Recurrence Meets Transformers for Universal Multimodal Retrieval | Paper Citation Record

Observation a8ca069a-18d3-4f2d-bdde-f7af3d8a7937 · outbound

This paper cites UniIR: Training and Benchmarking Universal Multimodal Information Retrievers,.

Recurrence Meets Transformers for Universal Multimodal Retrieval UniIR: Training and Benchmarking Universal Multimodal Information Retrievers,

Reference 1

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Observation 1792502f-5eb9-4515-87fa-835e6cbb704f · outbound

This paper cites Recurrence-Enhanced Vision-and-Language Transformers for Robust Multimodal Document Retrieval,.

Recurrence Meets Transformers for Universal Multimodal Retrieval Recurrence-Enhanced Vision-and-Language Transformers for Robust Multimodal Document Retrieval,

Reference 2

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Observation d988b9ac-49cb-4cab-ad37-be7dba9ddbf7 · outbound

This paper cites Unsupervised Dense Information Retrieval with Contrastive Learning.

Recurrence Meets Transformers for Universal Multimodal Retrieval Unsupervised Dense Information Retrieval with Contrastive Learning

Reference 3

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Observation 9f52a211-5a86-464f-9de6-8bae366df22a · outbound

This paper cites SIFT meets CNN: A decade survey of instance retrieval,.

Recurrence Meets Transformers for Universal Multimodal Retrieval SIFT meets CNN: A decade survey of instance retrieval,

Reference 4

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Observation 4027c289-8b90-4620-8c5e-e6a302f4a09a · outbound

This paper cites Large-scale image retrieval with attentive deep local features,.

Recurrence Meets Transformers for Universal Multimodal Retrieval Large-scale image retrieval with attentive deep local features,

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-04T20:06:09.004839Z

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Observation 3bcd3642-679d-4a9c-a57a-3db334b70d0e · outbound

This paper cites Microsoft COCO: Common Objects in Context,.

Recurrence Meets Transformers for Universal Multimodal Retrieval Microsoft COCO: Common Objects in Context,

Reference 6

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Observation c6229284-f665-4841-a8de-c53b52abc0bc · outbound

This paper cites Conceptual Captions: A Cleaned, Hypernymed, Image Alt-text Dataset For Automatic Image Captioning,.

Recurrence Meets Transformers for Universal Multimodal Retrieval Conceptual Captions: A Cleaned, Hypernymed, Image Alt-text Dataset For Automatic Image Captioning,

Reference 7

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Observation 9c6b7e85-f9ea-489c-b3d6-381ce3ebfb15 · outbound

This paper cites LAION-5B: An open large-scale dataset for training next generation image-text models,.

Recurrence Meets Transformers for Universal Multimodal Retrieval LAION-5B: An open large-scale dataset for training next generation image-text models,

Reference 8

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Observation 52034a3c-ed93-4ad3-b8ff-0d46d790feb4 · outbound

This paper cites Learning Transferable Visual Models From Natural Language Super- vision,.

Recurrence Meets Transformers for Universal Multimodal Retrieval Learning Transferable Visual Models From Natural Language Super- vision,

Reference 9

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Observation bcc40c18-7530-4917-a83a-428a3dd8e3aa · outbound

This paper cites Scaling up visual and vision-language representation learning with noisy text supervision,.

Recurrence Meets Transformers for Universal Multimodal Retrieval Scaling up visual and vision-language representation learning with noisy text supervision,

Reference 10

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Observation 582e9aad-f2a4-4d20-b248-f15f3818faf2 · outbound

This paper cites Reproducible scaling laws for contrastive language-image Learning,.

Recurrence Meets Transformers for Universal Multimodal Retrieval Reproducible scaling laws for contrastive language-image Learning,

Reference 11

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Observation b68572ae-dfbe-441f-b4fa-60882edb52e3 · outbound

This paper cites Sigmoid Loss for Language Image Pre-Training,.

