arXiv AI June 11, 2026 By Dongzhe Fan, Yi Fang, Jiajin Liu, Djellel Difallah, Qiaoyu Tan

MLaGA: Multimodal Large Language and Graph Assistant

arXiv:2506. 02568v2 Announce Type: replace Abstract: Large Language Models (LLMs) have demonstrated substantial efficacy in advancing graph-structured data analysis.

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

arXiv Machine Learning

Jul 22

One Model, Many Graphs: Learning over Attributed Graphs across Heterogeneous Modalities with Vision-Language Models

arXiv:2607. 19128v1 Announce Type: new Abstract: Vision-language models (VLMs) provide a unified representation space for textual and visual information, yet their potential as general-purpose backbones for graph-structured data remains largely unexplored.

By Jiayi Yang, Yifang Chen, Yuanfu Sun, Jiajin Liu, Qiaoyu Tan

llms rag multimodal benchmarks

arXiv AI

Jul 21

GRIP: In-Parameter Graph Reasoning through Fine-Tuning Large Language Models

arXiv:2511. 07457v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have demonstrated remarkable capabilities in modeling sequential textual data and generalizing across diverse tasks.

By Jiarui Feng, Donghong Cai, Yixin Chen, Muhan Zhang

llms fine-tuning safety

arXiv AI

Jul 29

CHARM: A Multimodal Graph Foundation Model with Hierarchical Context Modeling for Zero-Shot Transfer

arXiv:2607. 26023v1 Announce Type: new Abstract: Graph foundation models (GFMs) have emerged as a promising paradigm for transferring knowledge across graph domains and tasks.

By Ankang Yang, Jitao Zhao, Di Jin, Yuxiao Huang, Dongxiao He

llms fine-tuning multimodal

arXiv Machine Learning

Jun 11

GraspLLM: Towards Zero-Shot Generalization on Text-Attributed Graphs with LLMs

arXiv:2606. 11898v1 Announce Type: cross Abstract: Research on Text-Attributed Graphs (TAGs) has gained significant attention recently due to its broad applications across various real-world data scenarios, such as citation networks, e-commerce platforms, social media, and web pages.

By Hengyi Feng, Zeang Sheng, Meiyi Qiang, Meiyi Qiang, Wentao Zhang

llms rag benchmarks safety

arXiv AI

Jul 21

MMGraphRAG: Bridging Vision and Language with Interpretable Multimodal Knowledge Graphs

arXiv:2507. 20804v3 Announce Type: replace Abstract: Large Language Models (LLMs) suffer from hallucinations due to their static parametric knowledge.

By Xueyao Wan, Hang Yu

llms rag multimodal benchmarks safety

arXiv AI

Aug 7

ViSR-KGC: Visual Subgraph Reasoning with Vision-Language Models for Multimodal Knowledge Graph Completion

arXiv:2608. 05833v1 Announce Type: new Abstract: Knowledge graph completion (KGC) aims to infer missing entities or relations from incomplete graph structures, and has evolved into multimodal knowledge graph completion (MMKGC), where entities are associated with multiple modalities such as text and images.

By Jiafan Li, Mengxue Yang, Jiaqi Zhu, Liang Chang, Ying Li, Hongan Wang

llms rag multimodal

Related stories

One Model, Many Graphs: Learning over Attributed Graphs across Heterogeneous Modalities with Vision-Language Models

GRIP: In-Parameter Graph Reasoning through Fine-Tuning Large Language Models

CHARM: A Multimodal Graph Foundation Model with Hierarchical Context Modeling for Zero-Shot Transfer

GraspLLM: Towards Zero-Shot Generalization on Text-Attributed Graphs with LLMs

MMGraphRAG: Bridging Vision and Language with Interpretable Multimodal Knowledge Graphs

ViSR-KGC: Visual Subgraph Reasoning with Vision-Language Models for Multimodal Knowledge Graph Completion