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

• URL: http://arxiv.org/abs/2609.10239v1
• Date: Wed, 09 Sep 2026 14:32:07 GMT
• Title: LiteRAG: Cost-Efficient Graph-Based Retrieval-Augmented Generation
• Abstract summary: LiteRAG is a graph-based retrieval method that replaces expensive retrieval-time LLM control with query-conditioned algorithmic exploration and reasoning-chain context construction.<n>OnComp, a benchmark for multi-hop retrieval over distributed-systems papers, LiteRAG attains the highest overall quality.<n> LiteRAG's query-adaptive thresholding and community-aware hub penalization are the main drivers of its token-efficiency gains.
• Score: 1.0674604700001966
• License: http://creativecommons.org/licenses/by/4.0/
• 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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