Recurrence Meets Transformers for Universal Multimodal Retrieval Sigmoid Loss for Language Image Pre-Training,

Reference 12

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Observation 20c5bc2a-2a8d-4ee2-b109-de938fac207d · outbound

This paper cites SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features.

Recurrence Meets Transformers for Universal Multimodal Retrieval SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features

Reference 13

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Observation 73f81a17-c6d3-4a28-91d2-658463a6484e · outbound

This paper cites The Revolution of Multimodal Large Language Models: A Survey,.

Recurrence Meets Transformers for Universal Multimodal Retrieval The Revolution of Multimodal Large Language Models: A Survey,

Reference 14

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Observation a126f73a-3fb9-487d-9bb9-5a99c99a4093 · outbound

This paper cites Improved Baselines with Visual Instruction Tuning,.

Recurrence Meets Transformers for Universal Multimodal Retrieval Improved Baselines with Visual Instruction Tuning,

Reference 15

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Observation 8e9a095f-918e-449f-a442-2f29cfd86a5e · outbound

This paper cites LLaV A-MORE: A Comparative Study of LLMs and Visual Backbones for Enhanced Visual Instruction Tuning,.

Recurrence Meets Transformers for Universal Multimodal Retrieval LLaV A-MORE: A Comparative Study of LLMs and Visual Backbones for Enhanced Visual Instruction Tuning,

Reference 16

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Observation 5bdd6a16-d827-4f17-80e0-f86f9a32aa82 · outbound

This paper cites Qwen2.5-VL Technical Report.

Recurrence Meets Transformers for Universal Multimodal Retrieval Qwen2.5-VL Technical Report

Reference 17

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Observation b322d1b4-7979-4ddc-a319-2655e934f2be · outbound

This paper cites PreFLMR: Scaling Up Fine- Grained Late-Interaction Multi-modal Retrievers,.

Recurrence Meets Transformers for Universal Multimodal Retrieval PreFLMR: Scaling Up Fine- Grained Late-Interaction Multi-modal Retrievers,

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-04T20:06:09.039420Z

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Observation b593bbeb-fd40-4846-a029-dc65e6e78011 · outbound

This paper cites Can Pre-trained Vision and Language Models Answer Visual Information-Seeking Questions?.

Recurrence Meets Transformers for Universal Multimodal Retrieval Can Pre-trained Vision and Language Models Answer Visual Information-Seeking Questions?

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-04T20:06:09.041750Z

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Observation 331810db-6cb3-4ab9-9e6f-28a98cdfe718 · outbound

This paper cites Encyclopedic VQA: Visual questions about detailed properties of fine-grained categories,.

Recurrence Meets Transformers for Universal Multimodal Retrieval Encyclopedic VQA: Visual questions about detailed properties of fine-grained categories,

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-04T20:06:09.044289Z

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Observation d1f78b14-bd6b-433d-a01e-279da814e1f9 · outbound

This paper cites Long Short-Term Memory,.

Recurrence Meets Transformers for Universal Multimodal Retrieval Long Short-Term Memory,

Reference 21

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Observation 7d6f6d1d-d359-4f7c-a9b7-b837ce3adae3 · outbound

This paper cites Open-domain Visual Entity Recog- nition: Towards Recognizing Millions of Wikipedia Entities,.

Recurrence Meets Transformers for Universal Multimodal Retrieval Open-domain Visual Entity Recog- nition: Towards Recognizing Millions of Wikipedia Entities,

Reference 22

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Observation 1a048ac7-65df-49ee-948d-709bc298ce4d · outbound

This paper cites OK-VQA: A Visual Question Answering Benchmark Requiring External Knowledge,.

Recurrence Meets Transformers for Universal Multimodal Retrieval OK-VQA: A Visual Question Answering Benchmark Requiring External Knowledge,

Reference 23

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Observation 91419f0c-a081-47d8-a75b-c9473fdbcc9b · outbound

This paper cites EVA-CLIP: Improved Training Techniques for CLIP at Scale.

