MG$^2$-RAG: Multi-Granularity Graph for Multimodal Retrieval-Augmented Generation

📰 ArXiv cs.AI

MG$^2$-RAG is a multimodal retrieval-augmented generation model that uses a multi-granularity graph to improve cross-modal reasoning

advanced Published 8 Apr 2026

Action Steps

Construct a multi-granularity graph to capture structural dependencies between modalities
Use the graph to perform retrieval-augmented generation, mitigating hallucinations in multimodal large language models
Fine-tune the model on specific tasks to adapt to different cross-modal reasoning requirements
Evaluate the model's performance on benchmarks to assess its effectiveness in multimodal retrieval-augmented generation

Who Needs to Know This

AI engineers and researchers working on multimodal large language models can benefit from this approach to improve the performance of their models, particularly in tasks that require complex cross-modal reasoning

Key Insight

💡 Using a multi-granularity graph can help capture fine-grained visual information and improve the performance of multimodal large language models

Key Takeaways

MG$^2$-RAG is a multimodal retrieval-augmented generation model that uses a multi-granularity graph to improve cross-modal reasoning

Full Article

Title: MG$^2$-RAG: Multi-Granularity Graph for Multimodal Retrieval-Augmented Generation

Abstract:
arXiv:2604.04969v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) mitigates hallucinations in Multimodal Large Language Models (MLLMs), yet existing systems struggle with complex cross-modal reasoning. Flat vector retrieval often ignores structural dependencies, while current graph-based methods rely on costly ``translation-to-text'' pipelines that discard fine-grained visual information. To address these limitations, we propose \textbf{MG$^2$-RAG}, a lightweight \textbf{M}u

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