FastInsight: Fast and Insightful Retrieval via Fusion Operators for Graph RAG

• URL: http://arxiv.org/abs/2601.18579v1
• Date: Mon, 26 Jan 2026 15:23:41 GMT
• Title: FastInsight: Fast and Insightful Retrieval via Fusion Operators for Graph RAG
• Authors: Seonho An, Chaejeong Hyun, Min-Soo Kim,
• Abstract summary: Graph-based Reranker (GRanker) functions as a graph model-based search, and Semantic-Topological eXpansion (S) operates as a vector-graph search.<n>FastInsight significantly improves both retrieval accuracy and generation quality compared to state-of-the-art baselines.
• Score: 4.927427162899616
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Existing Graph RAG methods aiming for insightful retrieval on corpus graphs typically rely on time-intensive processes that interleave Large Language Model (LLM) reasoning. To enable time-efficient insightful retrieval, we propose FastInsight. We first introduce a graph retrieval taxonomy that categorizes existing methods into three fundamental operations: vector search, graph search, and model-based search. Through this taxonomy, we identify two critical limitations in current approaches: the topology-blindness of model-based search and the semantics-blindness of graph search. FastInsight overcomes these limitations by interleaving two novel fusion operators: the Graph-based Reranker (GRanker), which functions as a graph model-based search, and Semantic-Topological eXpansion (STeX), which operates as a vector-graph search. Extensive experiments on broad retrieval and generation datasets demonstrate that FastInsight significantly improves both retrieval accuracy and generation quality compared to state-of-the-art baselines, achieving a substantial Pareto improvement in the trade-off between effectiveness and efficiency.

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