Recurrence Meets Transformers for Universal Multimodal Retrieval EVA-CLIP: Improved Training Techniques for CLIP at Scale

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-04T20:06:09.054038Z

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Observation 950f727c-63a1-4901-9867-b64af77b98f7 · outbound

This paper cites Flickr30k Entities: Collecting Region-to-Phrase Correspondences for Richer Image-to-Sentence Models,.

Recurrence Meets Transformers for Universal Multimodal Retrieval Flickr30k Entities: Collecting Region-to-Phrase Correspondences for Richer Image-to-Sentence Models,

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-04T20:06:09.056915Z

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Observation ed794539-b2af-427d-8e46-55940d9d77e1 · outbound

This paper cites Image Retrieval on Real-Life Images With Pre-Trained Vision-and-Language Models,.

Recurrence Meets Transformers for Universal Multimodal Retrieval Image Retrieval on Real-Life Images With Pre-Trained Vision-and-Language Models,

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-04T20:06:09.059565Z

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Observation 5d1b67ac-a3ea-4d46-8727-c550582c3394 · outbound

This paper cites Long-CLIP: Unlocking the Long-Text Capability of CLIP,.

Recurrence Meets Transformers for Universal Multimodal Retrieval Long-CLIP: Unlocking the Long-Text Capability of CLIP,

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-04T20:06:09.062053Z

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Observation b273f406-2d24-4385-a4b2-1b714b2a8435 · outbound

This paper cites Thinking Fast and Slow: Efficient Text-to-Visual Retrieval With Transformers,.

Recurrence Meets Transformers for Universal Multimodal Retrieval Thinking Fast and Slow: Efficient Text-to-Visual Retrieval With Transformers,

Reference 28

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Observation 4e0da6de-bfea-4d8a-9317-224bae26ae74 · outbound

This paper cites Smooth-AP: Smoothing the Path Towards Large-Scale Image Retrieval,.

Recurrence Meets Transformers for Universal Multimodal Retrieval Smooth-AP: Smoothing the Path Towards Large-Scale Image Retrieval,

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-04T20:06:09.066886Z

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Observation 2861b53c-a93a-469a-8a3b-8398139d3926 · outbound

This paper cites Conditioned and Composed Image Retrieval Combining and Partially Fine-Tuning CLIP-Based Features,.

Recurrence Meets Transformers for Universal Multimodal Retrieval Conditioned and Composed Image Retrieval Combining and Partially Fine-Tuning CLIP-Based Features,

Reference 30

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Observation c586d371-e594-4e4a-8495-2341acf003ff · outbound

This paper cites BLIP: Bootstrapping Language- Image Pre-training for Unified Vision-Language Understanding and Generation,.

Recurrence Meets Transformers for Universal Multimodal Retrieval BLIP: Bootstrapping Language- Image Pre-training for Unified Vision-Language Understanding and Generation,

Reference 31

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Observation 67842716-390b-4f85-8040-c282f7d21289 · outbound

This paper cites GENIUS: A Generative Framework for Universal Multimodal Search,.

Recurrence Meets Transformers for Universal Multimodal Retrieval GENIUS: A Generative Framework for Universal Multimodal Search,

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-04T20:06:09.074201Z

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Observation d76ecf55-2ab5-4a1b-a724-74536f0cabbb · outbound

This paper cites Fine-grained Late- interaction Multi-modal Retrieval for Retrieval Augmented Visual Ques- tion Answering,.

Recurrence Meets Transformers for Universal Multimodal Retrieval Fine-grained Late- interaction Multi-modal Retrieval for Retrieval Augmented Visual Ques- tion Answering,

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-04T20:06:09.076615Z

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Observation 854b0c7c-1671-4879-9d49-2ddb5b578578 · outbound

This paper cites ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT,.

Recurrence Meets Transformers for Universal Multimodal Retrieval ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT,

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-04T20:06:09.078945Z

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Observation ca31e2cc-583e-4813-b8c2-6bd7e9ec1f37 · outbound

This paper cites Language models are few-shot learners,.

