Paper Citation Record · LEDGER

2.5 Years in Class: A Multimodal Textbook for Vision-Language Pretraining

As of 22 August 2026, this Paper Citation Record lists 65 of 65 outbound references and 3 inbound Pith citation observations for arXiv:2501.00958.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2501.00958 v4

Coverage vector

measured 65 of 65 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T22:41:33.683491Z

measured 68 of 68 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-16T15:27:04.228144Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-05-16T15:27:04.347986Z

Reference resolution

65 of 65 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 19a81898-c503-4dc9-b66c-74b2259c93a4 · outbound

This paper cites Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone.

2.5 Years in Class: A Multimodal Textbook for Vision-Language Pretraining Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone

Reference 1

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Observation 5d468dde-5869-4eb3-bf8b-6d5243185b2a · outbound

This paper cites Phi-4 Technical Report.

2.5 Years in Class: A Multimodal Textbook for Vision-Language Pretraining Phi-4 Technical Report

Reference 2

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Observation d1b21053-f891-4028-9c21-1c167550c89f · outbound

This paper cites GPT-4 Technical Report.

2.5 Years in Class: A Multimodal Textbook for Vision-Language Pretraining GPT-4 Technical Report

Reference 3

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Observation 70e7d395-7f3f-44d5-b60a-900eb5e1c84b · outbound

This paper cites Flamingo: a visual language model for few-shot learning.

2.5 Years in Class: A Multimodal Textbook for Vision-Language Pretraining Flamingo: a visual language model for few-shot learning

Reference 4

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Observation b4ba0d46-c611-4779-94ff-6deaecfd7e9f · outbound

This paper cites OpenFlamingo: An Open-Source Framework for Training Large Autoregressive Vision-Language Models.

2.5 Years in Class: A Multimodal Textbook for Vision-Language Pretraining OpenFlamingo: An Open-Source Framework for Training Large Autoregressive Vision-Language Models

Reference 5

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Observation 6da73a83-1a9c-4f35-8ccd-c6d10f6daf40 · outbound

This paper cites MINT-1T: Scaling Open-Source Multimodal Data by 10x: A Multimodal Dataset with One Trillion Tokens.

2.5 Years in Class: A Multimodal Textbook for Vision-Language Pretraining MINT-1T: Scaling Open-Source Multimodal Data by 10x: A Multimodal Dataset with One Trillion Tokens

Reference 6

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Observation d5cfa623-c584-4114-977b-0878e5c3958d · outbound

This paper cites Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond.

2.5 Years in Class: A Multimodal Textbook for Vision-Language Pretraining Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond

Reference 7

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Observation 63a009ad-b5fc-4f62-b699-44e810313bc0 · outbound

This paper cites Coyo-700m: Image-text pair dataset.

2.5 Years in Class: A Multimodal Textbook for Vision-Language Pretraining Coyo-700m: Image-text pair dataset

Reference 8

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Observation 27405bc8-1e8b-4909-8ce9-6709ea4a9fa5 · outbound

This paper cites CoMM: A Coherent Interleaved Image-Text Dataset for Multimodal Understanding and Generation.

2.5 Years in Class: A Multimodal Textbook for Vision-Language Pretraining CoMM: A Coherent Interleaved Image-Text Dataset for Multimodal Understanding and Generation

Reference 9

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Observation abc1fe38-9012-4cf2-a9ab-826fe649d5d7 · outbound

This paper cites InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks.

2.5 Years in Class: A Multimodal Textbook for Vision-Language Pretraining InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks

Reference 10

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Observation 305a66cc-66bb-4c0f-98c6-b63c226491ae · outbound

This paper cites How Far Are We to GPT-4V? Closing the Gap to Commercial Multimodal Models with Open-Source Suites.

2.5 Years in Class: A Multimodal Textbook for Vision-Language Pretraining How Far Are We to GPT-4V? Closing the Gap to Commercial Multimodal Models with Open-Source Suites

Reference 11

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Observation 65f0a421-4009-4948-907f-b4dabf5d67c3 · outbound

This paper cites VideoLLaMA 2: Advancing Spatial-Temporal Modeling and Audio Understanding in Video-LLMs.

2.5 Years in Class: A Multimodal Textbook for Vision-Language Pretraining VideoLLaMA 2: Advancing Spatial-Temporal Modeling and Audio Understanding in Video-LLMs

Reference 12

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Observation 7417e222-25cc-4ed3-a0a5-430725dcbf4c · outbound

This paper cites Infinity-MM: Scaling Multimodal Performance with Large-Scale and High-Quality Instruction Data.

