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.

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

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 Machine Learning

1d ago

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 AI

Jun 16

MMLongEmbed: Benchmarking Multimodal Embedding Models in Long-Context Scenarios

arXiv:2606. 14747v1 Announce Type: cross Abstract: Recent advancements have significantly expanded the theoretical context windows of Multimodal Embedding Models (MEMs).

By Haitian Wang, Ruoxi Sun, Quantong Qiu, Juntao Li, Junhui Li, Hua Chen, Jinxiong Chang, Min Zhang

rag multimodal benchmarks

arXiv AI

Jun 19

ELVA: Exploring Ranking-Driven Universal Multimodal Retrieval

arXiv:2606. 20280v1 Announce Type: cross Abstract: Leveraging Multimodal Large Language Models (MLLMs) via contrastive learning has become a mainstream paradigm for improving the performance of Universal Multimodal Retrieval (UMR).

By Yuhan Liu, Pei Fu, Hang Li, Yukun Qi, Chao Jiang, Jingwen Fu, Zhen Liu, Bin Qin, Zhenbo Luo, Jian Luan, Jingmin Xin

llms reinforcement-learning multimodal benchmarks

arXiv AI

5d ago

Generative Universal Multimodal Retrieval with Dual-role Identifiers

arXiv:2608. 12987v1 Announce Type: cross Abstract: Generative information retrieval (GIR) has emerged as a compelling alternative to the conventional index-retrieve-then-rank retrieval pipeline by training a generator to produce the identifiers of relevant items directly.

By Kaipeng Li, Haitao Yu, Xuanchen Zhou

rag diffusion efficiency multimodal benchmarks

arXiv Machine Learning

Jun 16

MVEB: Massive Video Embedding Benchmark

arXiv:2606. 14958v1 Announce Type: cross Abstract: We introduce the Massive Video Embedding Benchmark (MVEB), a 23-task benchmark for video embeddings spanning classification, zero-shot classification, clustering, pair classification, retrieval, and video-centric question answering.

By Adnan El Assadi, Roman Solomatin, Isaac Chung, Chenghao Xiao, Deep Shah, Manan Dey, Shriya Sudhakar, Zacharie Bugaud, Wissam Siblini, Ayush Sunil Munot, Yashwanth Devavarapu, Rakshitha Ireddi, Michelle Yang, M\'arton Kardos, Niklas Muennighoff, Kenneth Enevoldsen

rag nlp multimodal benchmarks

Related stories

Multimodal Representation Alignment for Cross-modal Information Retrieval

FuseLIP: Multimodal Embeddings via Early Fusion of Discrete Tokens

MMLongEmbed: Benchmarking Multimodal Embedding Models in Long-Context Scenarios

ELVA: Exploring Ranking-Driven Universal Multimodal Retrieval

Generative Universal Multimodal Retrieval with Dual-role Identifiers

MVEB: Massive Video Embedding Benchmark