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

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.

Authors 3

Daniel Alejandro Coll Tejeda, Pedro Garc\'ia L\'opez, Daniel Barcelona-Pons

Specification

Official page: arxiv.org/abs/2609.10239
Source:arXiv (Atom API + RSS)T1observed 2 h agohigh
Arxiv announce type: cross
Source:arXiv (Atom API + RSS)T1observed 2 h agohigh
arXiv id: 2609.10239
Source:arXiv (Atom API + RSS)T1observed 2 h agohigh
Categories: cs.IR, cs.AI, cs.CL
Source:arXiv (Atom API + RSS)T1observed 2 h agohigh
PDF: arxiv.org/pdf/2609.10239
Source:arXiv (Atom API + RSS)T1observed 2 h agohigh
Primary category: cs.IR
Source:arXiv (Atom API + RSS)T1observed 2 h agohigh
Published: 11 Sept 2026
Source:arXiv (Atom API + RSS)T1observed 2 h agohigh

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9

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T19

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2 h ago

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Authors: Daniel Alejandro Coll Tejeda, Pedro Garc\'ia L\'opez, Daniel Barcelona-Pons

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Value	Valid from → to	Status	Source	Confidence	Extractor
cs.IR, cs.AI, cs.CL	11 Sept 2026 21:03 UTC → current	current	arXiv (Atom API + RSS)T1	high	deterministic

Claims are temporal and append-only: a new observation closes the previous claim (valid_to) instead of overwriting it. Conflicting claims from different sources are kept side by side and flagged — never averaged. Methodology →

11 Sept 2026
New paperPaperLiteRAG: Cost-Efficient Graph-Based Retrieval-Augmented Generation
New paper: LiteRAG: Cost-Efficient Graph-Based Retrieval-Augmented Generation
arxiv
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Source documents
Source	Document	Type	Tier	Last observed	Snapshots
arXiv (Atom API + RSS)	rss.arxiv.org/rss/cs.AI	feed	T1· Official	2 h ago	1

Tier 1 = official/primary, 2 = quality secondary, 3 = community, 4 = unverified. Every snapshot is archived; see all sources and the methodology.