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.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

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

Jul 9

AnchorPrune: Relevance-Anchored Contextual Expansion for Visual Token Pruning

arXiv:2607. 07033v1 Announce Type: cross Abstract: Large vision-language models incur substantial inference costs because high-resolution inputs introduce thousands of visual tokens, many of which are redundant for a given query.

By Kyuan Oh, Bumsoo Kim

llms efficiency multimodal benchmarks

arXiv Machine Learning

Aug 3

FORGE: Frame Orthogonality in Relevance Geometry for Long-Form Video Understanding

arXiv:2607. 25266v2 Announce Type: replace-cross Abstract: Multimodal large language models (MLLMs) have enabled long-form video understanding at a scale that was not previously possible.

By Ghazal Kaviani, Ghassan AlRegib

llms rag nlp multimodal

arXiv Machine Learning

Aug 5

VIBE: Vector Index Benchmark for Embeddings

arXiv:2505. 17810v2 Announce Type: replace Abstract: Approximate nearest neighbor (ANN) search is a performance-critical component of many machine learning pipelines, and rigorous benchmarking is essential for assessing the performance of vector indexes for ANN search.

By Elias J\"a\"asaari, Ville Hyv\"onen, Matteo Ceccarello, Teemu Roos, Martin Aum\"uller

rag multimodal benchmarks

arXiv AI

Jun 30

PIXELRAG: Web Screenshots Beat Text for Retrieval-Augmented Generation

arXiv:2606. 28344v1 Announce Type: cross Abstract: Augmenting large language models (LLMs) with retrieved web text has become a dominant paradigm, yet the web is not natively textual: existing systems depend on complex parsing pipelines that linearize HTML and discard layout, visual structure, and formatting.

By Yichuan Wang, Zhifei Li, Zirui Wang, Paul Teiletche, Lesheng Jin, Matei Zaharia, Joseph E. Gonzalez, Sewon Min

llms rag agents multimodal benchmarks

arXiv AI

Jul 29

Multimodal Hybrid Retrieval-Augmented Generation for Scientific Document Understanding using Open-Source SLMs

arXiv:2607. 24799v1 Announce Type: cross Abstract: Large Language Models tend to hallucinate when answering domain-specific ques tions from scientific documents without prior fine-tuning.

By Alexandru-Andrei Sauc\u{a}, Ana-Luiza Rusnac

llms rag fine-tuning multimodal benchmarks

Related stories

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

AnchorPrune: Relevance-Anchored Contextual Expansion for Visual Token Pruning

FORGE: Frame Orthogonality in Relevance Geometry for Long-Form Video Understanding

VIBE: Vector Index Benchmark for Embeddings

PIXELRAG: Web Screenshots Beat Text for Retrieval-Augmented Generation

Multimodal Hybrid Retrieval-Augmented Generation for Scientific Document Understanding using Open-Source SLMs