{"total":160,"rows":[{"id":"2310.02255","title":"MathVista: Evaluating Mathematical Reasoning of Foundation Models in Visual Contexts","created":"2023-10-03","cited_by_count":1614,"area":"lr"},{"id":"2412.05271","title":"Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling","created":"2024-12-06","cited_by_count":1599,"area":"lr"},{"id":"2403.05525","title":"DeepSeek-VL: Towards Real-World Vision-Language Understanding","created":"2024-03-08","cited_by_count":733,"area":null},{"id":"2412.04468","title":"NVILA: Efficient Frontier Visual Language Models","created":"2024-12-05","cited_by_count":209,"area":"mllm"},{"id":"2409.01704","title":"General OCR Theory: Towards OCR-2.0 via a Unified End-to-end Model","created":"2024-09-03","cited_by_count":180,"area":null},{"id":"2311.16483","title":"ChartLlama: A Multimodal LLM for Chart Understanding and Generation","created":"2023-11-27","cited_by_count":163,"area":"mllm"},{"id":"2311.10774","title":"MMC: Advancing Multimodal Chart Understanding with Large-scale Instruction Tuning","created":"2023-11-15","cited_by_count":150,"area":"mllm"},{"id":"2312.15915","title":"ChartBench: A Benchmark for Complex Visual Reasoning in Charts","created":"2023-12-26","cited_by_count":120,"area":"lr"},{"id":"2501.15368","title":"Baichuan-Omni-1.5 Technical Report","created":"2025-01-26","cited_by_count":111,"area":null},{"id":"2504.05506","title":"ChartQAPro: A More Diverse and Challenging Benchmark for Chart Question Answering","created":"2025-04-07","cited_by_count":108,"area":null},{"id":"2404.07503","title":"Best Practices and Lessons Learned on Synthetic Data","created":"2024-04-11","cited_by_count":106,"area":"synth"},{"id":"2402.04615","title":"ScreenAI: A Vision-Language Model for UI and Infographics Understanding","created":"2024-02-07","cited_by_count":98,"area":"mllm"},{"id":"2410.16261","title":"Mini-InternVL: A Flexible-Transfer Pocket Multimodal Model with 5% Parameters and 90% Performance","created":"2024-10-21","cited_by_count":94,"area":"mllm"},{"id":"2501.06598","title":"ChartCoder: Advancing Multimodal Large Language Model for Chart-to-Code Generation","created":"2025-01-11","cited_by_count":92,"area":"mllm"},{"id":"2510.14528","title":"PaddleOCR-VL: Boosting Multilingual Document Parsing via a 0.9B Ultra-Compact Vision-Language Model","created":"2025-10-16","cited_by_count":89,"area":"mllm"},{"id":"2501.05452","title":"ReFocus: Visual Editing as a Chain of Thought for Structured Image Understanding","created":"2025-01-09","cited_by_count":83,"area":"lr"},{"id":"2401.02384","title":"ChartAssisstant: A Universal Chart Multimodal Language Model via Chart-to-Table Pre-training and Multitask Instruction Tuning","created":"2024-01-04","cited_by_count":79,"area":"mllm"},{"id":"2407.04172","title":"ChartGemma: Visual Instruction-tuning for Chart Reasoning in the Wild","created":"2024-07-04","cited_by_count":79,"area":null},{"id":"2407.09413","title":"SPIQA: A Dataset for Multimodal Question Answering on Scientific Papers","created":"2024-07-12","cited_by_count":75,"area":null},{"id":"2501.14818","title":"Eagle 2: Building Post-Training Data Strategies from Scratch for Frontier Vision-Language Models","created":"2025-01-20","cited_by_count":70,"area":"mllm"},{"id":"2403.09028","title":"ChartInstruct: Instruction Tuning for Chart Comprehension and Reasoning","created":"2024-03-14","cited_by_count":68,"area":"lr"},{"id":"2403.12027","title":"From Pixels to Insights: A Survey on Automatic Chart Understanding in the Era of Large Foundation Models","created":"2024-03-18","cited_by_count":53,"area":null},{"id":"2503.11576","title":"SmolDocling: An ultra-compact vision-language model for end-to-end multi-modal document conversion","created":"2025-03-14","cited_by_count":53,"area":"mllm"},{"id":"2510.17269","title":"FineVision: Open Data Is All You Need","created":"2025-10-20","cited_by_count":53,"area":"mllm"},{"id":"2404.16635","title":"TinyChart: Efficient Chart Understanding with Visual Token Merging and Program-of-Thoughts Learning","created":"2024-04-25","cited_by_count":52,"area":"lr"},{"id":"2405.07001","title":"ChartInsights: Evaluating Multimodal Large