mlsanityData to 14 Oct 2026

Qwen3 Embedding: Advancing Text Embedding and Reranking Through Foundation Models

arXiv 2506.05176 ↗First posted 5 Jun 2025cs.CLcited by 1,261

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Cited by 228 in LLM agents and tool use, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

In this work, we introduce the Qwen3 Embedding series, a significant advancement over its predecessor, the GTE-Qwen series, in text embedding and reranking capabilities, built upon the Qwen3 foundation models. Leveraging the Qwen3 LLMs' robust capabilities in multilingual text understanding and generation, our innovative multi-stage training pipeline combines large-scale unsupervised pre-training with supervised fine-tuning on high-quality datasets. Effective model merging strategies further ensure the robustness and adaptability of the Qwen3 Embedding series. During the training process, the Qwen3 LLMs serve not only as backbone models but also play a crucial role in synthesizing high-quality, rich, and diverse training data across multiple domains and languages, thus enhancing the training pipeline. The Qwen3 Embedding series offers a spectrum of model sizes (0.6B, 4B, 8B) for both embedding and reranking tasks, addressing diverse deployment scenarios where users can optimize for either efficiency or effectiveness. Empirical evaluations demonstrate that the Qwen3 Embedding series achieves state-of-the-art results across diverse benchmarks. Notably, it excels on the multilingual evaluation benchmark MTEB for text embedding, as well as in various retrieval tasks, including code retrieval, cross-lingual retrieval and multilingual retrieval. To facilitate reproducibility and promote community-driven research and development, the Qwen3 Embedding models are publicly available under the Apache 2.0 license.

Key facts. Cited by 1,261 papers in the map (data through 14 Oct 2026). Builds on 27 of its 28 references that are in the map. First posted on arXiv 5 Jun 2025.

Builds on

27 of 28 references.

References · 27

  1. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,170
  2. 2025Qwen3 Technical Reportcited by 8,820
  3. 2018Representation Learning with Contrastive Predictive Codingcited by 7,777
  4. 2024GPT-4o System Cardcited by 4,588
  5. 2020Dense Passage Retrieval for Open-Domain Question Answeringcited by 3,824
  6. 2022Text Embeddings by Weakly-Supervised Contrastive Pre-trainingcited by 1,253
  7. 2024M3-Embedding: Multi-Linguality, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillationcited by 1,073
  8. 2023Towards General Text Embeddings with Multi-stage Contrastive Learningcited by 723
  9. 2022MTEB: Massive Text Embedding Benchmarkcited by 640
  10. 2023C-Pack: Packed Resources For General Chinese Embeddingscited by 578
  11. 2024NV-Embed: Improved Techniques for Training LLMs as Generalist Embedding Modelscited by 472
  12. 2024Scaling Synthetic Data Creation with 1,000,000,000 Personascited by 417
15 more
  1. 2022One Embedder, Any Task: Instruction-Finetuned Text Embeddingscited by 333
  2. 2023Improving Text Embeddings with Large Language Modelscited by 303
  3. 2024mGTE: Generalized Long-Context Text Representation and Reranking Models for Multilingual Text Retrievalcited by 236
  4. 2024Generative Representational Instruction Tuningcited by 233
  5. 2020Embedding-based Retrieval in Facebook Searchcited by 156
  6. 2022Dense Text Retrieval based on Pretrained Language Models: A Surveycited by 136
  7. 2025MMTEB: Massive Multilingual Text Embedding Benchmarkcited by 125
  8. 2025Gemini Embedding: Generalizable Embeddings from Geminicited by 94
  9. 2023Zero-Shot Listwise Document Reranking with a Large Language Modelcited by 82
  10. 2023RankVicuna: Zero-Shot Listwise Document Reranking with Open-Source Large Language Modelscited by 70
  11. 2024FollowIR: Evaluating and Teaching Information Retrieval Models to Follow Instructionscited by 69
  12. 2023A Setwise Approach for Effective and Highly Efficient Zero-shot Ranking with Large Language Modelscited by 55
  13. 2023Embedding in Recommender Systems: A Surveycited by 20
  14. 2023A Two-Stage Adaptation of Large Language Models for Text Rankingcited by 18
  15. 2024Improving General Text Embedding Model: Tackling Task Conflict and Data Imbalance through Model Mergingcited by 14

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 275; 2026: 986.

1,261 papers.

