arXiv:2608. 16628v1 Announce Type: new Abstract: Modern Multimodal Retrieval-Augmented Generation (M-RAG) systems are fundamentally limited by the binary connectivity paradigm of traditional simple graphs, which fails to capture the intricate, high-order correlations among heterogeneous entities, such as the N-ary relationships between a visual chart, its scattered textual descriptions, and underlying numerical data.
By Shenao Chen, Yidan Xu, Xiangmin Han, Rundong Xue, Duanpo Wu, Yuhan Gao, Chenggang Yan, Yue Gao
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
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
arXiv:2607. 24861v1 Announce Type: cross Abstract: Question answering (QA) over complex documents requires models to retrieve and integrate evidence distributed across distant document regions and modalities.
By Xin He, Yili Wang, Wenqi Fan, Qing Li, Qinggang Zhang, Yi Chang, Xin Wang
arXiv:2608. 15055v1 Announce Type: new Abstract: Hypergraphs effectively model higher-order groupwise relationships beyond pairwise interactions, while pretrained language models (PLMs) and large language models (LLMs) provide rich semantic understanding from textual attributes.
By David Yoon Suk Kang, JungHyun Kim, Juhyun Jeon, Sang-Wook Kim
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