Awesome Multivector Retrieval

An extensive and commented list of resources on late-interaction multivector retrieval.

Contents

• Awesome Multivector Retrieval
  • Contents
  • Models
    • Foundational Models
    • General Models & Training
    • Compression & Token Pruning
    • Multimodal & Vision
    • Theory
  • Retrieval
    • Indexing & Search Algorithms
    • Scoring Kernels
  • Surveys, Analyses & Reproducibility
  • Software Libraries
    • Training & Inference Frameworks
    • Retrieval Engines & Indexes
    • Scoring Kernels
  • Model Checkpoints
    • General-Purpose
    • Specialized / Domain
    • Multimodal & Vision
  • Datasets and Encodings
    • NFCorpus
    • SciFact
    • ArguAna
    • SCIDOCS
    • FiQA-2018
    • TREC-COVID
    • Quora
    • LoTTE-pooled
    • MS MARCO v1
    • ViDoRe v3
  • Multimedia Resources

Models

Foundational Models

• ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT
  Omar Khattab, Matei Zaharia
  SIGIR, 2020
  📄 paper | 🛠️ code

• COIL: Revisit Exact Lexical Match in Information Retrieval with Contextualized Inverted List
  Luyu Gao, Zhuyun Dai, Jamie Callan
  NAACL, 2021
  📄 paper

• ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interaction
  Keshav Santhanam, Omar Khattab, Jon Saad-Falcon, Christopher Potts, Matei Zaharia
  NAACL, 2022
  📄 paper | 🛠️ code

General Models & Training

• Jina-ColBERT-v2: A General-Purpose Multilingual Late Interaction Retriever
  Rohan Jha, Bo Wang, Michael Günther, Georgios Mastrapas, Saba Sturua, Isabelle Mohr, Andreas Koukounas, Mohammad Kalim Akram, Nan Wang, Han Xiao
  MRL Workshop, 2024
  📄 paper

• PyLate: Flexible Training and Retrieval for Late Interaction Models
  Antoine Chaffin, Raphaël Sourty
  CIKM, 2025
  📄 paper | 🛠️ code

• ColBERT-Zero: To Pre-train Or Not To Pre-train ColBERT models
  Antoine Chaffin, Luca Arnaboldi, Amélie Chatelain, Florent Krzakala
  arXiv, 2026
  📄 paper

• Your Embedding Model is SMARTer Than You Think
  Jianrui Zhang, Hyun Jung Lee, Sukanta Ganguly, Tae-Eui Kam, Donghyun Kim, Yong Jae Lee
  arXiv, 2026
  📄 paper | 🛠️ code

• Party is over: regularizing ColBERT models to fix efficient ANN methods
  LightOn AI
  Blog, 2026
  📝 blog

• NumColBERT: Non-Intrusive Numeracy Injection for Late-Interaction Retrieval Models
  Haruki Fujimaki, Makoto P. Kato
  arXiv, 2026
  📄 paper

• mDenseOn with the mLateOn: Open Multilingual, Long-Context, and Code Retrieval Models
  LightOn AI
  Blog, 2026
  📝 blog

• GLInt: Geometry-Matched Hard Negatives for Late-Interaction Retrieval
  Aarush Sinha
  Blog, 2026
  📝 blog

• Multi-Vector (Late Interaction) Embedding Models with Sentence Transformers
  Tom Aarsen, Antoine Chaffin, Raphaël Sourty
  Blog, 2026
  📝 blog

• Training and Finetuning Multi-Vector Embedding Models with Sentence Transformers
  Tom Aarsen
  Blog, 2026
  📝 blog

• Exploring Static Embedding Retrieval
  Logan Markewich
  Blog, 2026
  📝 blog

• KURE-v2: A Korean-English Bilingual Late-Interaction Retrieval Model
  Youngjoon Jang
  Blog, 2026
  📝 blog

