{"total":128,"rows":[{"id":"2407.11691","title":"VLMEvalKit: An Open-Source Toolkit for Evaluating Large Multi-Modality Models","created":"2024-07-16","cited_by_count":455,"area":null},{"id":"2406.18521","title":"CharXiv: Charting Gaps in Realistic Chart Understanding in Multimodal LLMs","created":"2024-06-26","cited_by_count":234,"area":"mllm"},{"id":"2506.23918","title":"Thinking with Images for Multimodal Reasoning: Foundations, Methods, and Future Frontiers","created":"2025-06-30","cited_by_count":199,"area":"lr"},{"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":"2501.00321","title":"OCRBench v2: An Improved Benchmark for Evaluating Large Multimodal Models on Visual Text Localization and Reasoning","created":"2024-12-31","cited_by_count":173,"area":"mllm"},{"id":"2412.07626","title":"OmniDocBench: Benchmarking Diverse PDF Document Parsing with Comprehensive Annotations","created":"2024-12-10","cited_by_count":150,"area":"rag"},{"id":"2501.05444","title":"Can MLLMs Reason in Multimodality? EMMA: An Enhanced MultiModal ReAsoning Benchmark","created":"2025-01-09","cited_by_count":134,"area":"lr"},{"id":"2505.13259","title":"From Automation to Autonomy: A Survey on Large Language Models in Scientific Discovery","created":"2025-05-19","cited_by_count":93,"area":null},{"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":"2501.04686","title":"Unlocking Multimodal Mathematical Reasoning via Process Reward Model","created":"2025-01-08","cited_by_count":65,"area":"lr"},{"id":"2502.14846","title":"Scaling Text-Rich Image Understanding via Code-Guided Synthetic Multimodal Data Generation","created":"2025-02-20","cited_by_count":58,"area":"mllm"},{"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":"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":"2503.04629","title":"SurveyForge: On the Outline Heuristics, Memory-Driven Generation, and Multi-dimensional Evaluation for Automated Survey Writing","created":"2025-03-06","cited_by_count":49,"area":null},{"id":"2408.15769","title":"A Survey on Evaluation of Multimodal Large Language Models","created":"2024-08-28","cited_by_count":46,"area":"lr"},{"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":"2409.03643","title":"Image Over Text: Transforming Formula Recognition Evaluation with Character Detection Matching","created":"2024-09-05","cited_by_count":41,"area":null},{"id":"2510.12872","title":"KVCOMM: Online Cross-context KV-cache Communication for Efficient LLM-based Multi-agent Systems","created":"2025-10-14","cited_by_count":40,"area":"agents"},{"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":"2406.11633","title":"DocGenome: An Open Large-scale Scientific Document Benchmark for Training and Testing Multi-modal Large Language Models","created":"2024-06-17","cited_by_count":36,"area":"mllm"},{"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":"2505.21327","title":"MME-Reasoning: A Comprehensive Benchmark for Logical Reasoning in MLLMs","created":"2025-05-27","cited_by_count":33,"area":"lr"},{"id":"2505.11935","title":"ChartEdit: How Far Are MLLMs From Automating Chart Analysis? Evaluating MLLMs' Capability via Chart Editing","created":"2025-05-17","cited_by_count":32,"area":"mllm"},{"id":"2505.13444","title":"ChartMuseum: Testing Visual Reasoning Capabilities of Large Vision-Language Models","created":"2025-05-19","cited_by_count":32,"area":"lr"},{"id":"2410.18798","title":"Distill Visual Chart Reasoning Ability from LLMs to MLLMs","created":"2024-10-24","cited_by_count":31,"area":"lr"},{"id":"2505.18129","title":"One RL to See Them All: Visual Triple Unified Reinforcement Learning","created":"2025-05-23","cited_by_count":30,"area":"lr"},{"id":"2406.19237","title":"FlowVQA: Mapping Multimodal Logic in Visual Question Answering with Flowcharts","created":"2024-06-27","cited_by_count":28,"area":"lr"},{"id":"2511.16672","title":"EvoLMM: Self-Evolving Large Multimodal Models with Continuous Rewards","created":"2025-11-20","cited_by_count":27,"area":"mllm"},{"id":"2507.15509","title":"Chart-R1: Chain-of-Thought Supervision and Reinforcement for Advanced Chart Reasoner","created":"2025-07-21","cited_by_count":25,"area":"lr"},{"id":"2412.11863","title":"GeoX: Geometric Problem Solving Through Unified Formalized Vision-Language Pre-training","created":"2024-12-16","cited_by_count":23,"area":"mllm"},{"id":"2407.07053","title":"Multimodal Self-Instruct: