{"total":133,"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":"2403.05525","title":"DeepSeek-VL: Towards Real-World Vision-Language Understanding","created":"2024-03-08","cited_by_count":733,"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":"2212.10505","title":"DePlot: One-shot visual language reasoning by plot-to-table translation","created":"2022-12-20","cited_by_count":160,"area":"lr"},{"id":"2305.14761","title":"UniChart: A Universal Vision-language Pretrained Model for Chart Comprehension and Reasoning","created":"2023-05-24","cited_by_count":160,"area":"mllm"},{"id":"2402.12185","title":"ChartX & ChartVLM: A Versatile Benchmark and Foundation Model for Complicated Chart Reasoning","created":"2024-02-19","cited_by_count":128,"area":"lr"},{"id":"2312.15915","title":"ChartBench: A Benchmark for Complex Visual Reasoning in Charts","created":"2023-12-26","cited_by_count":120,"area":"lr"},{"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":"2505.19255","title":"VTool-R1: VLMs Learn to Think with Images via Reinforcement Learning on Multimodal Tool Use","created":"2025-05-25","cited_by_count":90,"area":"lr"},{"id":"2312.06109","title":"Vary: Scaling up the Vision Vocabulary for Large Vision-Language Models","created":"2023-12-11","cited_by_count":84,"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":"2401.00812","title":"If LLM Is the Wizard, Then Code Is the Wand: A Survey on How Code Empowers Large Language Models to Serve as Intelligent Agents","created":"2024-01-01","cited_by_count":79,"area":"agents"},{"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":"2312.11562","title":"A Survey of Reasoning with Foundation Models","created":"2023-12-17","cited_by_count":70,"area":"lr"},{"id":"2308.03349","title":"SciGraphQA: A Large-Scale Synthetic Multi-Turn Question-Answering Dataset for Scientific Graphs","created":"2023-08-07","cited_by_count":68,"area":null},{"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":"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":"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":"2402.19014","title":"Enhancing Visual Document Understanding with Contrastive Learning in Large Visual-Language Models","created":"2024-02-29","cited_by_count":30,"area":"mllm"},{"id":"2406.00257","title":"Are Large Vision Language Models up to the Challenge of Chart Comprehension and Reasoning? An Extensive Investigation into the Capabilities and Limitations of LVLMs","created":"2024-06-01","cited_by_count":30,"area":"lr"},{"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":"2509.25373","title":"From Perception to Cognition: A Survey of Vision-Language Interactive Reasoning in Multimodal Large Language Models","created":"2025-09-29","cited_by_count":26,"area":"lr"},{"id":"2304.02173","title":"ChartReader: A Unified Framework for Chart Derendering and Comprehension without Heuristic Rules","created":"2023-04-05","cited_by_count":24,"area":null},{"id":"2410.14179","title":"MultiChartQA: Benchmarking Vision-Language Models on Multi-Chart Problems","created":"2024-10-18","cited_by_count":24,"area":"mllm"},{"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":"2309.11268","title":"StructChart: On the Schema, Metric, and Augmentation for Visual Chart Understanding","created":"2023-09-20","cited_by_count":19,"area":null},{"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":"2403.16385","title":"Synthesize Step-by-Step: Tools, Templates and LLMs as Data Generators for Reasoning-Based Chart VQA","created":"2024-03-25","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":"2503.18172","title":"Unmasking Deceptive Visuals: Benchmarking Multimodal Large Language Models on Misleading Chart Question Answering","created":"2025-03-23","cited_by_count":16,"area":"mllm"},{"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":"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":"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":"2509.25185","title":"PixelCraft: A Multi-Agent System for High-Fidelity Visual Reasoning on Structured Images","created":"2025-09-29","cited_by_count":12,"area":"lr"},{"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":"2503.23131","title":"RefChartQA: Grounding Visual Answer on Chart Images through Instruction Tuning","created":"2025-03-29","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":"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":"2507.11810","title":"Evolving Roles of LLMs in Scientific Innovation: Assistant, Collaborator, Scientist, and Evaluator","created":"2025-07-16","cited_by_count":9,"area":null},{"id":"2301.13382","title":"Numeracy from Literacy: Data Science as an Emergent Skill from Large Language Models","created":"2023-01-31","cited_by_count":8,"area":null},{"id":"2308.01979","title":"RealCQA: Scientific Chart Question Answering as a Test-bed for First-Order Logic","created":"2023-08-03","cited_by_count":8,"area":null},{"id":"2502.20503","title":"Protecting multimodal large language models against misleading visualizations","created":"2025-02-27","cited_by_count":8,"area":"mllm"},{"id":"2309.14389","title":"Analyzing the Efficacy of an LLM-Only Approach for Image-based Document Question Answering","created":"2023-09-25","cited_by_count":7,"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":"2501.15074","title":"PatentLMM: Large Multimodal Model for Generating Descriptions for Patent Figures","created":"2025-01-25","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":"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":"2503.04095","title":"Chart-HQA: A Benchmark for Hypothetical Question Answering in