LiteRAG is presented, a graph-based retrieval method that replaces expensive retrieval-time LLM control with query-conditioned algorithmic exploration and reasoning-chain context construction and attains the highest overall quality among the evaluated methods.
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.
EffiRAG is presented, a graph-based RAG system designed to reduce this cost that uses the graph to locate relevant passages and generates answers from the original text, and preserves source information while keeping graph construction and query processing lightweight.
Yu-Zhong Zhang, Hao-Yang Ma, Chao Peng et al.· 0 citations
Retrieval-Augmented Generation (RAG) systems typically apply identical retrieval logic to all user queries, overlooking that different query intents depend on fundamentally different document relationships. We propose QMEG, a query-aware retrieval framework that constructs a multi-relational evidence graph with five ac...
Jia-Run Pan, Yu-Ling Fan, Li Ma et al.· 2026 3rd International Confe...· 0 citations
Mosaic is presented, a training-free framework that formulates GraphRAG retrieval as a per-query control problem that converts query-specific evidence requirements into a bounded policy over seed selection, graph traversal, stopping, and evidence selection while the corpus graph, indexes, scoring functions, grounding p...
Eunkyeong Lee, Kyeong-Jin Oh, Jinwon Kim et al.· 0 citations
Retrieval-Augmented Generation (RAG) systems for multi-hop Question Answering (QA) must balance retrieval quality with computational cost. This cost is incurred during indexing time, through the use of expensive Knowledge Graphs (KGs) or Large Language Models (LLMs) to generate summaries, or during querying, through it...
Gianluca Bonifazi, Christopher Buratti, Michele Marchetti et al.· 0 citations
PoP-RAG is proposed, which features a passage-on-edge (PoE) graph that links the passages with graph edges to resolve the one-to-many entity-passage mapping problem and enables targeted retrieval for each sub-query and ensures transparent response derivation logic, thus enhancing explainability.
Xin-Tong Hu, Qi-Ming Zeng, Yu-Hao Lin et al.· Transactions on Graph Intell...· 0 citations
Results show that geometric structure can support efficient multi-hop evidence discovery without LLM-based graph construction, and introduce Geometric Gain Graph RAG, a document-only framework whose offline graph construction uses no LLM calls or generated tokens.
Ze-Liang Li, Xiao-Fen Xing, K. Guo et al.· 0 citations