Synthetic Abstract Image and Visual Reasoning Instruction Using Language Model","created":"2024-07-09","cited_by_count":21,"area":"lr"},{"id":"2504.07089","title":"OmniCaptioner: One Captioner to Rule Them All","created":"2025-04-09","cited_by_count":20,"area":null},{"id":"2508.06492","title":"Effective Training Data Synthesis for Improving MLLM Chart Understanding","created":"2025-08-08","cited_by_count":20,"area":"synth"},{"id":"2502.01977","title":"AutoGUI: Scaling GUI Grounding with Automatic Functionality Annotations from LLMs","created":"2025-02-04","cited_by_count":19,"area":"mllm"},{"id":"2508.13587","title":"Breaking the SFT Plateau: Multimodal Structured Reinforcement Learning for Chart-to-Code Generation","created":"2025-08-19","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":"2505.19028","title":"InfoChartQA: A Benchmark for Multimodal Question Answering on Infographic Charts","created":"2025-05-25","cited_by_count":17,"area":"mllm"},{"id":"2508.17608","title":"ChartMaster: Advancing Chart-to-Code Generation with Real-World Charts and Chart Similarity Reinforcement Learning","created":"2025-08-25","cited_by_count":16,"area":null},{"id":"2407.08583","title":"The Synergy between Data and Multi-Modal Large Language Models: A Survey from Co-Development Perspective","created":"2024-07-11","cited_by_count":15,"area":"mllm"},{"id":"2502.14949","title":"KITAB-Bench: A Comprehensive Multi-Domain Benchmark for Arabic OCR and Document Understanding","created":"2025-02-20","cited_by_count":15,"area":null},{"id":"2410.05970","title":"PDF-WuKong: A Large Multimodal Model for Efficient Long PDF Reading with End-to-End Sparse Sampling","created":"2024-10-08","cited_by_count":14,"area":"mllm"},{"id":"2607.04884","title":"HunyuanOCR-1.5: Making Lightweight OCR VLMs Faster and Better","created":"2026-07-06","cited_by_count":14,"area":"mllm"},{"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":"2506.10116","title":"ChartReasoner: Code-Driven Modality Bridging for Long-Chain Reasoning in Chart Question Answering","created":"2025-06-11","cited_by_count":13,"area":"lr"},{"id":"2506.11375","title":"Benchmarking Multimodal LLMs on Recognition and Understanding over Chemical Tables","created":"2025-06-13","cited_by_count":13,"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":"2504.02906","title":"Boosting Chart-to-Code Generation in MLLM via Dual Preference-Guided Refinement","created":"2025-04-03","cited_by_count":11,"area":"mllm"},{"id":"2505.23179","title":"DIP-R1: Deep Inspection and Perception with RL Looking Through and Understanding Complex Scenes","created":"2025-05-29","cited_by_count":11,"area":null},{"id":"2510.23045","title":"A Survey of AI Scientists","created":"2025-10-27","cited_by_count":11,"area":null},{"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":"2503.18435","title":"On the Perception Bottleneck of VLMs for Chart Understanding","created":"2025-03-24","cited_by_count":10,"area":null},{"id":"2601.13606","title":"ChartVerse: Scaling Chart Reasoning via Reliable Programmatic Synthesis from Scratch","created":"2026-01-20","cited_by_count":10,"area":"lr"},{"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":"2506.14837","title":"Improved Iterative Refinement for Chart-to-Code Generation via Structured Instruction","created":"2025-06-15","cited_by_count":9,"area":"mllm"},{"id":"2510.23538","title":"JanusCoder: Towards a Foundational Visual-Programmatic Interface for Code Intelligence","created":"2025-10-27","cited_by_count":9,"area":null},{"id":"2505.23242","title":"ChartMind: A Comprehensive Benchmark for Complex Real-world Multimodal Chart Question Answering","created":"2025-05-29","cited_by_count":7,"area":"lr"},{"id":"2510.04514","title":"ChartAgent: A Multimodal Agent for Visually Grounded Reasoning in Complex Chart Question Answering","created":"2025-10-06","cited_by_count":7,"area":"lr"},{"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":"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":"2503.04095","title":"Chart-HQA: A Benchmark for Hypothetical Question Answering in Charts","created":"2025-03-06","cited_by_count":6,"area":"mllm"},{"id":"2505.12766","title":"Reasoning-OCR: Can Large Multimodal Models Solve Complex Logical Reasoning Problems from OCR Cues?","created":"2025-05-19","cited_by_count":6,"area":"lr"},{"id":"2512.00305","title":"ChartPoint: Guiding MLLMs with