• SmallReason-ColBERT: An Ultra-Small Late-Interaction Retriever for Reasoning Intensive Retrieval
  Abdelrahman Abdallah, Mohammed Ali, Adam Jatowt
  EMNLP, 2026
  📄 paper | 🛠️ code

Compression & Token Pruning

• Compact Token Representations with Contextual Quantization for Efficient Document Re-ranking
  Yingrui Yang, Yifan Qiao, Tao Yang
  ACL, 2022
  📄 paper

• Learned Token Pruning in Contextualized Late Interaction over BERT (ColBERT)
  Carlos Lassance, Maroua Maachou, Joohee Park, Stephane Clinchant
  SIGIR, 2022
  📄 paper

• Introducing Neural Bag of Whole-Words with ColBERTer: Contextualized Late Interactions using Enhanced Reduction
  Sebastian Hofstatter, Omar Khattab, Sophia Althammer, Mete Sertkan, Allan Hanbury
  CIKM, 2022
  📄 paper | 🛠️ code

• Joint Optimization of Multi-Vector Representation with Product Quantization
  Yufan Fang, Jing Zhan, Yiqun Liu, Jiafeng Mao, Min Zhang, Shaoping Ma
  NLPCC, 2022
  📄 paper

• Multi-Vector Retrieval as Sparse Alignment
  Yujie Qian, Jinhyuk Lee, Sai Meher Karthik Duddu, Zhuyun Dai, Siddhartha Brahma, Iftekhar Naim, Tao Lei, Vincent Y. Zhao
  arXiv, 2022
  📄 paper

• CITADEL: Conditional Token Interaction via Dynamic Lexical Routing for Efficient and Effective Multi-Vector Retrieval
  Minghan Li, Sean C. Lin, Barlas Oguz, Arnab Ghoshal, Jimmy Lin, Yashar Mehdad, Wen-tau Yih, Xilun Chen
  ACL, 2023
  📄 paper

• SLIM: Sparsified Late Interaction for Multi-Vector Retrieval with Inverted Indexes
  Minghan Li, Sheng-Chieh Lin, Xueguang Ma, Jimmy Lin
  SIGIR, 2023
  📄 paper

• Static Pruning for Multi-Representation Dense Retrieval
  Antonio Acquavia, Craig Macdonald, Nicola Tonellotto
  DocEng, 2023
  📄 paper | 🛠️ code

• Rethinking the Role of Token Retrieval in Multi-Vector Retrieval
  Jinhyuk Lee, Zhuyun Dai, Sai Meher Karthik Duddu, Tao Lei, Iftekhar Naim, Ming-Wei Chang, Vincent Y. Zhao
  NeurIPS, 2023
  📄 paper

• SPLATE: Sparse Late Interaction Retrieval
  Thibault Formal, Stephane Clinchant, Herve Dejean, Carlos Lassance
  SIGIR, 2024
  📄 paper

• Reducing the Footprint of Multi-Vector Retrieval with Minimal Performance Impact via Token Pooling
  Benjamin Clavié, Antoine Chaffin, Griffin Adams
  arXiv, 2024
  📄 paper

• Muvera: Multi-Vector Retrieval via Fixed Dimensional Encodings
  Laxman Dhulipala, Majid Hadian, Rajesh Jayaram, Jason Lee, Vahab Mirrokni
  NeurIPS, 2024
  📄 paper

• Enhancing ColBERT: A Method for Reducing Space Complexity and Accelerating Retrieval Speed
  Hai Nguyen T., Huong Le T.
  PACLIC, 2024
  📄 paper

• Token Pruning Optimization for Efficient Multi-vector Dense Retrieval
  Shanxiu He, Mutasem Al-Darabsah, Suraj Nair, Jonathan May, Tarun Agarwal, Tao Yang, Choon Hui Teo
  ECIR, 2025
  📄 paper

• CRISP: Clustering Multi-Vector Representations for Denoising and Pruning
  João Veneroso, Rajesh Jayaram, Jinmeng Rao, Gustavo Hernández Ábrego, Majid Hadian, Daniel Cer
  arXiv, 2025
  📄 paper