Recurrence Meets Transformers for Universal Multimodal Retrieval Language models are few-shot learners,

Reference 35

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Observation 54201e63-26b7-488c-a608-6e3c4b8df992 · outbound

This paper cites The Llama 3 Herd of Models.

Recurrence Meets Transformers for Universal Multimodal Retrieval The Llama 3 Herd of Models

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-04T20:06:09.083759Z

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Observation dee484a4-50ec-4804-97e7-5fab419e8214 · outbound

This paper cites LamRA: Large Multimodal Model as Your Advanced Retrieval Assistant,.

Recurrence Meets Transformers for Universal Multimodal Retrieval LamRA: Large Multimodal Model as Your Advanced Retrieval Assistant,

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-04T20:06:09.086532Z

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Observation 98ef7cc0-2c0e-421c-82d9-a709493efacd · outbound

This paper cites MM-Embed: Universal Multimodal Retrieval with Multimodal LLMs,.

Recurrence Meets Transformers for Universal Multimodal Retrieval MM-Embed: Universal Multimodal Retrieval with Multimodal LLMs,

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-04T20:06:09.089128Z

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Observation c6fa9f74-f7c8-46f9-ac62-20150874a583 · outbound

This paper cites PUMA: Layer- Pruned Language Model for Efficient Unified Multimodal Retrieval with Modality-Adaptive Learning,.

Recurrence Meets Transformers for Universal Multimodal Retrieval PUMA: Layer- Pruned Language Model for Efficient Unified Multimodal Retrieval with Modality-Adaptive Learning,

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-04T20:06:09.091490Z

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Observation a0c256f8-118b-46ad-8321-8af2be4a9b0c · outbound

This paper cites Joint Fusion and Encoding: Advancing Multimodal Retrieval from the Ground Up.

Recurrence Meets Transformers for Universal Multimodal Retrieval Joint Fusion and Encoding: Advancing Multimodal Retrieval from the Ground Up

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-04T20:06:09.094045Z

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Observation 8045a789-af75-4125-af60-5087d9a00c38 · outbound

This paper cites Attention Is All You Need,.

Recurrence Meets Transformers for Universal Multimodal Retrieval Attention Is All You Need,

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-04T20:06:09.096789Z

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Observation 6b72423c-abb8-47e8-8258-f3dd2b7b2bb0 · outbound

This paper cites Transformers: State- of-the-Art Natural Language Processing,.

Recurrence Meets Transformers for Universal Multimodal Retrieval Transformers: State- of-the-Art Natural Language Processing,

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-04T20:06:09.099531Z

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Observation 7dde63b2-f6c1-4adf-a628-cf4eacd7ff5f · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

Recurrence Meets Transformers for Universal Multimodal Retrieval LLaMA: Open and Efficient Foundation Language Models

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-04T20:06:09.101907Z

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Observation 2ffab2c6-b60d-462a-b859-9b3d819acbe1 · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale,.

Recurrence Meets Transformers for Universal Multimodal Retrieval An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale,

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-04T20:06:09.105110Z

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Observation 1aac34ea-134c-4dbe-9b3d-69b3ab4308ba · outbound

This paper cites Training data-efficient image transformers & distillation through attention,.

Recurrence Meets Transformers for Universal Multimodal Retrieval Training data-efficient image transformers & distillation through attention,

Reference 45

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Observation c43b9f68-3914-4ed1-9cd5-9115b16912ea · outbound

This paper cites Scaling Vision Transformers,.

Recurrence Meets Transformers for Universal Multimodal Retrieval Scaling Vision Transformers,

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-04T20:06:09.110276Z

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Observation f26dd060-5577-4bd0-ae7b-64ca67bee48f · outbound

This paper cites Transformers in Vision: A Survey,.

Recurrence Meets Transformers for Universal Multimodal Retrieval Transformers in Vision: A Survey,

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-04T20:06:09.112765Z

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Observation 58536b43-ec6f-4ba1-8eba-aa751a0e78ba · outbound

This paper cites When attention meets fast recurrence: Training language models with reduced compute,.

