Paper Citation Record · LEDGER

Building and better understanding vision-language models: insights and future directions

As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 48 inbound Pith citation observations for arXiv:2408.12637.

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

pith.paper-citation-record.v1
2408.12637 v1

Coverage vector

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measured 48 of 48 standing notices

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measured 48 of 48 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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Outbound references

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Pith citing papers

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MMMU-Pro: A More Robust Multi-discipline Multimodal Understanding Benchmark cites this paper.

MMMU-Pro: A More Robust Multi-discipline Multimodal Understanding Benchmark Building and better understanding vision-language models: insights and future directions

Reference 19

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Molmo and PixMo: Open Weights and Open Data for State-of-the-Art Vision-Language Models cites this paper.

Molmo and PixMo: Open Weights and Open Data for State-of-the-Art Vision-Language Models Building and better understanding vision-language models: insights and future directions

Reference 54

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Insight-V: Exploring Long-Chain Visual Reasoning with Multimodal Large Language Models cites this paper.

Insight-V: Exploring Long-Chain Visual Reasoning with Multimodal Large Language Models Building and better understanding vision-language models: insights and future directions

Reference 14

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VARCO-VISION: Expanding Frontiers in Korean Vision-Language Models cites this paper.

VARCO-VISION: Expanding Frontiers in Korean Vision-Language Models Building and better understanding vision-language models: insights and future directions

Reference 14

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NVILA: Efficient Frontier Visual Language Models cites this paper.

NVILA: Efficient Frontier Visual Language Models Building and better understanding vision-language models: insights and future directions

Reference 96

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Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling cites this paper.

Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling Building and better understanding vision-language models: insights and future directions

Reference 121

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Apollo: An Exploration of Video Understanding in Large Multimodal Models cites this paper.

Apollo: An Exploration of Video Understanding in Large Multimodal Models Building and better understanding vision-language models: insights and future directions

Reference 25

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A High-Quality Text-Rich Image Instruction Tuning Dataset via Hybrid Instruction Generation cites this paper.

A High-Quality Text-Rich Image Instruction Tuning Dataset via Hybrid Instruction Generation Building and better understanding vision-language models: insights and future directions

Reference 21

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2.5 Years in Class: A Multimodal Textbook for Vision-Language Pretraining cites this paper.

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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Automated Generation of Challenging Multiple-Choice Questions for Vision Language Model Evaluation cites this paper.

Automated Generation of Challenging Multiple-Choice Questions for Vision Language Model Evaluation Building and better understanding vision-language models: insights and future directions

Reference 21

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ECBench: Can Multi-modal Foundation Models Understand the Egocentric World? A Holistic Embodied Cognition Benchmark cites this paper.

ECBench: Can Multi-modal Foundation Models Understand the Egocentric World? A Holistic Embodied Cognition Benchmark Building and better understanding vision-language models: insights and future directions

Reference 17

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MSTS: A Multimodal Safety Test Suite for Vision-Language Models cites this paper.

MSTS: A Multimodal Safety Test Suite for Vision-Language Models Building and better understanding vision-language models: insights and future directions

Reference 16

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Eagle 2: Building Post-Training Data Strategies from Scratch for Frontier Vision-Language Models cites this paper.

Eagle 2: Building Post-Training Data Strategies from Scratch for Frontier Vision-Language Models Building and better understanding vision-language models: insights and future directions

Reference 108

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The Impact of Persona-based Political Perspectives on Hateful Content Detection cites this paper.

The Impact of Persona-based Political Perspectives on Hateful Content Detection Building and better understanding vision-language models: insights and future directions

Reference 7

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EVEv2: Improved Baselines for Encoder-Free Vision-Language Models cites this paper.

EVEv2: Improved Baselines for Encoder-Free Vision-Language Models Building and better understanding vision-language models: insights and future directions

Reference 38

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SmolVLM: Redefining small and efficient multimodal models cites this paper.

SmolVLM: Redefining small and efficient multimodal models Building and better understanding vision-language models: insights and future directions

Reference 21

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Benchmarking Multimodal Mathematical Reasoning with Explicit Visual Dependency cites this paper.