• Towards Lossless Token Pruning in Late-Interaction Retrieval Models
  Yuxuan Zong, Benjamin Piwowarski
  SIGIR, 2025
  📄 paper

• ColPruner: Combining Complementary Pruning Approaches for ColBERT in Web Search
  Wondo Rhee, Chan Lim, Taewon Yoon, Gyuhyeon Choi, Jooyoung Lee
  ReNeuIR Workshop, 2025
  📄 paper

• Towards Storage-Efficient Visual Document Retrieval: An Empirical Study on Reducing Patch-Level Embeddings
  Yubo Ma, Jinsong Li, Yuhang Zang, Xiaobao Wu, Xiaoyi Dong, Pan Zhang, Yuhang Cao, Haodong Duan, Jiaqi Wang, Yixin Cao, Aixin Sun
  ACL Findings, 2025
  📄 paper

• Sculpting the Vector Space: Towards Efficient Multi-Vector Visual Document Retrieval via Prune-then-Merge Framework
  Yibo Yan, Mingdong Ou, Yi Cao, Xin Zou, Jiahao Huo, Shuliang Liu, James Kwok, Xuming Hu
  arXiv, 2026
  📄 paper

• Coverage Matters: MarginMerge for Compressing Multi-Vector Visual Document Retrievers
  Ailar Mahdizadeh, Aria Salari, Sohail Rajabi, Shahriar Mirabbasi, Panos Nasiopoulos, Alireza Morsali
  arXiv, 2026
  📄 paper

• AdaMerge: Tuning-Free Patch Compression for Multi-Vector Visual Document Retrieval
  Jianxin You, Kun Ni
  CIKM, 2026
  📄 paper

• Multi-Vector Index Compression in Any Modality
  Hanxiang Qin, Alexander Martin, Rohan Jha, Chunsheng Zuo, Reno Kriz, Benjamin Van Durme
  SIGIR, 2026
  📄 paper | 🛠️ code

• Learn to Pool: Lightweight Fine-Tuning for Flexible Multi-Vector Compression
  Stefan Josef
  LIR Workshop, 2026
  📄 paper

• A Voronoi Cell Formulation for Principled Token Pruning in Late-Interaction Retrieval Models
  Yash Kankanampati, Yuxuan Zong, Nadi Tomeh, Benjamin Piwowarski, Joseph Le Roux
  SIGIR, 2026
  📄 paper

• CrossQ: Task-Aligned Cross-Token Conditional Quantization for Late Interaction Retrieval
  Rohit Kumar Salla, Manoj Saravanan, Ramya Manasa Amancherla
  ICML, 2026
  📄 paper

• EigenLI: Spectral Approximations to Late Interaction
  Archish S, Sabyasachi Basu, Ankit Garg, Ravishankar Krishnaswamy, Kirankumar Shiragur
  arXiv, 2026
  📄 paper

• Generative Late-Interaction Embeddings For Visual Document Retrieval
  Mohamed Eltahir, Talal Aloushan, Rose Khairoalsendi, Jana Shata, Mohammed Alhassan, Leen Alrehaili, Tanveer Hussain, Naeemullah Khan
  arXiv, 2026
  📄 paper

Multimodal & Vision

• ColPali: Efficient Document Retrieval with Vision Language Models
  Manuel Faysse, Hugues Sibille, Tony Wu, Bilel Omrani, Gautier Viaud, Celine Hudelot, Pierre Colombo
  ICLR, 2025
  📄 paper

• Video-ColBERT: Contextualized Late Interaction for Text-to-Video Retrieval
  Arun V. Reddy, Alexander Martin, Eugene Yang, Andrew Yates, Kate Sanders, Kenton Murray, Reno Kriz, Celso M. de Melo, Benjamin Van Durme, Rama Chellappa
  CVPR, 2025
  📄 paper