Recurrence Meets Transformers for Universal Multimodal Retrieval When attention meets fast recurrence: Training language models with reduced compute,

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-04T20:06:09.115088Z

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Observation 856e754f-dd00-483e-baa9-45b11ab4d7d7 · outbound

This paper cites Simple recurrent units for highly parallelizable recurrence,.

Recurrence Meets Transformers for Universal Multimodal Retrieval Simple recurrent units for highly parallelizable recurrence,

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-04T20:06:09.117483Z

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Observation ef0b1f1f-69ff-4c99-bd98-0b5456574a66 · outbound

This paper cites The Best of Both Worlds: Combining Recent Advances in Neural Machine Translation,.

Recurrence Meets Transformers for Universal Multimodal Retrieval The Best of Both Worlds: Combining Recent Advances in Neural Machine Translation,

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-04T20:06:09.119939Z

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Observation ee4debea-d943-4f43-afac-92874c33d6ba · outbound

This paper cites R-Transformer: Recurrent Neural Network Enhanced Transformer.

Recurrence Meets Transformers for Universal Multimodal Retrieval R-Transformer: Recurrent Neural Network Enhanced Transformer

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-04T20:06:09.122432Z

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Observation ace8f110-196c-44a2-bd84-36548fc19836 · outbound

This paper cites Block- Recurrent Transformers,.

Recurrence Meets Transformers for Universal Multimodal Retrieval Block- Recurrent Transformers,

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-04T20:06:09.125375Z

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Observation c4ba4dda-e624-4987-b6e9-451b7b691555 · outbound

This paper cites ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interaction,.

Recurrence Meets Transformers for Universal Multimodal Retrieval ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interaction,

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-04T20:06:09.127859Z

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Observation 2eaf6806-7ed9-43a5-bf4d-23dbb1ae284b · outbound

This paper cites Lambda-Skip Connections: the Architectural Component that Prevents Rank Collapse,.

Recurrence Meets Transformers for Universal Multimodal Retrieval Lambda-Skip Connections: the Architectural Component that Prevents Rank Collapse,

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-04T20:06:09.130438Z

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Observation eec145b5-2140-498b-8557-18373cbf9f90 · outbound

This paper cites Layer Normalization.

Recurrence Meets Transformers for Universal Multimodal Retrieval Layer Normalization

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-04T20:06:09.132854Z

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Observation 4a6ee3a3-927f-4a83-8e17-90a6c7aa00ea · outbound

This paper cites WIT: Wikipedia-based Image Text Dataset for Multimodal Multilingual Machine Learning,.

Recurrence Meets Transformers for Universal Multimodal Retrieval WIT: Wikipedia-based Image Text Dataset for Multimodal Multilingual Machine Learning,

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-04T20:06:09.135440Z

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Observation 1c34b95a-4a30-4a74-a8c3-80eba7e986e5 · outbound

This paper cites IGLUE: A Benchmark for Transfer Learning Across Modalities, Tasks, and Languages,.

Recurrence Meets Transformers for Universal Multimodal Retrieval IGLUE: A Benchmark for Transfer Learning Across Modalities, Tasks, and Languages,

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-04T20:06:09.137956Z

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Observation 65d7283c-b9be-4510-b79e-69cdabb5db47 · outbound

This paper cites KVQA: Knowledge- Aware Visual Question Answering,.

Recurrence Meets Transformers for Universal Multimodal Retrieval KVQA: Knowledge- Aware Visual Question Answering,

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-04T20:06:09.140358Z

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Observation 5d43dffc-4a4a-4ba7-8a8e-57c57a64f08c · outbound

This paper cites MS MARCO: A Human Generated MAchine Reading COmprehension Dataset,.

Recurrence Meets Transformers for Universal Multimodal Retrieval MS MARCO: A Human Generated MAchine Reading COmprehension Dataset,

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-04T20:06:09.142678Z

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Observation c8383d7b-6ff1-4845-9e57-568168d14655 · outbound

This paper cites Visual Instruction Tuning,.

