arXiv Machine Learning

Multimodal Graph Negative Learning

arXiv:2606. 12863v2 Announce Type: replace Abstract: Multimodal attributed graphs (MAGs) integrate graph topology with heterogeneous modality attributes, such as text and images, thereby enabling richer modeling of complex relational systems.

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 Machine Learning

Aug 10

TMTE: Effective Multimodal Graph Learning with Task-aware Modality and Topology Co-evolution

arXiv:2603. 27723v2 Announce Type: replace Abstract: Multimodal-attributed graphs (MAGs) are a fundamental data structure for multimodal graph learning (MGL), enabling both graph-centric and modality-centric tasks.

By Yinlin Zhu, Xunkai Li, Di Wu, Wang Luo, Miao Hu, Guocong Quan

rag multimodal benchmarks safety

arXiv Machine Learning

Jun 19

Towards Modality-imbalanced Federated Graph Learning: A Data Synthesis-based Approach

arXiv:2606. 20382v1 Announce Type: new Abstract: MultiModal Federated Graph Learning (MM-FGL) offers a natural collaborative training paradigm, but its practical deployment is challenged by two granularities of modality imbalance.

By Zhengyu Wu, Hongchao Qin, Xunkai Li, Zekai Chen, Rong-Hua Li, Guoren Wang

multimodal safety

arXiv AI

Jun 11

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.

By Dongzhe Fan, Yi Fang, Jiajin Liu, Djellel Difallah, Qiaoyu Tan

llms fine-tuning multimodal

arXiv AI

Jun 2

PolarMem: A Training-Free Polarized Latent Graph Memory for Verifiable Vision-Language Models

arXiv:2602. 00415v2 Announce Type: replace Abstract: Memory is not merely a storage mechanism for intelligent systems, but a structure for organizing evidence and constraining belief.

By Zhisheng Chen, Tingyu Wu, Zijie Zhou, Zhengwei Xie, Jinhan Li, Ziyan Weng, Liang Lin, Jingwei Song, Zikai Xiao, Yingwei Zhang

llms multimodal benchmarks

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 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 2

G2LoRA: Gradient Orthogonal Low-Rank Adaptation Framework for Graph Continual Learning on Text-Attributed Graphs

arXiv:2606. 01873v1 Announce Type: new Abstract: LLM-as-Aligner has emerged as a prevalent pre-training paradigm for Text-Attributed Graphs(TAGS), aligning graph and text modalities into a shared embedding space via CLIP-style contrastive learning.

By Yuhan Wang, Yibo Ding, Yutong Ye, Mufan Zhao, Wenbo Zhang, Ruijie Wang, Jianxin Li

llms rag fine-tuning 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

arXiv Machine Learning

Aug 3

MMFGU: Multimodal Federated Graph Unlearning

arXiv:2607. 28708v1 Announce Type: new Abstract: Multimodal federated graph learning enables clients to collaboratively train graph models over structural, textual, and visual signals without sharing private local data.

By Haodong Lu, Zekai Chen, Weiwei Ji, Shihao Li, Xunkai Li, Xun Wu, Yinlin Zhu, Rong-Hua Li

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 Machine Learning

Aug 4

Towards Effective Federated Multimodal Graph Learning via Navigating Multifaceted Heterogeneity

arXiv:2608. 00623v1 Announce Type: new Abstract: Multimodal-attributed graphs (MAGs), where nodes carry heterogeneous semantic content across multiple modalities while edges encode relational dependencies, have been widely adopted across diverse domains.

By Yinlin Zhu, Di Wu, Yi Zhang, Xunkai Li, Wang Luo, Wei-Jin Huang, Miao Hu, Guocong Quan

fine-tuning multimodal benchmarks

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