# RzenEmbed: Towards Comprehensive Multimodal Retrieval

arXiv: https://arxiv.org/abs/2510.27350 · HTML: https://mlsanity.com/paper/2510.27350/

## Key facts

- Cited by 29 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 47 of its 51 references that are in the map.
- First posted on arXiv 31 Oct 2025.

## Placed in

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

## Builds on (most cited first, 30 of 47)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Visual Instruction Tuning](https://mlsanity.com/paper/2304.08485/) (2023)
- [BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](https://mlsanity.com/paper/2301.12597/) (2023)
- [Qwen2.5-VL Technical Report](https://mlsanity.com/paper/2502.13923/) (2025)
- [BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation](https://mlsanity.com/paper/2201.12086/) (2022)
- [Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution](https://mlsanity.com/paper/2409.12191/) (2024)
- [LAION-5B: An open large-scale dataset for training next generation image-text models](https://mlsanity.com/paper/2210.08402/) (2022)
- [SQuAD: 100,000+ Questions for Machine Comprehension of Text](https://mlsanity.com/paper/1606.05250/) (2016)
- [HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering](https://mlsanity.com/paper/1809.09600/) (2018)
- [Sigmoid Loss for Language Image Pre-Training](https://mlsanity.com/paper/2303.15343/) (2023)
- [TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension](https://mlsanity.com/paper/1705.03551/) (2017)
- [LLaVA-OneVision: Easy Visual Task Transfer](https://mlsanity.com/paper/2408.03326/) (2024)
- [SimCSE: Simple Contrastive Learning of Sentence Embeddings](https://mlsanity.com/paper/2104.08821/) (2021)
- [Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone](https://mlsanity.com/paper/2404.14219/) (2024)
- [MS MARCO: A Human Generated MAchine Reading COmprehension Dataset](https://mlsanity.com/paper/1611.09268/) (2016)
- [InternVL3.5: Advancing Open-Source Multimodal Models in Versatility, Reasoning, and Efficiency](https://mlsanity.com/paper/2508.18265/) (2025)
- [FEVER: a large-scale dataset for Fact Extraction and VERification](https://mlsanity.com/paper/1803.05355/) (2018)
- [Reproducible scaling laws for contrastive language-image learning](https://mlsanity.com/paper/2212.07143/) (2022)
- [ShareGPT4V: Improving Large Multi-Modal Models with Better Captions](https://mlsanity.com/paper/2311.12793/) (2023)
- [Towards General Text Embeddings with Multi-stage Contrastive Learning](https://mlsanity.com/paper/2308.03281/) (2023)
- [PaliGemma: A versatile 3B VLM for transfer](https://mlsanity.com/paper/2407.07726/) (2024)
- [Contrastive Learning with Hard Negative Samples](https://mlsanity.com/paper/2010.04592/) (2020)
- [Florence-2: Advancing a Unified Representation for a Variety of Vision Tasks](https://mlsanity.com/paper/2311.06242/) (2023)
- [Improving Text Embeddings with Large Language Models](https://mlsanity.com/paper/2401.00368/) (2023)
- [VLM2Vec: Training Vision-Language Models for Massive Multimodal Embedding Tasks](https://mlsanity.com/paper/2410.05160/) (2024)
- [CogVLM2: Visual Language Models for Image and Video Understanding](https://mlsanity.com/paper/2408.16500/) (2024)
- [ColPali: Efficient Document Retrieval with Vision Language Models](https://mlsanity.com/paper/2407.01449/) (2024)
- [UniIR: Training and Benchmarking Universal Multimodal Information Retrievers](https://mlsanity.com/paper/2311.17136/) (2023)
- [MM-Embed: Universal Multimodal Retrieval with Multimodal LLMs](https://mlsanity.com/paper/2411.02571/) (2024)
- [EmbeddingGemma: Powerful and Lightweight Text Representations](https://mlsanity.com/paper/2509.20354/) (2025)

