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When Is Graph Structure Worth Its Cost? The Case for Structure Pricing in Retrieval-Augmented Generation

arXiv · AI, language, vision and robotics · article · Sep 16, 2026 · UTC

Graph-based retrieval-augmented generation (RAG) can help answer questions that require information from many documents. However, building a graph often requires many language-model calls during ingestion. It is therefore important to ask whether its quality gains justify the additional cost. We present EffiRAG, a graph-based RAG system designed to reduce this cost. It uses the graph to locate relevant passages and generates answers from the original text. This design preserves source information while keeping graph construction and query processing lightweight. We evaluate EffiRAG on UltraDom

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