• ColMate: Contrastive Late Interaction and Masked Text for Multimodal Document Retrieval
  Ahmed Masry, Megh Thakkar, Patrice Bechard, Sathwik Tejaswi Madhusudhan, Rabiul Awal, Shambhavi Mishra, Akshay Kalkunte Suresh, Srivatsava Daruru, Enamul Hoque, Spandana Gella, Torsten Scholak, Sai Rajeswar
  EMNLP, 2025
  📄 paper

• MetaEmbed: Scaling Multimodal Retrieval at Test-Time with Flexible Late Interaction
  Zilin Xiao, Qi Ma, Mengting Gu, Chun-cheng Jason Chen, Xintao Chen, Vicente Ordonez, Vijai Mohan
  ICLR, 2026
  📄 paper | 🛠️ code

• AdaptiveEmbed: Sample-Adaptive Multi-Vector Representation for Multimodal Retrieval
  Xinze Liu, Lei Yang, Dayan Wu, Hengjie Zhu, Zihao Zhang, Hanqi Wu, Tianzhu Hu, Peng Fu, Zheng Lin, Weiping Wang
  arXiv, 2026
  📄 paper

Theory

• Multi-Vector Embeddings are Provably More Expressive than Single Vector Embeddings
  Rajesh Jayaram
  arXiv, 2026
  📄 paper

• Quantifying and Expanding the Theoretical Capacity of Late-Interaction Retrieval Models
  Julian Killingback, Varad Ingale, Hamed Zamani, Cameron Musco
  arXiv, 2026
  📄 paper

• Retrieval Needs Multivectors: An Exponential Separation
  Mihir Agarwal, Viraj Agrawal, Sabyasachi Basu, Ankit Garg, Kirankumar Shiragur
  arXiv, 2026
  📄 paper

Retrieval

Indexing & Search Algorithms

• Baleen: Robust Multi-Hop Reasoning at Scale via Condensed Retrieval
  Omar Khattab, Christopher Potts, Matei Zaharia
  NeurIPS, 2021
  📄 paper

• PLAID: An Efficient Engine for Late Interaction Retrieval
  Keshav Santhanam, Omar Khattab, Christopher Potts, Matei Zaharia
  CIKM, 2022
  📄 paper | 🛠️ code

• DESSERT: An Efficient Algorithm for Vector Set Search with Vector Set Queries
  Joshua Engels, Benjamin Coleman, Vihan Lakshman, Anshumali Shrivastava
  NeurIPS, 2023
  📄 paper

• Efficient Multi-Vector Dense Retrieval with Bit Vectors
  Franco Maria Nardini, Cosimo Rulli, Rossano Venturini
  ECIR, 2024
  📄 paper | 🛠️ code

• Efficient Constant-Space Multi-vector Retrieval
  Sean MacAvaney, Antonio Mallia, Nicola Tonellotto
  ECIR, 2025
  📄 paper

• IGP: Efficient Multi-Vector Retrieval via Proximity Graph Index
  Ziyang Bian, Man Lung Yiu, Buzhou Tang
  SIGIR, 2025
  📄 paper | 🛠️ code

• WARP: An Efficient Engine for Multi-Vector Retrieval
  Jan Luca Scheerer, Matei Zaharia, Christopher Potts, Gustavo Alonso, Omar Khattab
  SIGIR, 2025
  📄 paper | 🛠️ code

• Multivector Reranking in the Era of Strong First-Stage Retrievers
  Silvio Martinico, Franco Maria Nardini, Cosimo Rulli, Rossano Venturini
  ECIR, 2026
  📄 paper | 🛠️ code | 🛠️ code

• SMVE: Sparse Multi-Vector Retrieval
  Martin Spisak, Marek Galovic
  LIR Workshop, 2026
  📝 blog

• No More K-means: Single-Stage Sparse Coding for Efficient Multi-Vector Retrieval
  Lixuan Guo, Yifei Wang, Tiansheng Wen, Aosong Feng, Stefanie Jegelka, Chenyu You
  ICML, 2026
  📄 paper