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  1. 2025Evaluating Memory in LLM Agents via Incremental Multi-Turn Interactionscited by 254
  2. 2026Qwen3-VL-Embedding and Qwen3-VL-Reranker: A Unified Framework for State-of-the-Art Multimodal Retrieval and Rankingcited by 230
  3. 2025BrowseComp-Plus: A More Fair and Transparent Evaluation Benchmark of Deep-Research Agentcited by 200
  4. 2025EmbeddingGemma: Powerful and Lightweight Text Representationscited by 139
  5. 2025OpenRubrics: Towards Scalable Synthetic Rubric Generation for Reward Modeling and LLM Alignmentcited by 132
  6. 2025On the Theoretical Limitations of Embedding-Based Retrievalcited by 104
  7. 2025Reinforcement Learning for Self-Improving Agent with Skill Librarycited by 100
  8. 2025Jointly Reinforcing Diversity and Quality in Language Model Generationscited by 80
  9. 2026SkillX: Automatically Constructing Skill Knowledge Bases for Agentscited by 76
  10. 2025Remember Me, Refine Me: A Dynamic Procedural Memory Framework for Experience-Driven Agent Evolutioncited by 73
  11. 2025Breaking the Exploration Bottleneck: Rubric-Scaffolded Reinforcement Learning for General LLM Reasoningcited by 63
  12. 2026Memento-Skills: Let Agents Design Agentscited by 62
38 more
  1. 2025MemoryBench: A Benchmark for Memory and Continual Learning in LLM Systemscited by 61
  2. 2026SkillRouter: Skill Routing for LLM Agents at Scalecited by 61
  3. 2026EverMemOS: A Self-Organizing Memory Operating System for Structured Long-Horizon Reasoningcited by 54
  4. 2025WorldMM: Dynamic Multimodal Memory Agent for Long Video Reasoningcited by 53
  5. 2026AMA-Bench: Evaluating Long-Horizon Memory for Agentic Applicationscited by 53
  6. 2026SkillNet: Create, Evaluate, and Connect AI Skillscited by 53
  7. 2025Llama-Embed-Nemotron-8B: A Universal Text Embedding Model for Multilingual and Cross-Lingual Taskscited by 51
  8. 2025LiveMCPBench: Can Agents Navigate an Ocean of MCP Tools?cited by 48
  9. 2025SkyRL-Agent: Efficient RL Training for Multi-turn LLM Agentcited by 48
  10. 2025ToolOrchestra: Elevating Intelligence via Efficient Model and Tool Orchestrationcited by 47
  11. 2026SWE-Pruner: Self-Adaptive Context Pruning for Coding Agentscited by 44
  12. 2025MetaEmbed: Scaling Multimodal Retrieval at Test-Time with Flexible Late Interactioncited by 39
  13. 2026τ-Knowledge: Evaluating Conversational Agents over Unstructured Knowledgecited by 39
  14. 2026SkillRet: A Large-Scale Benchmark for Skill Retrieval in LLM Agentscited by 39
  15. 2025Seq vs Seq: An Open Suite of Paired Encoders and Decoderscited by 38
  16. 2026Beyond Semantic Similarity: Rethinking Retrieval for Agentic Search via Direct Corpus Interactioncited by 38
  17. 2025DIVER: A Multi-Stage Approach for Reasoning-intensive Information Retrievalcited by 37
  18. 2026OpenResearcher: A Fully Open Pipeline for Long-Horizon Deep Research Trajectory Synthesiscited by 37
  19. 2025KaLM-Embedding-V2: Superior Training Techniques and Data Inspire A Versatile Embedding Modelcited by 36
  20. 2025Planning with Reasoning using Vision Language World Modelcited by 36
  21. 2025Beyond GPT-5: Making LLMs Cheaper and Better via Performance-Efficiency Optimized Routingcited by 34
  22. 2025Coevolutionary Continuous Discrete Diffusion: Make Your Diffusion Language Model a Latent Reasonercited by 32
  23. 2025SPELL: Self-Play Reinforcement Learning for Evolving Long-Context Language Modelscited by 29
  24. 2025ICL-Router: In-Context Learned Model Representations for LLM Routingcited by 28
  25. 2025Narrow Finetuning Leaves Clearly Readable Traces in Activation Differencescited by 28
  26. 2025Llama Nemoretriever Colembed: Top-Performing Text-Image Retrieval Modelcited by 27
  27. 2026ViDoRe V3: A Comprehensive Evaluation of Retrieval Augmented Generation in Complex Real-World Scenarioscited by 26
  28. 2025Pillar-0: A New Frontier for Radiology Foundation Modelscited by 25
  29. 2026SkillFlow:Benchmarking Lifelong Skill Discovery and Evolution for Autonomous Agentscited by 25
  30. 2025jina-reranker-v3: Last but Not Late Interaction for Listwise Document Rerankingcited by 24
  31. 2026A-RAG: Scaling Agentic Retrieval-Augmented Generation via Hierarchical Retrieval Interfacescited by 24
  32. 2026Reinforcement World Model Learning for LLM-based Agentscited by 24
  33. 2026REMem: Reasoning with Episodic Memory in Language Agentcited by 23
  34. 2026SkillOrchestra: Learning to Route Agents via Skill Transfercited by 23
  35. 2025Scaling Language-Centric Omnimodal Representation Learningcited by 22
  36. 2025Ming-Flash-Omni: A Sparse, Unified Architecture for Multimodal Perception and Generationcited by 22
  37. 2025Kardia-R1: Unleashing LLMs to Reason toward Understanding and Empathy for Emotional Support via Rubric-as-Judge Reinforcement Learningcited by 22
  38. 2026jina-embeddings-v5-text: Task-Targeted Embedding Distillationcited by 22

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