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arXiv AI By Yuzhong Zhang, Haoyang Ma, Chao Peng, Lionel Briand, Boxi Yu, Jialun Cao

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

Read the original on arXiv AI →

The paper introduces EffiRAG, a graph-based retrieval‑augmented generation system that reduces the cost of building and querying a graph by using it only to locate relevant passages and generating answers from the original text. On the UltraDomain benchmark, EffiRAG outperforms LightRAG‑hybrid in 93 of 120 questions while cutting total system cost by 57 % (from USD 0.952 to USD 0.408). The study shows that graph‑based RAG can be both more accurate and cheaper, especially as the corpus grows, and recommends evaluating such systems on both answer quality and cost.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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