Recurrence Meets Transformers for Universal Multimodal Retrieval Visual Instruction Tuning,

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-04T20:06:09.145450Z

Source-reported events for the cited work

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Observation 635b5415-afb3-4dce-9e2b-8540313b3e6a · outbound

This paper cites EDIS: Entity- Driven Image Search over Multimodal Web Content,.

Recurrence Meets Transformers for Universal Multimodal Retrieval EDIS: Entity- Driven Image Search over Multimodal Web Content,

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-04T20:06:09.147996Z

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Observation 71e30fde-2f50-40c1-ac9e-1ccd0e201500 · outbound

This paper cites Fashion IQ: A New Dataset Towards Retrieving Images by Natural Language Feedback,.

Recurrence Meets Transformers for Universal Multimodal Retrieval Fashion IQ: A New Dataset Towards Retrieving Images by Natural Language Feedback,

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-04T20:06:09.150370Z

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Observation 7b94b925-1f30-4816-9ccd-b347f679f5a6 · outbound

This paper cites Automatic Spatially-Aware Fashion Concept Discovery,.

Recurrence Meets Transformers for Universal Multimodal Retrieval Automatic Spatially-Aware Fashion Concept Discovery,

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-04T20:06:09.152908Z

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Observation b1318d12-c2cf-44ba-861b-07f92d83509b · outbound

This paper cites Visual News: Benchmark and Challenges in News Image Captioning,.

Recurrence Meets Transformers for Universal Multimodal Retrieval Visual News: Benchmark and Challenges in News Image Captioning,

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-04T20:06:09.155312Z

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Observation 65ec9ad0-dfc7-4bcf-ae02-3a1eb54ba887 · outbound

This paper cites DreamSim: Learning New Dimensions of Human Visual Similarity using Synthetic Data,.

Recurrence Meets Transformers for Universal Multimodal Retrieval DreamSim: Learning New Dimensions of Human Visual Similarity using Synthetic Data,

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-04T20:06:09.157984Z

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Observation b6ad4bbe-f9a0-4346-a4ee-8ea6a3abf05f · outbound

This paper cites ADAM: a Method for Stochastic Optimiza- tion,.

Recurrence Meets Transformers for Universal Multimodal Retrieval ADAM: a Method for Stochastic Optimiza- tion,

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-04T20:06:09.160539Z

Source-reported events for the cited work

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Observation 55204586-01e9-45d9-a426-4000ae0ad663 · outbound

This paper cites Billion-Scale Similarity Search with GPUs,.

Recurrence Meets Transformers for Universal Multimodal Retrieval Billion-Scale Similarity Search with GPUs,

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-04T20:06:09.162988Z

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Observation abf2788a-b98e-49be-9b54-5802401b71c7 · outbound

This paper cites Multi- Layer Visual Feature Fusion in Multimodal LLMs: Methods, Analysis, and Best Practices,.

Recurrence Meets Transformers for Universal Multimodal Retrieval Multi- Layer Visual Feature Fusion in Multimodal LLMs: Methods, Analysis, and Best Practices,

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-04T20:06:09.165771Z

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Observation e120b265-e3e4-4676-a724-883245c14fe4 · outbound

This paper cites Wiki-LLaV A: Hierarchical Retrieval-Augmented Generation for Multimodal LLMs,.

Recurrence Meets Transformers for Universal Multimodal Retrieval Wiki-LLaV A: Hierarchical Retrieval-Augmented Generation for Multimodal LLMs,

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-04T20:06:09.168123Z

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Observation 5e67fdea-71c0-43f0-96f4-cdbef572032b · outbound

This paper cites Tomayto, Tomahto. Beyond Token-level Answer Equivalence for Ques- tion Answering Evaluation,.