2.5 Years in Class: A Multimodal Textbook for Vision-Language Pretraining Infinity-MM: Scaling Multimodal Performance with Large-Scale and High-Quality Instruction Data

Reference 13

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Observation cb38d3f1-76b2-4abc-9760-f168676a69de · outbound

This paper cites Textbooks Are All You Need.

2.5 Years in Class: A Multimodal Textbook for Vision-Language Pretraining Textbooks Are All You Need

Reference 14

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Observation ea6ee0ee-fb3f-409d-b746-f4e57e05b61f · outbound

This paper cites Video-MMMU: Evaluating Knowledge Acquisition from Multi-Discipline Professional Videos.

2.5 Years in Class: A Multimodal Textbook for Vision-Language Pretraining Video-MMMU: Evaluating Knowledge Acquisition from Multi-Discipline Professional Videos

Reference 15

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Observation e84cd31b-bb6e-491f-acc8-75453cb690aa · outbound

This paper cites Language is not all you need: Aligning perception with language mod- els.

2.5 Years in Class: A Multimodal Textbook for Vision-Language Pretraining Language is not all you need: Aligning perception with language mod- els

Reference 16

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Observation 3c082ae1-28ad-4531-9aaf-9217b1f59e70 · outbound

This paper cites Phi-2: The surprising power of small language models.

2.5 Years in Class: A Multimodal Textbook for Vision-Language Pretraining Phi-2: The surprising power of small language models

Reference 17

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Observation 796e83b5-d867-4a60-93dd-1c144f0fab90 · outbound

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

2.5 Years in Class: A Multimodal Textbook for Vision-Language Pretraining Scaling up visual and vision-language representa- tion learning with noisy text supervision

Reference 18

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Observation 1ef98b67-3213-4d26-8d38-47b74fb39781 · outbound

This paper cites MANTIS: Interleaved Multi-Image Instruction Tuning.

2.5 Years in Class: A Multimodal Textbook for Vision-Language Pretraining MANTIS: Interleaved Multi-Image Instruction Tuning

Reference 19

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Observation 4662635b-f70f-440c-bc44-1957f06b8306 · outbound

This paper cites Building and better understanding vision-language models: insights and future directions.

2.5 Years in Class: A Multimodal Textbook for Vision-Language Pretraining Building and better understanding vision-language models: insights and future directions

Reference 20

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Observation 4f70afbb-80fb-401c-b613-9d37cd13c1db · outbound

This paper cites What matters when building vision-language models?.

2.5 Years in Class: A Multimodal Textbook for Vision-Language Pretraining What matters when building vision-language models?

Reference 21

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Observation 0fe4f996-0c70-4d58-b9c0-8393bc5eaab2 · outbound

This paper cites Rush, Douwe Kiela, Matthieu Cord, and Victor Sanh.

2.5 Years in Class: A Multimodal Textbook for Vision-Language Pretraining Rush, Douwe Kiela, Matthieu Cord, and Victor Sanh

Reference 22

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Observation 78360fe2-975a-4e0a-a6f7-72197a3a3d47 · outbound

This paper cites Unicoder-vl: A universal encoder for vision and language by cross-modal pre-training.

2.5 Years in Class: A Multimodal Textbook for Vision-Language Pretraining Unicoder-vl: A universal encoder for vision and language by cross-modal pre-training

Reference 23

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Observation e6e3b362-b6b0-4b4d-99be-20510be8ac04 · outbound

This paper cites Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models.

2.5 Years in Class: A Multimodal Textbook for Vision-Language Pretraining Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models

Reference 24

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Observation ac0ba78b-5f72-4496-89f4-a62cafe8728f · outbound

This paper cites OmniCorpus: A Unified Multimodal Corpus of 10 Billion-Level Images Interleaved with Text.

2.5 Years in Class: A Multimodal Textbook for Vision-Language Pretraining OmniCorpus: A Unified Multimodal Corpus of 10 Billion-Level Images Interleaved with Text

Reference 25

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Observation 63661e11-afca-46a1-be82-c457fa8f58d7 · outbound

This paper cites Textbooks Are All You Need II: phi-1.5 technical report.

2.5 Years in Class: A Multimodal Textbook for Vision-Language Pretraining Textbooks Are All You Need II: phi-1.5 technical report

Reference 26

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Observation 1a1f334b-6289-441d-b46a-ea7f2305610f · outbound

This paper cites Towards General Text Embeddings with Multi-stage Contrastive Learning.

2.5 Years in Class: A Multimodal Textbook for Vision-Language Pretraining Towards General Text Embeddings with Multi-stage Contrastive Learning

Reference 27

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Observation 166456da-11b0-4c38-8319-91d3bab432f0 · outbound

This paper cites Vila: On pre-training for visual language models, 2023.

