EcphoryRAG: Re-Imagining Knowledge-Graph RAG via Human Associative Memory

• URL: http://arxiv.org/abs/2510.08958v1
• Date: Fri, 10 Oct 2025 03:07:27 GMT
• Title: EcphoryRAG: Re-Imagining Knowledge-Graph RAG via Human Associative Memory
• Authors: Zirui Liao,
• Abstract summary: We introduce EcphoryRAG, an entity-centric knowledge graph RAG framework.<n>During indexing, EcphoryRAG extracts and stores only core entities with corresponding metadata.<n>For retrieval, the system first extracts cue entities from queries, then performs a scalable multi-hop associative search.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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