{"total":22,"rows":[{"id":"2408.03326","title":"LLaVA-OneVision: Easy Visual Task Transfer","created":"2024-08-06","cited_by_count":2719,"area":"mllm"},{"id":"2406.16860","title":"Cambrian-1: A Fully Open, Vision-Centric Exploration of Multimodal LLMs","created":"2024-06-24","cited_by_count":838,"area":"mllm"},{"id":"2405.02246","title":"What matters when building vision-language models?","created":"2024-05-03","cited_by_count":326,"area":"mllm"},{"id":"2408.12637","title":"Building and better understanding vision-language models: insights and future directions","created":"2024-08-22","cited_by_count":173,"area":"mllm"},{"id":"2409.11402","title":"NVLM: Open Frontier-Class Multimodal LLMs","created":"2024-09-17","cited_by_count":125,"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":"2510.17269","title":"FineVision: Open Data Is All You Need","created":"2025-10-20","cited_by_count":53,"area":"mllm"},{"id":"2407.04923","title":"OmChat: A Recipe to Train Multimodal Language Models with Strong Long Context and Video Understanding","created":"2024-07-06","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":"2503.20502","title":"MLLM-Selector: Necessity and Diversity-driven High-Value Data Selection for Enhanced Visual Instruction Tuning","created":"2025-03-26","cited_by_count":9,"area":"mllm"},{"id":"2503.22577","title":"Breaking Language Barriers in Visual Language Models via Multilingual Textual Regularization","created":"2025-03-28","cited_by_count":4,"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":"2504.12316","title":"Data Metabolism: An Efficient Data Design Schema For Vision Language Model","created":"2025-04-10","cited_by_count":1,"area":"mllm"},{"id":"2508.12512","title":"LangVision-LoRA-NAS: Neural Architecture Search for Variable LoRA Rank in Vision Language Models","created":"2025-08-17","cited_by_count":1,"area":"mllm"},{"id":"2510.21501","title":"GranViT: A Fine-Grained Vision Model With Autoregressive Perception For MLLMs","created":"2025-10-23","cited_by_count":1,"area":"mllm"},{"id":"2602.07790","title":"MaD-Mix: Multi-Modal Data Mixtures via Latent Space Coupling for Vision-Language Model Training","created":"2026-02-08","cited_by_count":1,"area":"mllm"},{"id":"2604.00161","title":"Q-Mask: Query-driven Causal Masks for Text Anchoring in OCR-Oriented Vision-Language Models","created":"2026-03-31","cited_by_count":1,"area":"mllm"},{"id":"2504.12315","title":"Capybara-OMNI: An Efficient Paradigm for Building Omni-Modal Language Models","created":"2025-04-10","cited_by_count":0,"area":"mllm"},{"id":"2602.15769","title":"ViTaB-A: Evaluating Multimodal Large Language Models on Visual Table Attribution","created":"2026-02-17","cited_by_count":0,"area":"mllm"}],"next_cursor":null}