EchoRAG: Evolving Communities and Harmonizing Intent for Retrieval-Augmented Generation

Graph-based retrieval-augmented generation (RAG) organizes external corpora into reusable knowledge communities, but typically overlooks the complex query process that determines knowledge utility. This creates two coupled gaps: an *intent-blind knowledge utilization gap*, where underspecified queries yield evidence misaligned with latent needs, and a *query-blind knowledge organization gap*, where structurally coherent communities ignore downstream query distributions. To bridge them, we propose **E**volving **C**ommunities and **H**arm**o**nizing Intent for **R**etrieval-**A**ugmented **G**eneration (EchoRAG), a self-evolving framework that approximates query-specific knowledge organization without per-query reconstruction. EchoRAG begins with a structural initialization to establish a baseline graph partition and then introduces query-guided collaborative community evolution: accumulated query feedback determines where and when to update the knowledge base, while structural-semantic signals guide community evolution. EchoRAG further incorporates an intent-harmonized knowledge utilization mechanism: it infers latent intent to retrieve and transform retrieved knowledge into grounded, intent-aligned evidence anchored to source spans. Utilization traces provide delayed feedback for future evolution, closing the loop between organization and utilization. Extensive experiments on three benchmark datasets demonstrate that EchoRAG significantly outperforms existing state-of-the-art baseline approaches. Our code is available at <https://anonymous.4open.science/r/EchoRAG>.