# A Multi-Granularity Retrieval Framework for Visually-Rich Documents

arXiv: https://arxiv.org/abs/2505.01457 · HTML: https://mlsanity.com/paper/2505.01457/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Placed in Document understanding and multimodal RAG, one of 16 directions in Multimodal LLMs / vision-language models.
- Builds on 2 of its 2 references that are in the map.
- First posted on arXiv 1 May 2025.

## Placed in

- Multimodal LLMs / vision-language models / [Document understanding and multimodal RAG](https://mlsanity.com/mllm/document-understanding-and-multimodal-rag/)
- Retrieval-augmented generation / [Multimodal RAG](https://mlsanity.com/rag/multimodal-rag/)

## Builds on (most cited first, 2 of 2)

- [GME: Improving Universal Multimodal Retrieval by Multimodal LLMs](https://mlsanity.com/paper/2412.16855/) (2024)
- [The 1st EReL@MIR Workshop on Efficient Representation Learning for Multimodal Information Retrieval](https://mlsanity.com/paper/2504.14788/) (2025)

## Built on by (most cited first, 6 of 6)

- [DocPruner: A Storage-Efficient Framework for Multi-Vector Visual Document Retrieval via Adaptive Patch-Level Embedding Pruning](https://mlsanity.com/paper/2509.23883/) (2025)
- [Scaling Beyond Context: A Survey of Multimodal Retrieval-Augmented Generation for Document Understanding](https://mlsanity.com/paper/2510.15253/) (2025)
- [LLM-Centric RAG with Multi-Granular Indexing and Confidence Constraints](https://mlsanity.com/paper/2510.27054/) (2025)
- [From Scenes to Elements: Multi-Granularity Evidence Retrieval for Verifiable Multimodal RAG](https://mlsanity.com/paper/2605.15019/) (2026)
- [FLOWREADER: Min-Cost Flow Optimization for Multi-Modal Long Document Q&A](https://mlsanity.com/paper/2606.07235/) (2026)
- [Evo-Retriever: LLM-Guided Curriculum Evolution with Viewpoint-Pathway Collaboration for Multimodal Document Retrieval](https://mlsanity.com/paper/2603.16455/) (2026)

## Abstract (arXiv)

Retrieval-augmented generation (RAG) systems have predominantly focused on text-based retrieval, limiting their effectiveness in handling visually-rich documents that encompass text, images, tables, and charts. To bridge this gap, we propose a unified multi-granularity multimodal retrieval framework tailored for two benchmark tasks: MMDocIR and M2KR. Our approach integrates hierarchical encoding strategies, modality-aware retrieval mechanisms, and vision-language model (VLM)-based candidate filtering to effectively capture and utilize the complex interdependencies between textual and visual modalities. By leveraging off-the-shelf vision-language models and implementing a training-free hybrid retrieval strategy, our framework demonstrates robust performance without the need for task-specific fine-tuning. Experimental evaluations reveal that incorporating layout-aware search and VLM-based candidate verification significantly enhances retrieval accuracy, achieving a top performance score of 65.56. This work underscores the potential of scalable and reproducible solutions in advancing multimodal document retrieval systems.