• LEMUR: Learned Multi-Vector Retrieval
  Elias Jääsaari, Ville Hyvönen, Teemu Roos
  ICML, 2026
  📄 paper | 🛠️ code

• Efficient Multivector Retrieval with Token-Aware Clustering and Hierarchical Indexing
  Silvio Martinico, Franco Maria Nardini, Cosimo Rulli, Rossano Venturini
  SIGIR, 2026
  📄 paper | 🛠️ code

• ColBERTSaR: Sparsified ColBERT Index via Product Quantization
  Eugene Yang, Andrew Yates, Dawn Lawrie, James Mayfield, Saron Samuel, Rohan Jha
  SIGIR, 2026
  📄 paper | 🛠️ code

• PLAID-PRF: Pseudo-Relevance Feedback with Centroid-like Tokens in PLAID
  Xiao Wang, Sean MacAvaney, Craig Macdonald
  SIGIR, 2026
  📄 paper | 🛠️ code

• Chimera: Efficient Multi-Vector Retrieval via GPU-CPU Co-Processing
  Yanqi Chen, Juelin Liu, Alexandra Meliou, Xiao Yan
  arXiv, 2026
  📄 paper

Scoring Kernels

• Col-Bandit: Query-Time Top-K Estimation for Late-Interaction Retrieval
  Roi Pony, Adi Raz Goldfarb, Oshri Naparstek, Idan Friedman, Udi Barzelay, Eli Schwartz
  arXiv, 2026
  📄 paper

• FLASH-MAXSIM: IO-Aware Fused Kernels for Late-Interaction Scoring
  Roi Pony, Adi Raz Goldfarb, Idan Friedman, Daniel Ezer, Udi Barzelay
  arXiv, 2026
  📄 paper | 🛠️ code

• TileMaxSim: IO-Aware GPU MaxSim Scoring with Dimension Tiling and Fused Product Quantization
  Ashutosh Sharma
  arXiv, 2026
  📄 paper | 🛠️ code

Surveys, Analyses & Reproducibility

• A Reproducibility Study of PLAID
  Sean MacAvaney, Nicola Tonellotto
  SIGIR, 2024
  📄 paper

• A Replicability Study of XTR
  Rohan Jha, Reno Kriz, Benjamin Van Durme
  arXiv, 2026
  📄 paper

• Reproduction Beyond Benchmarks: ConstBERT and ColBERT-v2 Across Backends and Query Distributions
  Utshab Kumar Ghosh, Ashish David, Shubham Chatterjee
  SIGIR, 2026
  📄 paper

• Comparing Token Pruning Approaches for Multi-Vector Retrieval
  Ferdinand Schlatt, Hanno Barschel, Matthias Hagen
  SIGIR, 2026
  📄 paper

• A Brief Comparison of Training-Free Multi-Vector Sequence Compression Methods
  Rohan Jha, Chunsheng Zuo, Reno Kriz, Benjamin Van Durme
  LIR Workshop, 2026
  📄 paper

• A Survey of Late-Interaction Neural Retrieval: Paradigms, Systems, and Research Frontiers
  Xiao Wang, Chuting Yu, Minghan Li, Binci Yang, Hang Li, Ben He
  SSRN, 2026
  📄 paper

Software Libraries

Training & Inference Frameworks

• ColBERT
  Reference implementation for ColBERT and ColBERTv2, and includes PLAID support for efficient late-interaction retrieval.

• RAGatouille
  Python toolkit to train and serve ColBERT-based late-interaction retrievers.

• PyLate
  Python library for training, fine-tuning, inference, and retrieval with ColBERT-style late-interaction models on single and multi-GPU setups.

• Sentence Transformers
  Framework for dense, sparse, reranker, and (v6.0+) ColBERT-style multi-vector late-interaction models via the MultiVectorEncoder, with a unified training and inference API.

