mlsanityData to 7 Oct 2026

Qwen3-VL-Embedding and Qwen3-VL-Reranker: A Unified Framework for State-of-the-Art Multimodal Retrieval and Ranking

arXiv 2601.04720 ↗First posted 8 Jan 2026cs.CLcited by 211

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Cited by 52 in Multimodal LLMs / vision-language models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

In this report, we introduce the Qwen3-VL-Embedding and Qwen3-VL-Reranker model series, the latest extensions of the Qwen family built on the Qwen3-VL foundation model. Together, they provide an end-to-end pipeline for high-precision multimodal search by mapping diverse modalities, including text, images, document images, and video, into a unified representation space. The Qwen3-VL-Embedding model employs a multi-stage training paradigm, progressing from large-scale contrastive pre-training to reranking model distillation, to generate semantically rich high-dimensional vectors. It supports Matryoshka Representation Learning, enabling flexible embedding dimensions, and handles inputs up to 32k tokens. Complementing this, Qwen3-VL-Reranker performs fine-grained relevance estimation for query-document pairs using a cross-encoder architecture with cross-attention mechanisms. Both model series inherit the multilingual capabilities of Qwen3-VL, supporting more than 30 languages, and are released in 2B and 8B parameter sizes to accommodate diverse deployment requirements. Empirical evaluations demonstrate that the Qwen3-VL-Embedding series achieves state-of-the-art results across diverse multimodal embedding evaluation benchmarks. Specifically, Qwen3-VL-Embedding-8B attains an overall score of 77.8 on MMEB-V2, ranking first among all models (as of January 8, 2025). This report presents the architecture, training methodology, and practical capabilities of the series, demonstrating their effectiveness on various multimodal retrieval tasks, including image-text retrieval, visual question answering, and video-text matching.

Key facts. Cited by 211 papers in the map (data through 7 Oct 2026). Builds on 37 of its 45 references that are in the map. First posted on arXiv 8 Jan 2026.

Builds on

37 of 45 references.

References · 37

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
  2. 2014Microsoft COCO: Common Objects in Contextcited by 17,498
  3. 2018Representation Learning with Contrastive Predictive Codingcited by 7,662
  4. 2024GPT-4o System Cardcited by 4,467
  5. 2024Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolutioncited by 4,151
  6. 2016SQuAD: 100,000+ Questions for Machine Comprehension of Textcited by 4,115
  7. 2025Qwen3-VL Technical Reportcited by 2,534
  8. 2016Making the V in VQA Matter: Elevating the Role of Image Understanding in Visual Question Answeringcited by 2,498
  9. 2015Character-level Convolutional Networks for Text Classificationcited by 2,293
  10. 2013Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computationcited by 2,031
  11. 2016MS MARCO: A Human Generated MAchine Reading COmprehension Datasetcited by 1,591
  12. 2022Text Embeddings by Weakly-Supervised Contrastive Pre-trainingcited by 1,216
25 more
  1. 2025Qwen3 Embedding: Advancing Text Embedding and Reranking Through Foundation Modelscited by 1,175
  2. 2024M3-Embedding: Multi-Linguality, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillationcited by 1,050
  3. 2021NExT-QA:Next Phase of Question-Answering to Explaining Temporal Actionscited by 735
  4. 2023Towards General Text Embeddings with Multi-stage Contrastive Learningcited by 705
  5. 2020Contrastive Learning with Hard Negative Samplescited by 619
  6. 2019Learned Step Size Quantizationcited by 604
  7. 2025VideoLLaMA 3: Frontier Multimodal Foundation Models for Image and Video Understandingcited by 502
  8. 2024NV-Embed: Improved Techniques for Training LLMs as Generalist Embedding Modelscited by 466
  9. 2024Multilingual E5 Text Embeddings: A Technical Reportcited by 357
  10. 2022Matryoshka Representation Learningcited by 268
  11. 2024VLM2Vec: Training Vision-Language Models for Massive Multimodal Embedding Taskscited by 222
  12. 2024ColPali: Efficient Document Retrieval with Vision Language Modelscited by 190
  13. 2024E5-V: Universal Embeddings with Multimodal Large Language Modelscited by 123
  14. 2025MMTEB: Massive Multilingual Text Embedding Benchmarkcited by 120
  15. 2025VLM2Vec-V2: Advancing Multimodal Embedding for Videos, Images, and Visual Documentscited by 110
  16. 2025Gemini Embedding: Generalizable Embeddings from Geminicited by 93
  17. 2024MegaPairs: Massive Data Synthesis For Universal Multimodal Retrievalcited by 84
  18. 2025Llama-Embed-Nemotron-8B: A Universal Text Embedding Model for Multilingual and Cross-Lingual Taskscited by 46
  19. 2025A Survey of Multimodal Retrieval-Augmented Generationcited by 37
  20. 2025RzenEmbed: Towards Comprehensive Multimodal Retrievalcited by 28
  21. 2023Multimodality Representation Learning: A Survey on Evolution, Pretraining and Its Applicationscited by 27
  22. 2025Llama Nemoretriever Colembed: Top-Performing Text-Image Retrieval Modelcited by 24
  23. 2024Improving General Text Embedding Model: Tackling Task Conflict and Data Imbalance through Model Mergingcited by 14
  24. 2025Supervised Fine-Tuning or Contrastive Learning? Towards Better Multimodal LLM Rerankingcited by 6
  25. 2025MOON Embedding: Multimodal Representation Learning for E-commerce Search Advertisingcited by 5

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 211.

