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arXiv · 2609.10239

LiteRAG: Cost-Efficient Graph-Based Retrieval-Augmented Generation

Daniel Alejandro Coll Tejeda·Pedro García López·Daniel Barcelona-Pons
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Abstract

Graph-based retrieval can improve multi-hop question answering, but existing approaches often incur high query-time costs and produce diffuse, oversized contexts that reduce generation efficiency. We present LiteRAG, a graph-based retrieval method that replaces expensive retrieval-time LLM control with query-conditioned algorithmic exploration and reasoning-chain context construction. On DistComp, a benchmark for multi-hop retrieval over distributed-systems papers, LiteRAG attains the highest overall quality among the evaluated methods (0.798) while reducing per-query latency by over 100$\times$ and cost by over 99% relative to GraphRAG Global and DRIFT. On UltraDomain, it matches LinearRAG on overall quality while using about 14$\times$ fewer tokens. An ablation study indicates that LiteRAG's query-adaptive thresholding and community-aware hub penalization are the main drivers of its token-efficiency gains.

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Daniel Alejandro Coll Tejeda, Pedro García López, Daniel Barcelona-Pons. 2026-09-09. LiteRAG: Cost-Efficient Graph-Based Retrieval-Augmented Generation. https://arxiv.org/abs/2609.10239

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