Benchmarking Multimodal Mathematical Reasoning with Explicit Visual Dependency Building and better understanding vision-language models: insights and future directions

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R^3-VQA: "Read the Room" by Video Social Reasoning cites this paper.

R^3-VQA: "Read the Room" by Video Social Reasoning Building and better understanding vision-language models: insights and future directions

Reference 26

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FG-CLIP: Fine-Grained Visual and Textual Alignment cites this paper.

FG-CLIP: Fine-Grained Visual and Textual Alignment Building and better understanding vision-language models: insights and future directions

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MoCa: Modality-aware Continual Pre-training Makes Better Bidirectional Multimodal Embeddings cites this paper.

MoCa: Modality-aware Continual Pre-training Makes Better Bidirectional Multimodal Embeddings Building and better understanding vision-language models: insights and future directions

Reference 20

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MoCa: Modality-aware Continual Pre-training Makes Better Bidirectional Multimodal Embeddings cites this paper.

MoCa: Modality-aware Continual Pre-training Makes Better Bidirectional Multimodal Embeddings Building and better understanding vision-language models: insights and future directions

Reference 2024

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Table Understanding and (Multimodal) LLMs: A Cross-Domain Case Study on Scientific vs. Non-Scientific Data cites this paper.

Table Understanding and (Multimodal) LLMs: A Cross-Domain Case Study on Scientific vs. Non-Scientific Data Building and better understanding vision-language models: insights and future directions

Reference 28

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Vision-Language Models Can't See the Obvious cites this paper.

Vision-Language Models Can't See the Obvious Building and better understanding vision-language models: insights and future directions

Reference 25

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Docopilot: Improving Multimodal Models for Document-Level Understanding cites this paper.

Docopilot: Improving Multimodal Models for Document-Level Understanding Building and better understanding vision-language models: insights and future directions

Reference 33

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LMM-Det: Make Large Multimodal Models Excel in Object Detection cites this paper.

LMM-Det: Make Large Multimodal Models Excel in Object Detection Building and better understanding vision-language models: insights and future directions

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VisR-Bench: An Empirical Study on Visual Retrieval-Augmented Generation for Multilingual Long Document Understanding cites this paper.

VisR-Bench: An Empirical Study on Visual Retrieval-Augmented Generation for Multilingual Long Document Understanding Building and better understanding vision-language models: insights and future directions

Reference 19

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InterChart: Benchmarking Visual Reasoning Across Decomposed and Distributed Chart Information cites this paper.

InterChart: Benchmarking Visual Reasoning Across Decomposed and Distributed Chart Information Building and better understanding vision-language models: insights and future directions

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Measuring Epistemic Humility in Multimodal Large Language Models cites this paper.

Measuring Epistemic Humility in Multimodal Large Language Models Building and better understanding vision-language models: insights and future directions

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MultiMat: Multimodal Program Synthesis for Procedural Materials using Large Multimodal Models cites this paper.

MultiMat: Multimodal Program Synthesis for Procedural Materials using Large Multimodal Models Building and better understanding vision-language models: insights and future directions

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Diagnosing Corruption-Induced Reliability Failures in Vision-Language Models cites this paper.

Diagnosing Corruption-Induced Reliability Failures in Vision-Language Models Building and better understanding vision-language models: insights and future directions

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Magic-MM-Embedding: Towards Visual-Token-Efficient Universal Multimodal Embedding with MLLMs cites this paper.

Magic-MM-Embedding: Towards Visual-Token-Efficient Universal Multimodal Embedding with MLLMs Building and better understanding vision-language models: insights and future directions

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Fine-R1: Make Multi-modal LLMs Excel in Fine-Grained Visual Recognition by Chain-of-Thought Reasoning cites this paper.

Fine-R1: Make Multi-modal LLMs Excel in Fine-Grained Visual Recognition by Chain-of-Thought Reasoning Building and better understanding vision-language models: insights and future directions

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Back to the Barn with LLAMAs: Evolving Pretrained LLM Backbones in Finetuning Vision Language Models cites this paper.