Language Models for Low-Level Chart Question Answering","created":"2024-05-11","cited_by_count":50,"area":"mllm"},{"id":"2312.10160","title":"Do LVLMs Understand Charts? Analyzing and Correcting Factual Errors in Chart Captioning","created":"2023-12-15","cited_by_count":49,"area":"mllm"},{"id":"2409.03277","title":"ChartMoE: Mixture of Diversely Aligned Expert Connector for Chart Understanding","created":"2024-09-05","cited_by_count":46,"area":"moe"},{"id":"2404.09987","title":"OneChart: Purify the Chart Structural Extraction via One Auxiliary Token","created":"2024-04-15","cited_by_count":39,"area":null},{"id":"2504.08748","title":"A Survey of Multimodal Retrieval-Augmented Generation","created":"2025-03-26","cited_by_count":37,"area":"rag"},{"id":"2409.01577","title":"EvoChart: A Benchmark and a Self-Training Approach Towards Real-World Chart Understanding","created":"2024-09-03","cited_by_count":34,"area":"synth"},{"id":"2505.20289","title":"VisualToolAgent (VisTA): A Reinforcement Learning Framework for Visual Tool Selection","created":"2025-05-26","cited_by_count":34,"area":"lr"},{"id":"2605.27365","title":"LocateAnything: Fast and High-Quality Vision-Language Grounding with Parallel Box Decoding","created":"2026-05-26","cited_by_count":28,"area":"mllm"},{"id":"2407.17291","title":"How Good (Or Bad) Are LLMs at Detecting Misleading Visualizations?","created":"2024-07-24","cited_by_count":27,"area":"mllm"},{"id":"2407.20174","title":"Advancing Multimodal Large Language Models in Chart Question Answering with Visualization-Referenced Instruction Tuning","created":"2024-07-29","cited_by_count":27,"area":"mllm"},{"id":"2311.07453","title":"ChartCheck: Explainable Fact-Checking over Real-World Chart Images","created":"2023-11-13","cited_by_count":26,"area":null},{"id":"2408.05346","title":"DataNarrative: Automated Data-Driven Storytelling with Visualizations and Texts","created":"2024-08-09","cited_by_count":25,"area":"agents"},{"id":"2412.09613","title":"PVC: Progressive Visual Token Compression for Unified Image and Video Processing in Large Vision-Language Models","created":"2024-12-12","cited_by_count":25,"area":"mllm"},{"id":"2404.18144","title":"Generative AI for Visualization: State of the Art and Future Directions","created":"2024-04-28","cited_by_count":23,"area":null},{"id":"2410.14702","title":"Polymath: A Challenging Multi-modal Mathematical Reasoning Benchmark","created":"2024-10-06","cited_by_count":22,"area":"lr"},{"id":"2403.12596","title":"Chart-based Reasoning: Transferring Capabilities from LLMs to VLMs","created":"2024-03-19","cited_by_count":20,"area":"lr"},{"id":"2508.06492","title":"Effective Training Data Synthesis for Improving MLLM Chart Understanding","created":"2025-08-08","cited_by_count":20,"area":"synth"},{"id":"2401.13311","title":"ConTextual: Evaluating Context-Sensitive Text-Rich Visual Reasoning in Large Multimodal Models","created":"2024-01-24","cited_by_count":19,"area":"lr"},{"id":"2403.11236","title":"ChartThinker: A Contextual Chain-of-Thought Approach to Optimized Chart Summarization","created":"2024-03-17","cited_by_count":19,"area":"lr"},{"id":"2407.11229","title":"Unraveling the Truth: Do VLMs really Understand Charts? A Deep Dive into Consistency and Robustness","created":"2024-07-15","cited_by_count":17,"area":null},{"id":"2410.04064","title":"Text2Chart31: Instruction Tuning for Chart Generation with Automatic Feedback","created":"2024-10-05","cited_by_count":17,"area":"alignment"},{"id":"2509.23988","title":"LLM/Agent-as-Data-Analyst: A Survey","created":"2025-09-28","cited_by_count":16,"area":"agents"},{"id":"2409.19242","title":"SciDoc2Diagrammer-MAF: Towards Generation of Scientific Diagrams from Documents guided by Multi-Aspect Feedback Refinement","created":"2024-09-28","cited_by_count":15,"area":null},{"id":"2503.14939","title":"VisNumBench: Evaluating Number Sense of Multimodal Large Language Models","created":"2025-03-19","cited_by_count":14,"area":"mllm"},{"id":"2403.00209","title":"ChartReformer: Natural Language-Driven Chart Image Editing","created":"2024-03-01","cited_by_count":13,"area":null},{"id":"2407.14506","title":"On Pre-training