2.5 Years in Class: A Multimodal Textbook for Vision-Language Pretraining Vila: On pre-training for visual language models, 2023

Reference 28

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Observation 53517bd6-ca20-41c7-a2d1-d9d3fada8173 · outbound

This paper cites Improved baselines with visual instruction tuning, 2023.

2.5 Years in Class: A Multimodal Textbook for Vision-Language Pretraining Improved baselines with visual instruction tuning, 2023

Reference 29

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2.5 Years in Class: A Multimodal Textbook for Vision-Language Pretraining Visual instruction tuning, 2023

Reference 30

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Observation da4c6b75-3f40-4f4a-8471-3b20b200386d · outbound

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2.5 Years in Class: A Multimodal Textbook for Vision-Language Pretraining Improved baselines with visual instruction tuning

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2.5 Years in Class: A Multimodal Textbook for Vision-Language Pretraining Visual instruction tuning

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Observation d3b828b9-517c-4837-9239-0b8ee1b52986 · outbound

This paper cites DeepSeek-VL: Towards Real-World Vision-Language Understanding.

2.5 Years in Class: A Multimodal Textbook for Vision-Language Pretraining DeepSeek-VL: Towards Real-World Vision-Language Understanding

Reference 33

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Observation 2c5fd610-f5d1-4690-bab9-f151740d2b5d · outbound

This paper cites Learn to explain: Multimodal reasoning via thought chains for science question answering.

2.5 Years in Class: A Multimodal Textbook for Vision-Language Pretraining Learn to explain: Multimodal reasoning via thought chains for science question answering

Reference 34

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2.5 Years in Class: A Multimodal Textbook for Vision-Language Pretraining Ok-vqa: A visual question answering benchmark requiring external knowledge

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2.5 Years in Class: A Multimodal Textbook for Vision-Language Pretraining MM1: Methods, Analysis & Insights from Multimodal LLM Pre-training

Reference 36

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This paper cites Howto100m: Learning a text-video embedding by watching hundred million narrated video clips.

2.5 Years in Class: A Multimodal Textbook for Vision-Language Pretraining Howto100m: Learning a text-video embedding by watching hundred million narrated video clips

Reference 37

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This paper cites True few- shot learning with language models.

2.5 Years in Class: A Multimodal Textbook for Vision-Language Pretraining True few- shot learning with language models

Reference 38

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Observation 136864f4-bcbf-4e70-8305-93e908a04fe7 · outbound

This paper cites Learning transferable visual models from natural language supervi- sion.

2.5 Years in Class: A Multimodal Textbook for Vision-Language Pretraining Learning transferable visual models from natural language supervi- sion

Reference 39

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Observation 7b8dd9a1-41b7-46e7-ac8d-34b2652dea38 · outbound

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2.5 Years in Class: A Multimodal Textbook for Vision-Language Pretraining How2: A Large-scale Dataset for Multimodal Language Understanding

Reference 40

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This paper cites LAION-400M: Open Dataset of CLIP-Filtered 400 Million Image-Text Pairs.

2.5 Years in Class: A Multimodal Textbook for Vision-Language Pretraining LAION-400M: Open Dataset of CLIP-Filtered 400 Million Image-Text Pairs

Reference 41

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2.5 Years in Class: A Multimodal Textbook for Vision-Language Pretraining Laion-5b: An open large-scale dataset for training next generation image-text models

Reference 42

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Observation cfb94201-92a2-4bc9-b51b-4bb9643bf3c9 · outbound

This paper cites Towards vqa models that can read.

2.5 Years in Class: A Multimodal Textbook for Vision-Language Pretraining Towards vqa models that can read

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This paper cites Generative multimodal mod- els are in-context learners.

2.5 Years in Class: A Multimodal Textbook for Vision-Language Pretraining Generative multimodal mod- els are in-context learners

Reference 44

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Observation a4bf3cd4-a804-47e7-8021-08b2e75e73b2 · outbound

This paper cites Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context.

2.5 Years in Class: A Multimodal Textbook for Vision-Language Pretraining Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context

Reference 45

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Observation 2862ef4c-f3d6-410f-9b37-35db36abbf42 · outbound

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

2.5 Years in Class: A Multimodal Textbook for Vision-Language Pretraining LLaMA: Open and Efficient Foundation Language Models

Reference 46

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Observation 535b67e5-2d7b-49b8-ad45-1e3e1a625524 · outbound

This paper cites Mobile- clip: Fast image-text models through multi-modal reinforced training.

2.5 Years in Class: A Multimodal Textbook for Vision-Language Pretraining Mobile- clip: Fast image-text models through multi-modal reinforced training

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:41:34.236099Z

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Observation 01e51f8b-1e81-4081-893f-cc2d29d6c965 · outbound

This paper cites PIN: A Knowledge-Intensive Dataset for Paired and Interleaved Multimodal Documents.

