAG框架。在索引阶段，EcphoryRAG仅提取并存储核心实体及其元数据，相比其他结构化RAG系统，令牌消耗降低高达94%。在检索阶段，系统首先从查询中提取提示实体，然后在知识图谱上执行可扩展的多跳关联搜索。关键在于，EcphoryRAG能动态推断实体间的隐含关系以填充上下文，实现深度推理而无需预先枚举所有关系。在2WikiMultiHop、HotpotQA和MuSiQue基准上的大量评估显示，EcphoryRAG达到新最优水平，相较于强基线方法HippoRAG，平均精确匹配（EM）得分从0.392提升至0.474。这些结果验证了实体-提示-多跳检索范式在复杂问答任务中的有效性。
原文摘要 · Abstract (English)
Cognitive neuroscience research indicates that humans leverage cues to activate entity-centered memory traces (engrams) for complex, multi-hop recollection. Inspired by this mechanism, we introduce EcphoryRAG, an entity-centric knowledge graph RAG framework. During indexing, EcphoryRAG extracts and stores only core entities with corresponding metadata, a lightweight approach that reduces token consumption by up to 94\% compared to other structured RAG systems. For retrieval, the system first extracts cue entities from queries, then performs a scalable multi-hop associative search across the knowledge graph. Crucially, EcphoryRAG dynamically infers implicit relations between entities to populate context, enabling deep reasoning without exhaustive pre-enumeration of relationships. Extensive evaluations on the 2WikiMultiHop, HotpotQA, and MuSiQue benchmarks demonstrate that EcphoryRAG sets a new state-of-the-art, improving the average Exact Match (EM) score from 0.392 to 0.474 over strong KG-RAG methods like HippoRAG. These results validate the efficacy of the entity-cue-multi-hop retrieval paradigm for complex question answering.
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