of Multimodal Language Models Customized for Chart Understanding","created":"2024-07-19","cited_by_count":13,"area":"mllm"},{"id":"2503.04110","title":"InterChat: Enhancing Generative Visual Analytics using Multimodal Interactions","created":"2025-03-06","cited_by_count":13,"area":null},{"id":"2508.12680","title":"Vision-G1: Towards General Vision Language Reasoning with Multi-Domain Data Curation","created":"2025-08-18","cited_by_count":13,"area":"lr"},{"id":"2310.02804","title":"DOMINO: A Dual-System for Multi-step Visual Language Reasoning","created":"2023-10-04","cited_by_count":12,"area":"lr"},{"id":"2508.04650","title":"EncQA: Benchmarking Vision-Language Models on Visual Encodings for Charts","created":"2025-08-06","cited_by_count":12,"area":"mllm"},{"id":"2405.21013","title":"StrucTexTv3: An Efficient Vision-Language Model for Text-rich Image Perception, Comprehension, and Beyond","created":"2024-05-31","cited_by_count":11,"area":"mllm"},{"id":"2412.05983","title":"Chimera: Improving Generalist Model with Domain-Specific Experts","created":"2024-12-08","cited_by_count":11,"area":"mllm"},{"id":"2503.23131","title":"RefChartQA: Grounding Visual Answer on Chart Images through Instruction Tuning","created":"2025-03-29","cited_by_count":11,"area":"mllm"},{"id":"2512.00752","title":"Charts Are Not Images: On the Challenges of Scientific Chart Editing","created":"2025-11-30","cited_by_count":11,"area":null},{"id":"2604.25884","title":"QCalEval: Benchmarking Vision-Language Models for Quantum Calibration Plot Understanding","created":"2026-04-28","cited_by_count":11,"area":"mllm"},{"id":"2312.10610","title":"Do LLMs Work on Charts? Designing Few-Shot Prompts for Chart Question Answering and Summarization","created":"2023-12-17","cited_by_count":10,"area":null},{"id":"2412.16158","title":"HoVLE: Unleashing the Power of Monolithic Vision-Language Models with Holistic Vision-Language Embedding","created":"2024-12-20","cited_by_count":10,"area":"mllm"},{"id":"2404.01548","title":"mChartQA: A universal benchmark for multimodal Chart Question Answer based on Vision-Language Alignment and Reasoning","created":"2024-04-02","cited_by_count":9,"area":"lr"},{"id":"2406.14056","title":"VGA: Vision GUI Assistant -- Minimizing Hallucinations through Image-Centric Fine-Tuning","created":"2024-06-20","cited_by_count":9,"area":"mllm"},{"id":"2501.09349","title":"ChartInsighter: An Approach for Mitigating Hallucination in Time-series Chart Summary Generation with A Benchmark Dataset","created":"2025-01-16","cited_by_count":9,"area":null},{"id":"2502.16586","title":"Multimodal Large Language Models for Text-rich Image Understanding: A Comprehensive Review","created":"2025-02-23","cited_by_count":9,"area":"mllm"},{"id":"2412.05243","title":"CompCap: Improving Multimodal Large Language Models with Composite Captions","created":"2024-12-06","cited_by_count":8,"area":"mllm"},{"id":"2511.20648","title":"LocateAnything3D: Vision-Language 3D Detection with Chain-of-Sight","created":"2025-11-25","cited_by_count":8,"area":null},{"id":"2405.00021","title":"SIMPLOT: Enhancing Chart Question Answering by Distilling Essentials","created":"2024-02-22","cited_by_count":7,"area":"lr"},{"id":"2507.13361","title":"VLMs have Tunnel Vision: Evaluating Nonlocal Visual Reasoning in Leading VLMs","created":"2025-07-04","cited_by_count":7,"area":"lr"},{"id":"2508.03164","title":"ChartCap: Mitigating Hallucination of Dense Chart Captioning","created":"2025-08-05","cited_by_count":7,"area":"mllm"},{"id":"2508.09716","title":"The Perils of Chart Deception: How Misleading Visualizations Affect Vision-Language Models","created":"2025-08-13","cited_by_count":7,"area":"mllm"},{"id":"2601.05688","title":"SketchVL: Policy Optimization via Fine-Grained Credit Assignment for Chart Understanding and More","created":"2026-01-09","cited_by_count":7,"area":"lr"},{"id":"2603.06569","title":"Penguin-VL: Exploring the Efficiency Limits of VLM with LLM-based Vision Encoders","created":"2026-03-06","cited_by_count":7,"area":"mllm"},{"id":"2409.01667","title":"VProChart: Answering Chart Question through Visual Perception Alignment Agent and Programmatic