Grounding Reflection for Chart Reasoning","created":"2025-11-29","cited_by_count":6,"area":"lr"},{"id":"2502.01702","title":"Al-Khwarizmi: Discovering Physical Laws with Foundation Models","created":"2025-02-03","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":"2506.21319","title":"SimVecVis: A Dataset for Enhancing MLLMs in Visualization Understanding","created":"2025-06-26","cited_by_count":5,"area":"mllm"},{"id":"2506.23061","title":"Empowering Small VLMs to Think with Dynamic Memorization and Exploration","created":"2025-06-29","cited_by_count":5,"area":"lr"},{"id":"2509.17481","title":"ChartHal: A Fine-grained Framework Evaluating Hallucination of Large Vision Language Models in Chart Understanding","created":"2025-09-22","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":"2604.01600","title":"MM-ReCoder: Advancing Chart-to-Code Generation with Reinforcement Learning and Self-Correction","created":"2026-04-02","cited_by_count":5,"area":"mllm"},{"id":"2501.14427","title":"GraphSOS: Graph Sampling and Order Selection to Help LLMs Understand Graphs Better","created":"2025-01-24","cited_by_count":4,"area":null},{"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":"2505.12307","title":"LogicOCR: Do Your Large Multimodal Models Excel at Logical Reasoning on Text-Rich Images?","created":"2025-05-18","cited_by_count":4,"area":"lr"},{"id":"2507.18140","title":"MathOPEval: A Fine-grained Evaluation Benchmark for Visual Operations of MLLMs in Mathematical Reasoning","created":"2025-07-24","cited_by_count":4,"area":"lr"},{"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":"2512.24119","title":"GeoBench: Rethinking Multimodal Geometric Problem-Solving via Hierarchical Evaluation","created":"2025-12-30","cited_by_count":4,"area":"lr"},{"id":"2405.19117","title":"ChartFormer: A Large Vision Language Model for Converting Chart Images into Tactile Accessible SVGs","created":"2024-05-29","cited_by_count":3,"area":null},{"id":"2410.13883","title":"Transformers Utilization in Chart Understanding: A Review of Recent Advances & Future Trends","created":"2024-10-05","cited_by_count":3,"area":null},{"id":"2505.07864","title":"Arrow-Guided VLM: Enhancing Flowchart Understanding via Arrow Direction Encoding","created":"2025-05-09","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":"2507.14819","title":"Doc2Chart: Intent-Driven Zero-Shot Chart Generation from Documents","created":"2025-07-20","cited_by_count":3,"area":null},{"id":"2509.04457","title":"Do MLLMs Really Understand the Charts?","created":"2025-08-27","cited_by_count":3,"area":"lr"},{"id":"2510.00063","title":"AstroMMBench: A Benchmark for Evaluating Multimodal Large Language Models Capabilities in Astronomy","created":"2025-09-29","cited_by_count":3,"area":"mllm"},{"id":"2510.03341","title":"OpusAnimation: Code-Based Dynamic Chart Generation","created":"2025-10-02","cited_by_count":3,"area":null},{"id":"2510.15040","title":"Composition-Grounded Data Synthesis for Visual Reasoning","created":"2025-10-16","cited_by_count":3,"area":"lr"},{"id":"2510.26781","title":"ChartAB: A Benchmark for Chart Grounding & Dense Alignment","created":"2025-10-30","cited_by_count":3,"area":null},{"id":"2503.16260","title":"Chain of Functions: A Programmatic Pipeline for Fine-Grained Chart Reasoning Data","created":"2025-03-20","cited_by_count":2,"area":"lr"},{"id":"2505.23065","title":"SNS-Bench-VL: Benchmarking Multimodal Large Language Models in Social Networking Services","created":"2025-05-29","cited_by_count":2,"area":"mllm"},{"id":"2508.08275","title":"MLLM-CTBench: A Benchmark for Continual Instruction Tuning with Reasoning Process Diagnosis","created":"2025-07-31","cited_by_count":2,"area":"lr"},{"id":"2508.12257","title":"Structuring the Unstructured: A Systematic Review of Text-to-Structure Generation for Agentic AI with a Universal Evaluation Framework","created":"2025-08-17","cited_by_count":2,"area":"agents"},{"id":"2511.02415","title":"ChartM$^3$: A Multi-Stage Code-Driven Pipeline for Constructing Multi-Dimensional and Multi-Step Visual Reasoning Data in Chart Comprehension","created":"2025-11-04","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":"2605.23694","title":"ChartFI: Benchmarking Faithfulness and Insightfulness of Chart Descriptions from Multimodal Large Language Models","created":"2026-05-22","cited_by_count":2,"area":"mllm"}],"next_cursor":"100"}