• PyLate-rs
  High-performance Rust inference engine for PyLate models, with Python bindings and optimized integration with FastPlaid for retrieval pipelines.

Retrieval Engines & Indexes

• FastPlaid
  GPU-optimized engine for ColBERT/PLAID-style late-interaction retrieval.

• kANNolo
  ANN library for dense, sparse, and multivector retrieval.

• Vectorium
  Rust library for compact storage/access of dense, sparse, and multivector embeddings.

• Firn
  Rust search engine for single-vector and late-interaction multivector namespaces, backed by LanceDB on object storage with RAM/NVMe result caching.

• NextPlaid
  CPU-oriented local-first multivector retrieval engine with memory-mapped storage.

• EMVB
  Reference implementation for Efficient Multi-Vector Dense Retrieval with Bit Vectors.

• IGP
  Official C++ implementation for IGP: proximity-graph indexing for multi-vector retrieval (with Python scripts for experiments).

• WARP
  Official implementation for WARP, an efficient multi-vector retrieval engine.

• ColGrep
  High-performance code search CLI tool powered by LateOn-Code and NextPlaid, enabling semantic + hybrid (regex + semantic) code retrieval locally with incremental indexing.

• TACHIOM
  Fast and scalable multivector retrieval system with Token-Aware Clustering (TAC) and hierarchical Product Quantization for efficient late-interaction search.

• TopK
  Managed retrieval engine with support for late-interaction search over billions of documents, online index updates, filtering, and more.

Scoring Kernels

• Flash-MaxSim
  IO-aware Triton kernel for MaxSim scoring in ColBERT/ColPali pipelines: tile-by-tile on-chip computation with zero intermediate memory and INT8 quantization support.

• maxsim
  Ahead-of-time compiled MaxSim kernel with CUDA and Metal backends (NVIDIA + Apple Silicon), distributed as a HuggingFace kernels package.

• late-interaction-kernels
  Fused Triton kernels for MaxSim scoring with CUDA, Metal, and CPU backends, native PyLate/colpali-engine integration, and PLAID-style compressed-index support.

• maxsim-cpu
  CPU-only MaxSim kernel written in Rust (libxsmm on x86, Apple Accelerate on ARM) with Python bindings.

• TileMaxSim
  IO-aware Triton kernel for MaxSim scoring with dimension tiling for embeddings wider than 128 dims and fused product quantization, achieving 80%+ peak HBM bandwidth.

Model Checkpoints

General-Purpose

• colbert-ir/colbertv2.0
  Official ColBERTv2 checkpoint (MS MARCO-trained) from the ColBERT authors, widely used as the canonical baseline model.

• lightonai/LateOn
  State-of-the-art ColBERT model (149M, ModernBERT-based) achieving 57.22 NDCG@10 on BEIR with fully open training data and strong generalization under decontamination.

• lightonai/mLateOn
  Multilingual ColBERT model (307M, mmBERT-based) covering 9 languages with SOTA multilingual, long-document, and code retrieval; strong zero-shot generalization to unseen languages (57.56 NDCG@10 on BEIR).

• jinaai/jina-colbert-v2
  Multilingual late-interaction retriever (0.6B, JinaBERT-based) supporting 89 languages with Matryoshka token embeddings (128/96/64 dims) for flexible efficiency-precision tradeoffs.

• chungimungi/GLInt
  English ColBERT model (149M, LateOn-based) trained with geometry-matched hard negatives mined via MaxSim geometry, reporting 57.43 NDCG@10 on BEIR.

• lightonai/LateOn-regularized
  LateOn variant trained with STE-based regularization to fix compatibility with projection-based retrieval methods (MUVERA, SMVE).

• lightonai/ColBERT-Zero
  Large-scale fully pre-trained ColBERT checkpoint trained on public data and released with the ColBERT-Zero paper.

• lightonai/GTE-ModernColBERT-v1
  PyLate late-interaction checkpoint based on ModernBERT with 128-dimensional token embeddings and strong long-context retrieval behavior.

