{"total":39,"rows":[{"id":"2408.01800","title":"MiniCPM-V: A GPT-4V Level MLLM on Your Phone","created":"2024-08-03","cited_by_count":1079,"area":"mllm"},{"id":"2311.06607","title":"Monkey: Image Resolution and Text Label Are Important Things for Large Multi-modal Models","created":"2023-11-11","cited_by_count":357,"area":"mllm"},{"id":"2407.03320","title":"InternLM-XComposer-2.5: A Versatile Large Vision Language Model Supporting Long-Contextual Input and Output","created":"2024-07-03","cited_by_count":195,"area":"mllm"},{"id":"2509.18154","title":"MiniCPM-V 4.5: Cooking Efficient MLLMs via Architecture, Data, and Training Recipe","created":"2025-09-16","cited_by_count":186,"area":"mllm"},{"id":"2307.02499","title":"mPLUG-DocOwl: Modularized Multimodal Large Language Model for Document Understanding","created":"2023-07-04","cited_by_count":168,"area":"mllm"},{"id":"2310.05126","title":"UReader: Universal OCR-free Visually-situated Language Understanding with Multimodal Large Language Model","created":"2023-10-08","cited_by_count":143,"area":"mllm"},{"id":"2402.05935","title":"SPHINX-X: Scaling Data and Parameters for a Family of Multi-modal Large Language Models","created":"2024-02-08","cited_by_count":131,"area":"mllm"},{"id":"2401.16160","title":"LLaVA-MoLE: Sparse Mixture of LoRA Experts for Mitigating Data Conflicts in Instruction Finetuning MLLMs","created":"2024-01-29","cited_by_count":84,"area":"mllm"},{"id":"2409.20566","title":"MM1.5: Methods, Analysis & Insights from Multimodal LLM Fine-tuning","created":"2024-09-30","cited_by_count":76,"area":"mllm"},{"id":"2411.02059","title":"TableGPT2: A Large Multimodal Model with Tabular Data Integration","created":"2024-11-04","cited_by_count":72,"area":"mllm"},{"id":"2404.12803","title":"TextSquare: Scaling up Text-Centric Visual Instruction Tuning","created":"2024-04-19","cited_by_count":62,"area":"mllm"},{"id":"2407.12709","title":"MoME: Mixture of Multimodal Experts for Generalist Multimodal Large Language Models","created":"2024-07-17","cited_by_count":46,"area":"mllm"},{"id":"2404.06918","title":"HRVDA: High-Resolution Visual Document Assistant","created":"2024-04-10","cited_by_count":37,"area":"mllm"},{"id":"2311.18248","title":"mPLUG-PaperOwl: Scientific Diagram Analysis with the Multimodal Large Language Model","created":"2023-11-30","cited_by_count":32,"area":"mllm"},{"id":"2410.05261","title":"TextHawk2: A Large Vision-Language Model Excels in Bilingual OCR and Grounding with 16x Fewer Tokens","created":"2024-10-07","cited_by_count":32,"area":"mllm"},{"id":"2412.09616","title":"V2PE: Improving Multimodal Long-Context Capability of Vision-Language Models with Variable Visual Position Encoding","created":"2024-12-12","cited_by_count":28,"area":"mllm"},{"id":"2407.14439","title":"Token-level Correlation-guided Compression for Efficient Multimodal Document Understanding","created":"2024-07-19","cited_by_count":22,"area":"mllm"},{"id":"2503.14140","title":"Marten: Visual Question Answering with Mask Generation for Multi-modal Document Understanding","created":"2025-03-18","cited_by_count":20,"area":"mllm"},{"id":"2407.08706","title":"HiRes-LLaVA: Restoring Fragmentation Input in High-Resolution Large Vision-Language Models","created":"2024-07-11","cited_by_count":16,"area":"mllm"},{"id":"2405.06706","title":"Exploring the Capabilities of Large Multimodal Models on Dense Text","created":"2024-05-09","cited_by_count":14,"area":"mllm"},{"id":"2503.02304","title":"A Token-level Text Image Foundation Model for Document Understanding","created":"2025-03-04","cited_by_count":14,"area":"mllm"},{"id":"2505.19892","title":"OptMerge: Unifying Multimodal LLM Capabilities and Modalities via Model Merging","created":"2025-05-26","cited_by_count":13,"area":"mllm"},{"id":"2407.19185","title":"LLaVA-Read: Enhancing Reading Ability of Multimodal Language Models","created":"2024-07-27","cited_by_count":12,"area":"mllm"},{"id":"2411.05254","title":"Hierarchical Visual Feature Aggregation for OCR-Free Document Understanding","created":"2024-11-08","cited_by_count":12,"area":"mllm"},{"id":"2503.21817","title":"Skip-Vision: Efficient and Scalable Acceleration of Vision-Language Models via Adaptive Token Skipping","created":"2025-03-26","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":"2411.17125","title":"DOGR: Towards Versatile Visual Document Grounding and Referring","created":"2024-11-26","cited_by_count":10,"area":"mllm"},{"id":"2508.00217","title":"Tabular Data Understanding with LLMs: A Survey of Recent Advances and Challenges","created":"2025-07-31","cited_by_count":10,"area":"mllm"},{"id":"2502.01341","title":"AlignVLM: Bridging Vision and Language Latent Spaces for Multimodal Document Understanding","created":"2025-02-03","cited_by_count":9,"area":"mllm"},{"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":"2405.16234","title":"Vision Language Models for Spreadsheet Understanding: Challenges and Opportunities","created":"2024-05-25","cited_by_count":4,"area":"mllm"},{"id":"2411.07722","title":"Is Cognition Consistent with Perception? Assessing and Mitigating Multimodal Knowledge Conflicts in Document Understanding","created":"2024-11-12","cited_by_count":4,"area":"mllm"},{"id":"2505.09498","title":"Flash-VL 2B: Optimizing Vision-Language Model Performance for Ultra-Low Latency and High Throughput","created":"2025-05-14","cited_by_count":4,"area":"mllm"},{"id":"2407.02842","title":"MindBench: A Comprehensive Benchmark for Mind Map Structure Recognition and Analysis","created":"2024-07-03","cited_by_count":3,"area":"mllm"},{"id":"2509.11662","title":"MindVL: Towards Efficient and Effective Training of Multimodal Large Language Models on Ascend NPUs","created":"2025-09-15","cited_by_count":3,"area":"mllm"},{"id":"2602.07642","title":"Efficient Table Retrieval and Understanding with Multimodal Large Language Models","created":"2026-02-07","cited_by_count":3,"area":"mllm"},{"id":"2408.16986","title":"AdaptVision: Dynamic Input Scaling in MLLMs for Versatile Scene Understanding","created":"2024-08-30","cited_by_count":1,"area":"mllm"},{"id":"2606.09578","title":"TABVERSE: Benchmarking Cross-Format Table Understanding in LLMs and VLMs","created":"2026-06-08","cited_by_count":1,"area":"mllm"},{"id":"2602.19188","title":"PositionOCR: Augmenting Positional Awareness in Multi-Modal Models via Hybrid Specialist Integration","created":"2026-02-22","cited_by_count":0,"area":"mllm"}],"next_cursor":null}