[2603.24925] GraphER: An Efficient Graph-Based Enrichment and Reranking Method for Retrieval-Augmented Generation

Computer Science > Machine Learning arXiv:2603.24925 (cs) [Submitted on 26 Mar 2026] Title:GraphER: An Efficient Graph-Based Enrichment and Reranking Method for Retrieval-Augmented Generation Authors:Ruizhong Miao, Yuying Wang, Rongguang Wang, Chenyang Li, Tao Sheng, Sujith Ravi, Dan Roth View a PDF of the paper titled GraphER: An Efficient Graph-Based Enrichment and Reranking Method for Retrieval-Augmented Generation, by Ruizhong Miao and 6 other authors View PDF HTML (experimental) Abstract:Semantic search in retrieval-augmented generation (RAG) systems is often insufficient for complex information needs, particularly when relevant evidence is scattered across multiple sources. Prior approaches to this problem include agentic retrieval strategies, which expand the semantic search space by generating additional queries. However, these methods do not fully leverage the organizational structure of the data and instead rely on iterative exploration, which can lead to inefficient retrieval. Another class of approaches employs knowledge graphs to model non-semantic relationships through graph edges. Although effective in capturing richer proximities, such methods incur significant maintenance costs and are often incompatible with the vector stores used in most production systems. To address these limitations, we propose GraphER, a graph-based enrichment and reranking method that captures multiple forms of proximity beyond semantic similarity. GraphER independently enriches dat...