• topk-io/Iso-ModernColBERT
  Isotropically corrected version of GTE-ModernColBERT-v1 built for efficient inference and scalable retrieval.

• sebastian-hofstaetter/colberter-128-32-msmarco / sebastian-hofstaetter/uni-colberter-128-1-msmarco
  ColBERTer checkpoints trained on MS MARCO (128-dim, with 32 and 1 unique whole-word vectors per document respectively).

Specialized / Domain

• lightonai/LateOn-Code
  Specialized ColBERT model (149M parameters) fine-tuned for code retrieval, achieving SOTA on MTEB Code benchmark.

• lightonai/LateOn-Code-edge
  Lightweight code retrieval model (17M parameters) for edge devices, matching larger models while running efficiently on CPU.

• lightonai/Reason-ModernColBERT
  Reasoning-focused late-interaction checkpoint fine-tuned on reasonir-hq, with strong BRIGHT benchmark performance for reasoning-intensive retrieval.

• nlpai-lab/KURE-v2
  Korean-English bilingual late-interaction model (154M, skt/A.X-Encoder-base) with 128-dim token vectors and 8,192-token context, reporting 0.8160 average nDCG@10 on MTEB(kor, v2).

• nlpai-lab/KURE-v2-unsupervised
  Stage-1 KURE-v2 checkpoint trained with weakly-supervised contrastive learning only (20.7M pairs, no relevance labels), reaching 0.7283 average nDCG@10 on MTEB(kor, v2).

• DataScience-UIBK/SmallReason-ColBERT-32M
  Ultra-small reasoning retriever (32M, mxbai-edge-colbert-v0-32m-based) with a query-side token-importance head, reporting 21.41 mean nDCG@10 on BRIGHT; load via WeightedColBERT.from_base(), as plain PyLate loading drops the head.

Multimodal & Vision

• vidore/colpali-v1.3
  Latest ColPali release (PaliGemma-3B + LoRA) for visual document retrieval, producing ColBERT-style multi-vector embeddings of page images.

• vidore/colqwen2-v1.0
  ColPali-style visual document retriever on Qwen2-VL-2B-Instruct, accepting dynamic image resolutions without aspect-ratio distortion (up to 768 patches).

• vidore/colqwen2.5-v0.2
  ColPali-style visual document retriever on Qwen2.5-VL-3B-Instruct, with dynamic image resolutions (up to 768 patches).

• vidore/colSmol-256M / vidore/colSmol-500M
  Lightweight ColPali-style visual document retrievers built on SmolVLM-256M-Instruct and SmolVLM-500M-Instruct.

Datasets and Encodings

NFCorpus

• Documents: 3,633
• Queries [test]: 323
• Reference Metric: nDCG@10

SciFact

• Documents: 5,183
• Queries [test]: 300
• Reference Metric: nDCG@10

ArguAna

• Documents: 8,674
• Queries [test]: 1,406
• Reference Metric: nDCG@10

SCIDOCS

• Documents: 25,657
• Queries [test]: 1,000
• Reference Metric: nDCG@10

FiQA-2018

• Documents: 57,638
• Queries [test]: 648
• Reference Metric: nDCG@10

TREC-COVID

• Documents: 171,332
• Queries [test]: 50
• Reference Metric: nDCG@10

Quora

• Documents: 522,931
• Queries [test]: 10,000
• Reference Metric: nDCG@10

LoTTE-pooled

• Documents: 2,428,854
• Queries [dev/search]: 2,931
• Reference Metric: Success@5

MS MARCO v1

• Documents: 8,841,823
• Queries [dev.small]: 6,980
• Reference Metric: MRR@10

ViDoRe v3

• Reference Metric: nDCG@10
• Visual document retrieval: each document is a page image.

Multimedia Resources

• Omar Khattab on Late Interaction in 2030. Link
• Multi-Vector Search with Amélie Chatelain and Antoine Chaffin - Weaviate Podcast #134. Link