Back to the Barn with LLAMAs: Evolving Pretrained LLM Backbones in Finetuning Vision Language Models Building and better understanding vision-language models: insights and future directions

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DenTab: A Dataset for Table Recognition and Visual QA on Real-World Dental Estimates cites this paper.

DenTab: A Dataset for Table Recognition and Visual QA on Real-World Dental Estimates Building and better understanding vision-language models: insights and future directions

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PBSBench: A Multi-Level Vision-Language Framework and Benchmark for Hematopathology Whole Slide Image Interpretation cites this paper.

PBSBench: A Multi-Level Vision-Language Framework and Benchmark for Hematopathology Whole Slide Image Interpretation Building and better understanding vision-language models: insights and future directions

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ZAYA1-VL-8B Technical Report cites this paper.

ZAYA1-VL-8B Technical Report Building and better understanding vision-language models: insights and future directions

Reference 132

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20/20 Vision Language Models: A Prescription for Better VLMs through Data Curation Alone cites this paper.

20/20 Vision Language Models: A Prescription for Better VLMs through Data Curation Alone Building and better understanding vision-language models: insights and future directions

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20/20 Vision Language Models: A Prescription for Better VLMs through Data Curation Alone cites this paper.

20/20 Vision Language Models: A Prescription for Better VLMs through Data Curation Alone Building and better understanding vision-language models: insights and future directions

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VEN-VL: A Visual Ensemble MoE Framework for Effective and Efficient Multi-Modal Understanding cites this paper.

VEN-VL: A Visual Ensemble MoE Framework for Effective and Efficient Multi-Modal Understanding Building and better understanding vision-language models: insights and future directions

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Zamba2-VL Technical Report cites this paper.

Zamba2-VL Technical Report Building and better understanding vision-language models: insights and future directions

Reference 114

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Stellar: Scalable Multimodal Document Retrieval for Natural Language Queries cites this paper.

Stellar: Scalable Multimodal Document Retrieval for Natural Language Queries Building and better understanding vision-language models: insights and future directions

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DataComp-VLM: Improved Open Datasets for Vision-Language Models cites this paper.

DataComp-VLM: Improved Open Datasets for Vision-Language Models Building and better understanding vision-language models: insights and future directions

Reference 143

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DataComp-VLM: Improved Open Datasets for Vision-Language Models cites this paper.

DataComp-VLM: Improved Open Datasets for Vision-Language Models Building and better understanding vision-language models: insights and future directions

Reference 143

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MVEI & EmObserver: Empowering MLLM-Oriented Visual Emotional Intelligence via Emotion Statement Judgement cites this paper.

MVEI & EmObserver: Empowering MLLM-Oriented Visual Emotional Intelligence via Emotion Statement Judgement Building and better understanding vision-language models: insights and future directions

Reference 62

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MulRobBench: A Decision-Level Benchmark for Safe and Security-Policy-Compliant Multimodal UAV Agents cites this paper.

MulRobBench: A Decision-Level Benchmark for Safe and Security-Policy-Compliant Multimodal UAV Agents Building and better understanding vision-language models: insights and future directions

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Locating Failure in Multi-Page Visually Rich Document Understanding: An Empirical Attribution cites this paper.

Locating Failure in Multi-Page Visually Rich Document Understanding: An Empirical Attribution Building and better understanding vision-language models: insights and future directions

Reference 114

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DistilVDR: A Compact End-to-End Visual Document Retriever via Dual-Student Distillation cites this paper.

DistilVDR: A Compact End-to-End Visual Document Retriever via Dual-Student Distillation Building and better understanding vision-language models: insights and future directions

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Edit2TikZ: A Comprehensive and Challenging Benchmark for Scientific Figure Editing with TikZ cites this paper.

Edit2TikZ: A Comprehensive and Challenging Benchmark for Scientific Figure Editing with TikZ Building and better understanding vision-language models: insights and future directions

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