Hugging Face Trending Papers July 22, 2026

VizRAG: Enhancing Retrieval-Augmented Generation with Hypergraph Visualization

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Hypergraph-based RAG systems surpass traditional graph-based approaches by organizing complex n-ary atomic facts among entities, rather than relying solely on binary relationships. Despite the advancements in multimodal large language models (MLLMs) with enhanced visual capabilities, current hypergraph-based RAG frameworks predominantly restrict knowledge retrieval and reconstruction to a unimodal, text-centric paradigm.

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arXiv AI

Aug 18

Hypergraph-based Multimodal Retrieval-Augmented Generation with Incremental Refinement

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

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arXiv AI

Jun 11

MLaGA: Multimodal Large Language and Graph Assistant

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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

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arXiv AI

Jul 29

HVM-GraphRAG: Holistic-View Multimodal Graph Retrieval-Augmented Generation on Complex Document

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

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arXiv AI

Aug 18

TAHB: A Comprehensive Benchmark for Text-Attributed Hypergraph Learning

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By David Yoon Suk Kang, JungHyun Kim, Juhyun Jeon, Sang-Wook Kim

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arXiv Computation and Language

Sep 18

Less Is More: Graph-free Multimodal RAG via Multi-signal Late Fusion

The paper introduces TrioRAG, a graph-free multimodal retrieval-augmented generation framework that combines evidence from the question, an anchor image, and a VLM-enhanced query via late fusion. It also presents AutoQA, a benchmark featuring noisy web-sourced images that require reasoning across manuals. TrioRAG outperforms graph-based systems on three benchmarks while cutting costs and speeding up inference by 1.6–2.3×.

By Tithi Rakshit, Hongkuan Zhou, Lavdim Halilaj, Yuqicheng Zhu

rag nlp multimodal benchmarks

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Hypergraph-based Multimodal Retrieval-Augmented Generation with Incremental Refinement

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