Recurrence Meets Transformers for Universal Multimodal Retrieval Tomayto, Tomahto. Beyond Token-level Answer Equivalence for Ques- tion Answering Evaluation,

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-04T20:06:09.170486Z

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Observation 19c4cf41-1285-4542-967d-ddbd300ddf19 · outbound

This paper cites Aug- menting Multimodal LLMs with Self-Reflective Tokens for Knowledge- based Visual Question Answering,.

Recurrence Meets Transformers for Universal Multimodal Retrieval Aug- menting Multimodal LLMs with Self-Reflective Tokens for Knowledge- based Visual Question Answering,

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-04T20:06:09.172726Z

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Observation ada187f9-6ea1-4a7e-8091-d71603d7fb3d · outbound

This paper cites RoRA-VLM: Robust Retrieval-Augmented Vision Language Models.

Recurrence Meets Transformers for Universal Multimodal Retrieval RoRA-VLM: Robust Retrieval-Augmented Vision Language Models

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-04T20:06:09.175173Z

Source-reported events for the cited work

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Observation ccaa28e4-9ba2-4657-b4da-72f4b444d3bd · outbound

This paper cites EchoSight: Advancing Visual-Language Models with Wiki Knowledge,.

Recurrence Meets Transformers for Universal Multimodal Retrieval EchoSight: Advancing Visual-Language Models with Wiki Knowledge,

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-04T20:06:09.177829Z

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Observation d2222c44-7745-4fec-99f5-5a4fc3d02b47 · outbound

This paper cites Towards General Continuous Memory for Vision-Language Models.

Recurrence Meets Transformers for Universal Multimodal Retrieval Towards General Continuous Memory for Vision-Language Models

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-04T20:06:09.180245Z

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Observation c3f50af2-666a-4c96-9b4f-03dc62a0dc58 · outbound

This paper cites mR$^2$AG: Multimodal Retrieval-Reflection-Augmented Generation for Knowledge-Based VQA.

Recurrence Meets Transformers for Universal Multimodal Retrieval mR$^2$AG: Multimodal Retrieval-Reflection-Augmented Generation for Knowledge-Based VQA

Reference 75

Resolution: unresolved; no resolver link, observed 2026-08-04T20:06:09.182830Z

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Observation d15fa04c-1442-4229-8cb8-15a2dbf51316 · outbound

This paper cites BLIP-2: Bootstrapping Language- Image Pre-training with Frozen Image Encoders and Large Language Models,.

Recurrence Meets Transformers for Universal Multimodal Retrieval BLIP-2: Bootstrapping Language- Image Pre-training with Frozen Image Encoders and Large Language Models,

Reference 76

Resolution: unresolved; no resolver link, observed 2026-08-04T20:06:09.185613Z

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Observation 8becf586-4688-4168-bb6c-96adbf4464fb · outbound

This paper cites InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning,.

Recurrence Meets Transformers for Universal Multimodal Retrieval InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning,

Reference 77

Resolution: unresolved; no resolver link, observed 2026-08-04T20:06:09.188123Z

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Observation 0e253ce2-8f5e-42ec-a572-d779bd89a12c · outbound

This paper cites WebQA: Multihop and Multimodal QA,.

Recurrence Meets Transformers for Universal Multimodal Retrieval WebQA: Multihop and Multimodal QA,

Reference 78

Resolution: unresolved; no resolver link, observed 2026-08-04T20:06:09.190546Z

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Observation ff4ca8b7-b9c0-4d9c-a4d1-7e79e43f8e12 · outbound

This paper cites The Eleven.

Recurrence Meets Transformers for Universal Multimodal Retrieval The Eleven

Reference 1992

Resolution: unresolved; no resolver link, observed 2026-08-04T20:06:09.193095Z

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Observation 92eb5617-04a8-47f6-ab27-745cf85812f7 · outbound

This paper cites Eu- phorbia pulcherrima.

Recurrence Meets Transformers for Universal Multimodal Retrieval Eu- phorbia pulcherrima

Reference 2016

Resolution: unresolved; no resolver link, observed 2026-08-04T20:06:09.196053Z

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