arXiv AI June 26, 2026 By Xiaochen Wang, Bao Hoang, Han Liu, Ting Wang, Fenglong Ma

MKG-RAG-Bench: Benchmarking Retrieval in Multimodal Knowledge Graph-Augmented Generation

arXiv:2606. 26458v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) over knowledge graphs has emerged as a promising approach for grounding large language models, yet existing benchmarks largely overlook the challenges of retrieval in multimodal knowledge graph RAG (MKG-RAG).

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv AI

Jul 21

A Survey on Knowledge-Oriented Retrieval-Augmented Generation

arXiv:2503. 10677v3 Announce Type: replace-cross Abstract: Retrieval-Augmented Generation (RAG) has gained significant attention in recent years for its potential to enhance natural language understanding and generation by combining large-scale retrieval systems with generative models.

By Mingyue Cheng, Yucong Luo, Jie Ouyang, Qi Liu, Huijie Liu, Li Li, Shuo Yu, Bohou Zhang, Jiawei Cao, Jie Ma, Daoyu Wang, Enhong Chen

rag nlp multimodal benchmarks safety

arXiv AI

Jul 29

Multimodal Hybrid Retrieval-Augmented Generation for Scientific Document Understanding using Open-Source SLMs

arXiv:2607. 24799v1 Announce Type: cross Abstract: Large Language Models tend to hallucinate when answering domain-specific ques tions from scientific documents without prior fine-tuning.

By Alexandru-Andrei Sauc\u{a}, Ana-Luiza Rusnac

llms rag fine-tuning multimodal benchmarks

arXiv AI

Jul 28

Reason Before You Retrieve: Agentic Planning for Multi-modal RAG

arXiv:2607. 22643v1 Announce Type: new Abstract: Multimodal retrieval-augmented generation (mRAG) aims to answer image-text queries with external knowledge, but most existing systems still retrieve directly from raw multimodal input over a flat evidence space.

By Tianyu Yang, Shir Simon, Zhenzhen Li, Minhao Cheng, Xiangliang Zhang

rag agents fine-tuning multimodal

arXiv AI

Jul 14

MG$^2$-RAG: Multi-Granularity Graph for Multimodal Retrieval-Augmented Generation

arXiv:2604. 04969v2 Announce Type: replace-cross Abstract: Retrieval-Augmented Generation (RAG) mitigates hallucinations in Multimodal Large Language Models (MLLMs), yet existing systems struggle with complex cross-modal reasoning.

By Sijun Dai, Qiang Huang, Xiaoxing You, Jun Yu

llms rag multimodal benchmarks safety

arXiv AI

1d ago

GraphLoom: Reliability-Calibrated Graph Evidence Routing for Multimodal KG-RAG

arXiv:2608. 15056v1 Announce Type: new Abstract: Multimodal retrieval-augmented generation (RAG) systems often rely on long unstructured contexts or aggressively expanded evidence graphs, which can introduce noisy evidence, weaken multi-hop reasoning, and increase unsupported generation.

By Zafar Ali, Asad Khan, Aalia Malik, Pavlos Kefalas

llms rag multimodal

arXiv AI

Jun 15

UniversalRAG: Retrieval-Augmented Generation over Corpora of Diverse Modalities and Granularities

arXiv:2504. 20734v5 Announce Type: replace-cross Abstract: Retrieval-Augmented Generation (RAG) has shown substantial promise in improving factual accuracy by grounding model responses with external knowledge relevant to queries.

By Woongyeong Yeo, Kangsan Kim, Soyeong Jeong, Jinheon Baek, Sung Ju Hwang

rag fine-tuning benchmarks

Related stories

A Survey on Knowledge-Oriented Retrieval-Augmented Generation

Multimodal Hybrid Retrieval-Augmented Generation for Scientific Document Understanding using Open-Source SLMs

Reason Before You Retrieve: Agentic Planning for Multi-modal RAG

MG$^2$-RAG: Multi-Granularity Graph for Multimodal Retrieval-Augmented Generation

GraphLoom: Reliability-Calibrated Graph Evidence Routing for Multimodal KG-RAG

UniversalRAG: Retrieval-Augmented Generation over Corpora of Diverse Modalities and Granularities