Charts","created":"2025-03-06","cited_by_count":6,"area":"mllm"},{"id":"2512.00305","title":"ChartPoint: Guiding MLLMs with Grounding Reflection for Chart Reasoning","created":"2025-11-29","cited_by_count":6,"area":"lr"},{"id":"2305.01938","title":"Doc2SoarGraph: Discrete Reasoning over Visually-Rich Table-Text Documents via Semantic-Oriented Hierarchical Graphs","created":"2023-05-03","cited_by_count":5,"area":null},{"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":"2405.19094","title":"Faithful Chart Summarization with ChaTS-Pi","created":"2024-05-29","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":"2501.04675","title":"Enhancing Financial VQA in Vision Language Models using Intermediate Structured Representations","created":"2025-01-08","cited_by_count":5,"area":"mllm"},{"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":"2505.17202","title":"CHART-6: Human-Centered Evaluation of Data Visualization Understanding in Vision-Language Models","created":"2025-05-22","cited_by_count":5,"area":"mllm"},{"id":"2602.16455","title":"Visual Self-Refine: A Pixel-Guided Paradigm for Accurate Chart Parsing","created":"2026-02-18","cited_by_count":5,"area":"mllm"},{"id":"2305.15080","title":"Visually-Situated Natural Language Understanding with Contrastive Reading Model and Frozen Large Language Models","created":"2023-05-24","cited_by_count":4,"area":null},{"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":"2408.14764","title":"SynthDoc: Bilingual Documents Synthesis for Visual Document Understanding","created":"2024-08-27","cited_by_count":4,"area":null},{"id":"2410.21414","title":"CT2C-QA: Multimodal Question Answering over Chinese Text, Table and Chart","created":"2024-10-28","cited_by_count":4,"area":null},{"id":"2412.20715","title":"ChartAdapter: Large Vision-Language Model for Chart Summarization","created":"2024-12-30","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":"2310.18983","title":"DCQA: Document-Level Chart Question Answering towards Complex Reasoning and Common-Sense Understanding","created":"2023-10-29","cited_by_count":3,"area":null},{"id":"2405.13580","title":"AltChart: Enhancing VLM-based Chart Summarization Through Multi-Pretext Tasks","created":"2024-05-22","cited_by_count":3,"area":null},{"id":"2501.04303","title":"Graph-Based Multimodal Contrastive Learning for Chart Question Answering","created":"2025-01-08","cited_by_count":3,"area":null},{"id":"2504.09764","title":"Socratic Chart: Cooperating Multiple Agents for Robust SVG Chart Understanding","created":"2025-04-14","cited_by_count":3,"area":"mllm"},{"id":"2505.19360","title":"ChartLens: Fine-grained Visual Attribution in Charts","created":"2025-05-25","cited_by_count":3,"area":"mllm"},{"id":"2507.14298","title":"In-Depth and In-Breadth: Pre-training Multimodal Language Models Customized for Comprehensive Chart Understanding","created":"2025-07-18","cited_by_count":3,"area":"mllm"},{"id":"2310.12147","title":"InViG: Benchmarking Interactive Visual Grounding with 500K Human-Robot Interactions","created":"2023-10-18","cited_by_count":2,"area":null},{"id":"2408.04852","title":"MSG-Chart: Multimodal Scene Graph for ChartQA","created":"2024-08-09","cited_by_count":2,"area":null},{"id":"2409.02611","title":"GoT-CQA: Graph-of-Thought Guided Compositional Reasoning for Chart Question Answering","created":"2024-09-04","cited_by_count":2,"area":"lr"},{"id":"2409.18764","title":"Charting the Future: Using Chart Question-Answering for Scalable Evaluation of LLM-Driven Data Visualizations","created":"2024-09-27","cited_by_count":2,"area":"synth"},{"id":"2506.06561","title":"LaMP-Cap: Personalized Figure Caption Generation With Multimodal Figure Profiles","created":"2025-06-06","cited_by_count":2,"area":null},{"id":"2506.08700","title":"ClimateViz: A Benchmark for Statistical Reasoning and Fact Verification on Scientific Charts","created":"2025-06-10","cited_by_count":2,"area":"lr"},{"id":"2602.10880","title":"Chart Specification: Structural Representations for Incentivizing VLM Reasoning in Chart-to-Code Generation","created":"2026-02-11","cited_by_count":2,"area":"lr"},{"id":"2603.28902","title":"ChartDiff: A Large-Scale Benchmark for Comprehending Pairs of Charts","created":"2026-03-30","cited_by_count":2,"area":null},{"id":"2606.29808","title":"Making Multimodal LLMs Reliable Chart Data Extractors: A Benchmark and Training Framework","created":"2026-06-29","cited_by_count":2,"area":"mllm"},{"id":"2409.16517","title":"SynChart: Synthesizing Charts from Language Models","created":"2024-09-25","cited_by_count":1,"area":null},{"id":"2505.17235","title":"CHAOS: Chart Analysis with Outlier Samples","created":"2025-05-22","cited_by_count":1,"area":null},{"id":"2602.07645","title":"From Dead Pixels to Editable Slides: Infographic Reconstruction into Native Google Slides via Vision-Language Region Understanding","created":"2026-02-07","cited_by_count":1,"area":null},{"id":"2602.10138","title":"Multimodal Information Fusion for Chart Understanding: A Survey of MLLMs -- Evolution, Limitations, and Cognitive Enhancement","created":"2026-02-08","cited_by_count":1,"area":null},{"id":"2602.18178","title":"Evaluating Graphical Perception Capabilities of Vision Transformers","created":"2026-02-20","cited_by_count":1,"area":null},{"id":"2602.18731","title":"Beyond Description: A Multimodal Agent Framework for Insightful Chart Summarization","created":"2026-02-21","cited_by_count":1,"area":"agents"},{"id":"2603.28583","title":"Navigating the Mirage: A Dual-Path Agentic Framework for Robust Misleading Chart Question Answering","created":"2026-03-30","cited_by_count":1,"area":"mllm"}],"next_cursor":"100"}