arXiv AI June 9, 2026 By Haowen Xiang, Yibo Yan, Jiahao Huo, Yu Huang, Yi Cao, Mingdong Ou, Xuming Hu

MM-Matryoshka: Towards Budget-Elastic Visual Document Retrieval via a 2D Multimodal Matryoshka Training Framework

arXiv:2606. 07654v1 Announce Type: cross Abstract: Multi-vector visual document retrievers achieve strong fine-grained matching by representing each page with multiple vectors from deep Vision-Language Models (VLMs), but this design makes deployment expensive in both storage and computational overhead.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv AI

Sep 1

Spatial Matryoshka Training for Multi-Granularity Visual Document Retrieval

arXiv:2608.29951v1 Announce Type: new Abstract: Multi-modal late-interaction retrievers achieve strong retrieval on visually rich documents by representing each page as per patch embeddings and match...

By Trishan Singha Roy, Arkadeep Acharya, Vishwajeet Kumar, Jaydeep Sen, Sachindra Joshi

rag multimodal

arXiv Computer Vision

Sep 21

MAGIC: Marginal-Guided Compression with Optimal Transport for Efficient Visual Document Retrieval

arXiv:2609.21018v1 Announce Type: new Abstract: Recent visual document retrieval (VDR) systems such as ColPali use multi-vector page embeddings, in which patch-level vectors enable fine-grained evide...

By Xu Yuan, Hua Liu, Wenqi Fan, Qing Li

rag benchmarks

arXiv AI

Jun 4

Overview of the EReL@MIR 2025 Multimodal Document Retrieval Challenge (Track 1)

arXiv:2606. 04240v1 Announce Type: cross Abstract: Retrieval over visually-rich documents, pages that interleave text with figures, tables, and charts, is essential for multimodal retrieval-augmented generation, yet most retrievers still discard the visual channel.

By Jingbiao Mei

llms rag fine-tuning multimodal

arXiv AI

Sep 30

ResComEmb: Effective and Efficient Multimodal Embedding via Residual Homogeneity Compression

arXiv:2609.37225v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) have shown strong potential for universal multimodal representation learning. However, existing methods eith...

By Zijing Cai, Yuzhe Wang, Jingxian Zhu, Fengbin Zhu, Richang Hong

llms rag multimodal

arXiv Computer Vision

Sep 1

Doc-REFRAG: Rethinking Multimodal Document Retrieval-Augmented Generation

arXiv:2608.30163v1 Announce Type: cross Abstract: Real-world knowledge resides in multimodal documents, necessitating retrieval-augmented generation (RAG) for accurate question answering. However, ex...

By Ruofan Hu, Shengyang Xu, Minjie Hong, Xiaoda Yang, Sashuai Zhou, Ke Lei, Tao Jin, Zhou Zhao

rag nlp reinforcement-learning efficiency multimodal benchmarks

arXiv AI

Sep 10

Query-Aware Token Budgeting for Efficient Late-Interaction Visual Document Retrieval

arXiv:2609.07262v1 Announce Type: cross Abstract: Late-interaction visual document retrievers preserve fine-grained page evidence by storing many token embeddings per page, but the resulting storage...

By PS Rishi, Rajeev Ranjan Dwivedi, Vinod K Kurmi

rag

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