Solution Reasoning","created":"2024-09-03","cited_by_count":6,"area":"lr"},{"id":"2409.18475","title":"Data Analysis in the Era of Generative AI","created":"2024-09-27","cited_by_count":6,"area":null},{"id":"2409.19747","title":"Natural Language Generation for Visualizations: State of the Art, Challenges and Future Directions","created":"2024-09-29","cited_by_count":6,"area":null},{"id":"2411.00257","title":"Understanding Graphical Perception in Data Visualization through Zero-shot Prompting of Vision-Language Models","created":"2024-10-31","cited_by_count":6,"area":"mllm"},{"id":"2411.08397","title":"CLaSP: Learning Concepts for Time-Series Signals from Natural Language Supervision","created":"2024-11-13","cited_by_count":6,"area":null},{"id":"2503.04095","title":"Chart-HQA: A Benchmark for Hypothetical Question Answering in Charts","created":"2025-03-06","cited_by_count":6,"area":"mllm"},{"id":"2311.09612","title":"Efficient End-to-End Visual Document Understanding with Rationale Distillation","created":"2023-11-16","cited_by_count":5,"area":null},{"id":"2403.17784","title":"SciCapenter: Supporting Caption Composition for Scientific Figures with Machine-Generated Captions and Ratings","created":"2024-03-26","cited_by_count":5,"area":null},{"id":"2407.21038","title":"Advancing Chart Question Answering with Robust Chart Component Recognition","created":"2024-07-19","cited_by_count":5,"area":null},{"id":"2408.14419","title":"CHARTOM: A Visual Theory-of-Mind Benchmark for LLMs on Misleading Charts","created":"2024-08-26","cited_by_count":5,"area":"mllm"},{"id":"2412.17606","title":"SBS Figures: Pre-training Figure QA from Stage-by-Stage Synthesized Images","created":"2024-12-23","cited_by_count":5,"area":null},{"id":"2505.15046","title":"ChartCards: A Chart-Metadata Generation Framework for Multi-Task Chart Understanding","created":"2025-05-21","cited_by_count":5,"area":null},{"id":"2509.18425","title":"Losing the Plot: How VLM responses degrade on imperfect charts","created":"2025-09-22","cited_by_count":5,"area":"mllm"},{"id":"2406.10085","title":"Enhancing Question Answering on Charts Through Effective Pre-training Tasks","created":"2024-06-14","cited_by_count":4,"area":null},{"id":"2412.10455","title":"Geo-LLaVA: A Large Multi-Modal Model for Solving Geometry Math Problems with Meta In-Context Learning","created":"2024-12-12","cited_by_count":4,"area":"lr"},{"id":"2412.20715","title":"ChartAdapter: Large Vision-Language Model for Chart Summarization","created":"2024-12-30","cited_by_count":4,"area":"mllm"},{"id":"2501.19353","title":"Do Large Multimodal Models Solve Caption Generation for Scientific Figures? Lessons Learned from SciCap Challenge 2023","created":"2025-01-31","cited_by_count":4,"area":"mllm"},{"id":"2503.22693","title":"Bridging Language Models and Financial Analysis","created":"2025-03-14","cited_by_count":4,"area":null},{"id":"2506.06279","title":"CoMemo: LVLMs Need Image Context with Image Memory","created":"2025-06-06","cited_by_count":4,"area":"mllm"},{"id":"2509.09286","title":"Visual Programmability: A Guide for Code-as-Thought in Chart Understanding","created":"2025-09-11","cited_by_count":4,"area":"lr"},{"id":"2512.07186","title":"START: Spatial and Textual Learning for Chart Understanding","created":"2025-12-08","cited_by_count":4,"area":"mllm"},{"id":"2512.14040","title":"ChartAgent: A Chart Understanding Framework with Tool Integrated Reasoning","created":"2025-12-16","cited_by_count":4,"area":"lr"},{"id":"2601.12318","title":"Beyond Human Annotation: Recent Advances in Data Generation Methods for Document Intelligence","created":"2026-01-18","cited_by_count":4,"area":null},{"id":"2603.09326","title":"OddGridBench: Exposing the Lack of Fine-Grained Visual Discrepancy Sensitivity in Multimodal Large Language Models","created":"2026-03-10","cited_by_count":4,"area":"mllm"},{"id":"2607.07836","title":"Infinity-Parser2 Technical Report","created":"2026-07-08","cited_by_count":4,"area":"lr"},{"id":"2405.13580","title":"AltChart: Enhancing VLM-based Chart Summarization Through Multi-Pretext Tasks","created":"2024-05-22","cited_by_count":3,"area":null}],"next_cursor":"100"}