arXiv AI August 13, 2026 By Archan Dutta, Vyanktesh Kanungo

Can Frontier LLMs Match Natively Multimodal Embeddings? A Comparison on Hard-Negative Text-to-Image Retrieval

arXiv:2608. 11343v1 Announce Type: new Abstract: Multimodal retrieval and classification across different types of media, spanning text, images,video and audio, has traditionally relied on dual-encoder models that align visual and textual representations through contrastive learning.

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 Computer Vision

Sep 3

MARS: What Retrieval Signals Are Hidden in Multimodal Large Language Models for Text-Video Retrieval?

MARS introduces a multi‑layer, multi‑slot embedding framework for text‑video retrieval that constructs adaptive representation slots by combining hidden states from different decoder layers. By comparing corresponding text and video slots and aggregating their similarities, MARS captures fine‑grained cues that single‑token embeddings miss. A hard‑negative‑aware slot specialization objective further encourages slots to focus on discriminative matching cues, leading to state‑of‑the‑art results on four benchmarks.

By Uicheol Jung, Juyoung Hong, Geuntaek Lim, Yukyung Choi

llms rag multimodal benchmarks

arXiv Computation and Language

Aug 27

Recurrence Meets Transformers for Universal Multimodal Retrieval

The paper introduces ReT-2, a unified retrieval model that handles multimodal queries containing both images and text and searches across multimodal document collections. It employs a recurrent Transformer architecture with LSTM-inspired gating to integrate information across layers and modalities, capturing fine-grained visual and textual details. Evaluations on M2KR and M-BEIR benchmarks show state‑of‑the‑art performance, faster inference, and lower memory usage, and the model also boosts downstream tasks in retrieval‑augmented generation pipelines.

By Davide Caffagni, Sara Sarto, Marcella Cornia, Lorenzo Baraldi, Rita Cucchiara

llms rag fine-tuning multimodal benchmarks

arXiv AI

Jun 30

Multimodal Representation Alignment for Cross-modal Information Retrieval

arXiv:2506. 08774v2 Announce Type: replace-cross Abstract: Different machine learning models can represent the same underlying concept in different ways.

By Fan Xu, Luis A. Leiva

llms rag multimodal benchmarks safety

arXiv AI

4d ago

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 Machine Learning

Aug 18

FuseLIP: Multimodal Embeddings via Early Fusion of Discrete Tokens

arXiv:2506. 03096v2 Announce Type: replace-cross Abstract: Contrastive language-image pre-training aligns features of text-image pairs in a common latent space via distinct encoders for each modality.

By Christian Schlarmann, Francesco Croce, Nicolas Flammarion, Matthias Hein

llms rag diffusion multimodal

arXiv Computer Vision

Aug 25

Training-Free Pseudo-Fusion for Composed Image Retrieval with Diffusion Models and Multimodal Large Language Models

arXiv:2608.23102v1 Announce Type: new Abstract: Composed Image Retrieval (CIR) is an emerging paradigm in content-based image retrieval that enables users to formulate compositional queries by combin...