LiteRAG: Cost-Efficient Graph-Based Retrieval-Augmented Generation
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Computer Science > Information Retrieval
arXiv:2609.10239 (cs)
[Submitted on 9 Sep 2026]
Title:LiteRAG: Cost-Efficient Graph-Based Retrieval-Augmented Generation
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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.
| Comments: | 16 pages, 2 figures |
| Subjects: | Information Retrieval (cs.IR); Artificial Intelligence (cs.AI); Computation and Language (cs.CL) |
| Cite as: | arXiv:2609.10239 [cs.IR] |
| (or arXiv:2609.10239v1 [cs.IR] for this version) | |
| https://doi.org/10.48550/arXiv.2609.10239
arXiv-issued DOI via DataCite (pending registration)
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Submission history
From: Daniel Barcelona-Pons [view email][v1] Wed, 9 Sep 2026 14:32:07 UTC (310 KB)
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View a PDF of the paper titled LiteRAG: Cost-Efficient Graph-Based Retrieval-Augmented Generation, by Daniel Alejandro Coll Tejeda and Pedro Garc\'ia L\'opez and Daniel Barcelona-Pons
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