arXiv AI October 9, 2026 By Maya Arseven, Anette Frank, Beni Egressy, Johann Higl, Moritz Plenz

GLM-RAG: Graph Language Models for Graph-Based Retrieval-Augmented Generation

GLM-RAG introduces a graph language model (GLM) based retriever for retrieval‑augmented generation over knowledge graphs. The study compares GLM‑based, graph neural network (GNN)‑based, and vector‑search retrievers across single‑ and multi‑hop settings, emphasizing transferability to unseen domains. Results show that finetuned GLM retrievers achieve state‑of‑the‑art performance on two multi‑hop benchmarks and generalize better out of domain, while GNN retrievers excel at graph coverage and vector‑search performs best on single‑hop tasks.

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arXiv Computation and Language

Aug 27

SelfGraphRAG: Bridging the Supervision Gap in Graph-Based RAG with Synthetic QA Generation

SelfGraphRAG is a framework that generates synthetic question‑answer pairs directly from the structure of a knowledge graph to train a query‑conditioned graph retriever. By capturing multi‑hop paths and local neighborhoods, the generated questions provide relational supervision without requiring manually labeled data. Experiments on multi‑hop question answering and classification tasks show that SelfGraphRAG improves retrieval precision and downstream reasoning performance compared to embedding‑based baselines.

By Ben Lagnese, Manas Gaur

llms rag nlp benchmarks

arXiv AI

Jun 3

Generalizing Graph Foundation Models via Hyperbolic Retrieval-Augmented Generation

arXiv:2606. 03307v1 Announce Type: cross Abstract: Graph foundation models (GFMs) emerged as a dominant paradigm in graph representation learning by leveraging large-scale pre-training for cross-domain inference.

By Yifan Jin, Qirui Ji, Bin Qin, Jiangmeng Li, Lixiang Liu, Fuchun Sun, Changwen Zheng

rag benchmarks

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 Machine Learning

Oct 7

SeedER: Seed-Expand-Retrieve for Efficient Knowledge Graph Retrieval

arXiv:2605.23753v2 Announce Type: replace Abstract: Knowledge graphs (KGs) offer a rich representation for relational knowledge, but their irregular structure makes retrieval challenging: ego-graph e...

By Hamed Shirzad, Frederik Wenkel, Dominique Beaini, Danica J. Sutherland, Emmanuel Noutahi

llms rag agents reinforcement-learning fine-tuning

arXiv AI

Sep 2

KGFR: A Foundation Retriever for Generalized Knowledge Graph Question Answering

KGFR introduces a Knowledge Graph Foundation Retriever that collaborates with large language models to enhance knowledge‑intensive question answering. By encoding relations with LLM‑generated descriptions and initializing entities from question roles, KGFR enables zero‑shot generalization to unseen knowledge graphs. Its Asymmetric Progressive Propagation technique efficiently handles large graphs, while a controllable reasoning loop allows the LLM to request candidate answers, supporting facts, and reasoning paths.

By Yuanning Cui, Zequn Sun, Wei Hu, Zhangjie Fu

llms nlp

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