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  1. 2026Cosmos 3: Omnimodal World Models for Physical AIcited by 137
  2. 2026According to Me: Long-Term Personalized Referential Memory QAcited by 12
  3. 2026GEditBench v2: A Human-Aligned Benchmark for General Image Editingcited by 11
  4. 2026Unlocking Multimodal Document Intelligence: From Current Triumphs to Future Frontiers of Visual Document Retrievalcited by 8
  5. 2026CausalEmbed: Auto-Regressive Multi-Vector Generation in Latent Space for Visual Document Embeddingcited by 6
  6. 2026DeepImageSearch: Benchmarking Multimodal Agents for Context-Aware Image Retrieval in Visual Historiescited by 6
  7. 2026TSEmbed: Unlocking Task Scaling in Universal Multimodal Embeddingscited by 6
  8. 2026MMEmb-R1: Reasoning-Enhanced Multimodal Embedding with Pair-Aware Selection and Adaptive Controlcited by 6
  9. 2026TeleBoost: A Systematic Alignment Framework for High-Fidelity, Controllable, and Robust Video Generationcited by 5
  10. 2026VimRAG: Navigating Massive Visual Context in Retrieval-Augmented Generation via Multimodal Memory Graphcited by 5
  11. 2026AgenticOCR: Parsing Only What You Need for Efficient Retrieval-Augmented Generationcited by 5
  12. 2026ELVA: Exploring Ranking-Driven Universal Multimodal Retrievalcited by 5
38 more
  1. 2026Vesta: A Generalist Embodied Reasoning Modelcited by 5
  2. 2026OmniScience: A Large-scale Multi-modal Dataset for Scientific Image Understandingcited by 4
  3. 2026MOON3.0: Reasoning-aware Multimodal Representation Learning for E-commerce Product Understandingcited by 4
  4. 2026OpenSpatial: A Principled Data Engine for Empowering Spatial Intelligencecited by 4
  5. 2026Small Vision-Language Models are Smart Compressors for Long Video Understandingcited by 4
  6. 2026Denoising, Fast and Slow: Difficulty-Aware Adaptive Sampling for Image Generationcited by 4
  7. 2026IPIBench: Evaluating Interactive Proactive Intelligence of MLLMs under Continuous Streamscited by 4
  8. 2026WeMM-Embedding: WeChat Multi-Modal Embedding Technical Reportcited by 4
  9. 2026Magic-MM-Embedding: Towards Visual-Token-Efficient Universal Multimodal Embedding with MLLMscited by 3
  10. 2026Reasoning-Augmented Representations for Multimodal Retrievalcited by 3
  11. 2026ARK: A Dual-Axis Multimodal Retrieval Benchmark along Reasoning and Knowledgecited by 3
  12. 2026MultiBind: A Benchmark for Attribute Misbinding in Multi-Subject Generationcited by 3
  13. 2026MuSEAgent: A Multimodal Reasoning Agent with Stateful Experiencescited by 3
  14. 2026EVE: Verifiable Self-Evolution of MLLMs via Executable Visual Transformationscited by 3
  15. 2026Low-Cost Black-Box Detection of LLM Hallucinations via Dynamical System Predictioncited by 3
  16. 2026SteerVTE: Seamless Video Text Editing with Style and Glyph Controlcited by 3
  17. 2026Fusion Embedding: A Unified Embedding Space for Text, Image, Video, and Audiocited by 3
  18. 2026MolEmb: Multimodal Large Language Models Can Be Strong Molecular Embedding Modelscited by 3
  19. 2026Pailitao-VL: Unified Embedding and Reranker for Real-Time Multi-Modal Industrial Searchcited by 2
  20. 2026Beyond Global Similarity: Towards Fine-Grained, Multi-Condition Multimodal Retrievalcited by 2
  21. 2026PhotoBench: Beyond Visual Matching Towards Personalized Intent-Driven Photo Retrievalcited by 2
  22. 2026CAST: Modeling Visual State Transitions for Consistent Video Retrievalcited by 2
  23. 2026NanoVDR: Distilling a 2B Vision-Language Retriever into a 70M Text-Only Encoder for Visual Document Retrievalcited by 2
  24. 2026SemanticFace: Semantic Facial Action Estimation via Semantic Distillation in Interpretable Spacecited by 2
  25. 2026RS-WorldModel: a Unified Model for Remote Sensing Understanding and Future Sense Forecastingcited by 2
  26. 2026Towards Generation-Efficient Uncertainty Estimation in Large Language Modelscited by 2
  27. 2026MINER: Mining Multimodal Internal Representation for Efficient Retrievalcited by 2
  28. 2026PIXELRAG: Web Screenshots Beat Text for Retrieval-Augmented Generationcited by 2
  29. 2026MM-IssueLoc: A Controlled Benchmark for Evaluating Visual Evidence in Multimodal Repository-Level Issue Localizationcited by 2
  30. 2026Douyin Multimodal Embedding Model Technical Reportcited by 2
  31. 2026UEmbed: Unified Sparse and Dense Multimodal Embeddingscited by 2
  32. 2026CAPE-T2V: Captioner-Anchored Prompt Enhancement toward Two-Sided Conditioning Alignment in Text-to-Video Generationcited by 2
  33. 2026RANKVIDEO: Reasoning Reranking for Text-to-Video Retrievalcited by 1
  34. 2026OSCAR: Optimization-Steered Agentic Planning for Composed Image Retrievalcited by 1
  35. 2026Can Thinking Models Think to Detect Hateful Memes?cited by 1
  36. 2026Reconstructing Content with Collaborative Attention for Universal Multimodal Representation Learningcited by 1
  37. 2026LucidNFT: LR-Anchored Multi-Reward Preference Optimization for Flow-Based Real-World Super-Resolutioncited by 1
  38. 2026EXPLORE-Bench: Egocentric Scene Prediction with Long-Horizon Reasoningcited by 1

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