2.5 Years in Class: A Multimodal Textbook for Vision-Language Pretraining PIN: A Knowledge-Intensive Dataset for Paired and Interleaved Multimodal Documents

Reference 48

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Observation 81a89059-0ce5-4ce6-9ef3-025dadc1d6b9 · outbound

This paper cites Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution.

2.5 Years in Class: A Multimodal Textbook for Vision-Language Pretraining Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution

Reference 49

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Observation 368c1881-c794-4ecb-a8f0-b373eb63b056 · outbound

This paper cites Image quality assessment: from error visibility to structural similarity.

2.5 Years in Class: A Multimodal Textbook for Vision-Language Pretraining Image quality assessment: from error visibility to structural similarity

Reference 50

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Observation 7ca87b8d-1d5c-48a3-93f7-d08752ffe494 · outbound

This paper cites Qwen2 Technical Report.

2.5 Years in Class: A Multimodal Textbook for Vision-Language Pretraining Qwen2 Technical Report

Reference 51

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This paper cites An empirical study of gpt-3 for few-shot knowledge-based vqa.

2.5 Years in Class: A Multimodal Textbook for Vision-Language Pretraining An empirical study of gpt-3 for few-shot knowledge-based vqa

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:41:34.216387Z

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2.5 Years in Class: A Multimodal Textbook for Vision-Language Pretraining MiniCPM-V: A GPT-4V Level MLLM on Your Phone

Reference 53

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2.5 Years in Class: A Multimodal Textbook for Vision-Language Pretraining mPLUG-Owl3: Towards Long Image-Sequence Understanding in Multi-Modal Large Language Models

Reference 54

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2.5 Years in Class: A Multimodal Textbook for Vision-Language Pretraining Mmmu: A massive multi-discipline multimodal understanding and reasoning benchmark for ex- pert agi

Reference 55

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This paper cites Merlot reserve: Neu- ral script knowledge through vision and language and sound.

2.5 Years in Class: A Multimodal Textbook for Vision-Language Pretraining Merlot reserve: Neu- ral script knowledge through vision and language and sound

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:41:34.195186Z

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Observation c8521ea9-ad14-473a-8b8a-b88338adf286 · outbound

This paper cites Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding.

2.5 Years in Class: A Multimodal Textbook for Vision-Language Pretraining Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding

Reference 57

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2.5 Years in Class: A Multimodal Textbook for Vision-Language Pretraining Embodied-Reasoner: Synergizing Visual Search, Reasoning, and Action for Embodied Interactive Tasks

Reference 58

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2.5 Years in Class: A Multimodal Textbook for Vision-Language Pretraining MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models

Reference 59

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2.5 Years in Class: A Multimodal Textbook for Vision-Language Pretraining Multimodal c4: An open, billion-scale corpus of images interleaved with text

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:41:34.183742Z

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Observation 34633937-b0ca-4b6d-98f8-076e42a0e327 · outbound

This paper cites Implementation Details When synthesizing the Knowledge Taxonomy, we utilize GPT-4o to construct the taxonomy.

2.5 Years in Class: A Multimodal Textbook for Vision-Language Pretraining Implementation Details When synthesizing the Knowledge Taxonomy, we utilize GPT-4o to construct the taxonomy

Reference 61

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Observation c2c8b565-e288-43bc-b52a-676f0f40eb11 · outbound

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Reference 62

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2.5 Years in Class: A Multimodal Textbook for Vision-Language Pretraining We will continue to improve the quality and knowledge density of our textbook

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:41:34.146027Z

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Observation bf2a67c5-9339-456f-881b-0d87115b81c5 · outbound

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Reference 64

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Observation 735ffa26-4f41-48f0-845d-2f5b7dd4669c · outbound

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Reference 65

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Pith citing papers

Observation 07cc1ea5-4d39-47ed-aa23-194287dc94d8 · inbound

MMSearch-R1: Incentivizing LMMs to Search cites this paper.

MMSearch-R1: Incentivizing LMMs to Search 2.5 Years in Class: A Multimodal Textbook for Vision-Language Pretraining

Reference 68

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Observation 94d03705-9203-4199-b635-f0431c84279a · inbound

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Logics-Parsing-Omni Technical Report 2.5 Years in Class: A Multimodal Textbook for Vision-Language Pretraining

Reference 25

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Observation 3509a9af-5078-47ba-9cd8-a82bdce4b7d4 · inbound

Shaping Schema via Language Representation as the Next Frontier for LLM Intelligence Expanding cites this paper.

Shaping Schema via Language Representation as the Next Frontier for LLM Intelligence Expanding 2.5 Years in Class: A Multimodal Textbook for Vision-Language Pretraining

Reference 101

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