arXiv AI

ISO-RAG: Isoperimetric Noise Control for Retrieval-Augmented Generation

arXiv AI

Jun 30

Query-Aware Spreading Activation for Multi-Hop Retrieval over Knowledge Graphs

arXiv:2606. 30133v1 Announce Type: cross Abstract: Retrieval-augmented generation built on knowledge graphs (Graph RAG) outperforms flat passage retrieval on multi-hop question answering by leveraging graph structure.

By Illia Makarov, Mykola Glybovets

rag diffusion nlp benchmarks

arXiv Computation and Language

Sep 10

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

arXiv:2609.10239v1 Announce Type: cross Abstract: Graph-based retrieval can improve multi-hop question answering, but existing approaches often incur high query-time costs and produce diffuse, oversi...

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

llms rag nlp benchmarks

arXiv Computation and Language

Oct 22

Graph Topology Meets Energy-Driven Pathfinding: Preserving Contextual Consistency in Retrieval-Augmented Generation

ThreadRAG is a retrieval‑augmented generation framework that preserves contextual consistency by structuring retrieved documents into Semantic Nodes and building a Semantic Topology Graph (STG). It uses Energy‑Driven Pathfinding (EDP) to identify query‑relevant subgraphs and linearize them into coherent textual evidence paths. Experiments on HotpotQA, 2WikiMultihop, and another benchmark show ThreadRAG outperforms strong baselines, achieving a 14.4% EM gain on HotpotQA and a 5.8% EM lead on 2WikiMultihop while cutting token cost to 23.3% of the strongest baseline.

By Yufeng Li, Zaijing Li, Fuchen Zhang, Zongzong Wu, Ming Zhao, Fengxiao Tang

llms rag diffusion nlp benchmarks

arXiv AI

Jun 30

SemFlowRAG: Directed Semantic Flow from Abstraction to Evidence for Complex Reasoning

arXiv:2606. 28447v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) enhanced by Knowledge Graphs has shown promise in complex multi-hop reasoning tasks.

By Houyuan Qin, Rong Wu, Qinyuan Qin, Botian Shi, Jingjing Qu, Yang Sun, Pinlong Cai

rag

arXiv Computation and Language

Sep 4

R$^{2}$Adapter: A Routing and Rewriting Adapter for Efficient Hybrid RAG

R$^{2}$Adapter is a lightweight plug‑in that dynamically routes user queries between vanilla and graph‑based Retrieval‑Augmented Generation (RAG) systems. By sending only those queries that truly benefit from graph reasoning, it cuts graph‑retrieval overhead by up to 59% while keeping answer accuracy comparable. The adapter also rewrites uncertain graph‑routed queries to better expose multi‑hop reasoning needs, improving retrieval quality without extra supervision.

By Yucan Guo, Miao Su, Saiping Guan, Long Bai, Zhongni Hou, Zixuan Li, Xiaolong Jin, Jiafeng Guo, Xueqi Cheng

llms rag benchmarks

arXiv AI

Jun 6

Reducing Hallucinations in Complex Question Answering using Simple Graph-based Retrieval-Augmented Generation (long version)

arXiv:2606. 05901v1 Announce Type: cross Abstract: Large language models (LLMs) have fundamentally transformed the landscape of Natural Language Processing.

By Christopher J. Wedge, Joshua Stutter, Danny Dixon, Jacek Ca{\l}a

llms rag agents nlp fine-tuning benchmarks safety

arXiv Computation and Language

Sep 15

HyperProve: Answer-Guided Hypergraph Expansion for Multi-Hop Question Answering

arXiv:2609.13768v1 Announce Type: new Abstract: Multi-hop question answering often fails when retrieval treats evidence as isolated matches to the original question, since the facts needed to answer...

By An Nguyen Phu, Dung Nguyen Quang, Luu Hieu An, Linh Ngo Van, Trung Le, Thien Huu Nguyen

rag nlp benchmarks safety

arXiv AI

Jun 2

MemGraphRAG: Memory-based Multi-Agent System for Graph Retrieval-Augmented Generation

arXiv:2606. 00610v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) has become an essential method for mitigating hallucinations in Large Language Models (LLMs) by leveraging external knowledge.

By Chuanjie Wu, Zhishang Xiang, Yunbo Tang, Zerui Chen, Qinggang Zhang, Jinsong Su

llms rag agents benchmarks safety

arXiv Computation and Language

Aug 25

GTA-RAG: Graph-Trajectory-Augmented Reinforcement Learning for Multi-Turn Retrieval-Augmented Reasoning

arXiv:2608.22479v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) enables LLMs to access external knowledge for answering knowledge-intensive questions. For complex multi-hop quest...

By Jun Chen, Yongchao Liu, Pengyu Qiu, Jiajun Zheng, Juelu Zhang, Yujie Zeng, Qin Zhang, Ziyue Qiao, Xiao Luo

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arXiv Machine Learning

Sep 30

UltRAG: a Universal Simple Scalable Recipe for Knowledge Graph RAG

arXiv:2603.28773v2 Announce Type: replace-cross Abstract: Large language models (LLMs) frequently generate confident yet factually incorrect content when used for language generation (a phenomenon of...

By Dobrik Georgiev, Kheeran K. Naidu, Alberto Cattaneo, Federico Monti, Carlo Luschi, Daniel Justus

llms rag nlp benchmarks safety

arXiv Computation and Language

Sep 30

Corpus-Guided Dual-Path Propagation for Graph Retrieval-Augmented Generation

arXiv:2609.37661v1 Announce Type: new Abstract: Graph-based retrieval-augmented generation supports multi-hop retrieval by organizing corpus information into graphs. However, existing relation-free g...

By Baoxian Liu, Tong Wei

rag benchmarks

arXiv AI

Jul 17

HG-RAG: Hierarchy-Guided Retrieval-Augmented Generation for Structured Knowledge Graphs

arXiv:2607. 14095v1 Announce Type: new Abstract: Retrieval Augmented Generation (RAG) has proven to be a widely successful process at improving the quality of outputs from a Large Language Model (LLM) for wider context.

By Pranav Yadav

llms rag nlp safety

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