VizRAG: Enhancing Retrieval-Augmented Generation with Hypergraph Visualization

2026-07-22

AI Summary

Existing hypergraph-based RAG systems are confined to textual modalities and thus fail to leverage the visual perception capabilities of multimodal large language models. This work proposes the first RAG framework that supports visual awareness of hypergraph structures by introducing a hypergraph visualization mechanism, which encodes complex n-ary relations as images and seamlessly integrates them into both retrieval and generation processes, enabling structure-aware multimodal reasoning. By synergistically combining hypergraph modeling, multimodal large language models, and visualization techniques, the proposed approach substantially outperforms strong baselines across multiple benchmarks, demonstrating the effectiveness and innovative potential of hypergraph visualization in enhancing RAG performance.

Abstract

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. This limitation prevents them from fully leveraging the powerful visual perception capabilities of modern MLLMs. To address this gap, we systematically explore the integration of hypergraph awareness in RAG systems through visual cues. By incorporating visual representations of hypergraphs into the RAG pipeline, we introduce VizRAG, the first RAG system to support visual hypergraph structure awareness. Experimental results demonstrate that VizRAG significantly outperforms strong baselines, validating the promising potential of hypergraph visualization as a novel approach for RAG systems.

Problem

• hypergraph
• retrieval-augmented generation
• multimodal large language models
• visual perception
• knowledge retrieval

Innovation

• Hypergraph Visualization
• Retrieval-Augmented Generation
• Multimodal Large Language Models
• Visual Structure Awareness
• n-ary Relations