## Built on by (most cited first, 29 of 29)

- [Qwen3-VL-Embedding and Qwen3-VL-Reranker: A Unified Framework for State-of-the-Art Multimodal Retrieval and Ranking](https://mlsanity.com/paper/2601.04720/) (2026)
- [Sculpting the Vector Space: Towards Efficient Multi-Vector Visual Document Retrieval via Prune-then-Merge Framework](https://mlsanity.com/paper/2602.19549/) (2026)
- [Unlocking Multimodal Document Intelligence: From Current Triumphs to Future Frontiers of Visual Document Retrieval](https://mlsanity.com/paper/2602.19961/) (2026)
- [WeMM-Embedding: WeChat Multi-Modal Embedding Technical Report](https://mlsanity.com/paper/2608.24053/) (2026)
- [CausalEmbed: Auto-Regressive Multi-Vector Generation in Latent Space for Visual Document Embedding](https://mlsanity.com/paper/2601.21262/) (2026)
- [Em-Garde: A Propose-Match Framework for Proactive Streaming Video Understanding](https://mlsanity.com/paper/2603.19054/) (2026)
- [TSEmbed: Unlocking Task Scaling in Universal Multimodal Embeddings](https://mlsanity.com/paper/2603.04772/) (2026)
- [MMEmb-R1: Reasoning-Enhanced Multimodal Embedding with Pair-Aware Selection and Adaptive Control](https://mlsanity.com/paper/2604.06156/) (2026)
- [LaME: Learning to Think in Latent Space for Multimodal Embedding via Information Bottleneck](https://mlsanity.com/paper/2606.13061/) (2026)
- [Magic-MM-Embedding: Towards Visual-Token-Efficient Universal Multimodal Embedding with MLLMs](https://mlsanity.com/paper/2602.05275/) (2026)
- [ARK: A Dual-Axis Multimodal Retrieval Benchmark along Reasoning and Knowledge](https://mlsanity.com/paper/2602.09839/) (2026)
- [V-Retrver: Evidence-Driven Agentic Reasoning for Universal Multimodal Retrieval](https://mlsanity.com/paper/2602.06034/) (2026)
- [Bottleneck Tokens for Unified Multimodal Retrieval](https://mlsanity.com/paper/2604.11095/) (2026)
- [ExToken: Structured Exploration for Efficient Vision-Language-Action Reinforcement Fine-tuning](https://mlsanity.com/paper/2607.12931/) (2026)
- [Douyin Multimodal Embedding Model Technical Report](https://mlsanity.com/paper/2608.02148/) (2026)
- [UEmbed: Unified Sparse and Dense Multimodal Embeddings](https://mlsanity.com/paper/2608.02583/) (2026)
- [Rethinking Composed Image Retrieval Evaluation: A Fine-Grained Benchmark from Image Editing](https://mlsanity.com/paper/2601.16125/) (2026)
- [OSCAR: Optimization-Steered Agentic Planning for Composed Image Retrieval](https://mlsanity.com/paper/2602.08603/) (2026)
- [Weaving Visual Narratives: Agentic Image Bundle Composition Beyond Atomic Visual Matching](https://mlsanity.com/paper/2608.28695/) (2026)
- [FBCIR: Balancing Cross-Modal Focuses in Composed Image Retrieval](https://mlsanity.com/paper/2603.11520/) (2026)
- [Do Composed Image Retrieval Benchmarks Require Multimodal Composition?](https://mlsanity.com/paper/2605.14787/) (2026)
- [MMLongEmbed: Benchmarking Multimodal Embedding Models in Long-Context Scenarios](https://mlsanity.com/paper/2606.14747/) (2026)
- [Learning from Failures: Retrieval-Centric CoT via Hard Negatives for Unified Multimodal Retrieval](https://mlsanity.com/paper/2608.06060/) (2026)
- [DSE-VTG: Dual-Side Enhancement for Training-Free Video Temporal Grounding](https://mlsanity.com/paper/2609.08850/) (2026)
- [RegRet: Enhancing Region-Level Retrieval in Large Multimodal Models](https://mlsanity.com/paper/2609.16847/) (2026)
- [VaME: Exploring Variational Latent Reasoning for Multimodal Embeddings](https://mlsanity.com/paper/2609.33402/) (2026)
- [CoVisco: Codec-Native Vision Encoder with Native Token Compression for Unified Image-Video Understanding](https://mlsanity.com/paper/2609.39924/) (2026)
- [Omni-Embed-Mini: Binding Modalities Without Forgetting via Dense Distillation](https://mlsanity.com/paper/2610.02148/) (2026)
- [A Simulation-Grounded Agentic VLM Framework for Wildfire Monitoring and Reporting](https://mlsanity.com/paper/2610.02451/) (2026)

## Abstract (arXiv)

The rapid advancement of Multimodal Large Language Models (MLLMs) has extended CLIP-based frameworks to produce powerful, universal embeddings for retrieval tasks. However, existing methods primarily focus on natural images, offering limited support for other crucial visual modalities such as videos and visual documents. To bridge this gap, we introduce RzenEmbed, a unified framework to learn embeddings across a diverse set of modalities, including text, images, videos, and visual documents. We employ a novel two-stage training strategy to learn discriminative representations. The first stage focuses on foundational text and multimodal retrieval. In the second stage, we introduce an improved InfoNCE loss, incorporating two key enhancements. Firstly, a hardness-weighted mechanism guides the model to prioritize challenging samples by assigning them higher weights within each batch. Secondly, we implement an approach to mitigate the impact of false negatives and alleviate data noise. This strategy not only enhances the model's discriminative power but also improves its instruction-following capabilities. We further boost performance with learnable temperature parameter and model souping. RzenEmbed sets a new state-of-the-art on the MMEB benchmark. It not only achieves the best overall score but also outperforms all prior work on the challenging video and visual document retrieval tasks. Our models are available in https://huggingface.co/qihoo360/RzenEmbed.