Reasoning post-training improves Large Language Models (LLMs) on complex tasks such as mathematics and coding, but its benefits across diverse multimodal tasks remains uncertain. The trend of releasing parallel "Instruct" and "Thinking" models by leading teams is both resource-intensive and user-unfriendly. Prior work finds that the gains from reasoning training are influenced by multiple factors, such as base model capabilities, task characteristics, and Chain-of-Thought (CoT) data quality. However, principled criteria for determining when reasoning post-training is beneficial and which data should support it are still lacking. In this paper, we propose Dual Tuning, a reasoning efficacy-driven data curation framework for multimodal LLMs training. Given a target task and a base model, Dual Tuning jointly evaluates whether the training data is beneficial and whether reasoning training with current CoT content yields positive gains over non-reasoning alternatives. We apply Dual Tuning across spatial, mathematical, and multi-disciplinary tasks, and further analyze how reinforcement learning and thinking patterns affect reasoning efficacy. The Dual Tuning results guide data curation by identifying data that benefit reasoning training, data better suited to direct-answer training, and data that are detrimental under both training modes. Our work provides quantitative criteria for selecting appropriate training data and matching post-training strategies.
Multimodal Human-AI Synergy for Medical Imaging Quality Control: A Hybrid Intelligence Framework with Adaptive Dataset Curation and Closed-Loop Evaluation
Zhongda Hospital（中大医院）；School of Medicine, Southeast University（东南大学医学院）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）
MINT-1T: Scaling Open-Source Multimodal Data by 10x: A Multimodal Dataset with One Trillion Tokens
University of Washington（华盛顿大学）；Salesforce Research（Salesforce研究院）；University of Texas at Austin（德克萨斯大学奥斯汀分校）；Stanford University（斯坦福大学）；University of California, Berkeley（加州大学伯克利分校）
OphIn-500K: Curating Web-Scale Visual Instructions for Scaling Ophthalmic Multimodal Large Language Models
Arizona State University（亚利桑那州立大学）；Clemson University（克莱姆森大学）；Washington University in St. Louis（圣路易斯华盛顿大学）；University of Notre Dame（诺特丹大学）；Florida State University（佛罗里达州立大学）；Rice University（里德大学）；NVIDIA（英伟达）；Mayo Clinic（梅奥诊所）
From Data Deluge to Data Curation: A Filtering-WoRA Paradigm for Efficient Text-based Person Search
Beijing Institute of Technology（北京理工大学）；National University of Singapore（新加坡国立大学）；University of Macau（澳门大学）；Institute for Clarity in Documentation（文档清晰研究所）；Inria Paris-Rocquencourt（法国国家信息与自动化研究所巴黎-罗康分中心）；Rajiv Gandhi University（拉吉夫·甘地大学）；Tsinghua University（清华大学）；Palmer Research Laboratories（帕尔默研究实验室）
LMOD+: A Comprehensive Multimodal Dataset and Benchmark for Developing and Evaluating Multimodal Large Language Models in Ophthalmology
School of Medicine, Yale University（耶鲁大学医学院）；School of Computing, Australian National University（澳大利亚国立大学计算机学院）；School of Engineering, Imperial College London（伦敦帝国理工学院工程学院）；School of Computing, University of Georgia（佐治亚大学计算机学院）；Yong Loo Lin School of Medicine, National University of Singapore（新加坡国立大学杨秀隆医学学院）；National Eye Institute, National Institutes of Health（美国国立卫生研究院眼科研究所）；National Library of Medicine, National Institutes of Health（美国国立卫生研究院国家医学图书馆）；School of Computing Technologies, RMIT University（皇家墨尔本理工大学计算机技术学院）
OFFSIDE: Benchmarking Unlearning Misinformation in Multimodal Large Language Models
Harbin Institute of Technology（哈尔滨工业大学）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；BIAI, ZJUT & D5Data.ai（BIAI、浙江工业大学及D5Data.ai）；National University of Singapore（新加坡国立大学）
Balancing Efficiency and Efficacy: Training-Free Attention-Guided Switching Between Explicit and Latent Thoughts for MLLMs
Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳））；Tsinghua Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）；Peng Cheng Laboratory（鹏城实验室）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）
OmniEarth-Bench: Towards Holistic Evaluation of Earth's Six Spheres and Cross-Spheres Interactions with Multimodal Observational Earth Data
National University of Defense Technology, China（国防科技大学）；Shanghai Artificial Intelligence Laboratory, China（上海人工智能实验室）；Beijing University of Posts and Telecommunications, China（北京邮电大学）；Zhejiang University, China（浙江大学）；Shanghai Jiao Tong University, China（上海交通大学）；Fudan University, China（复旦大学）；Sun Yat-sen University, China（中山大学）；Nanjing University, China（南京大学）；University of Science and Technology of China（中国科学技术大学）；Wuhan University, China（武汉大学）
Towards On-Policy Data Evolution for Visual-Native Multimodal Deep Search Agents
Hong Kong University of Science and Technology（香港理工大学）；Renmin University of China（中国人民大学）；The Chinese University of Hong Kong（香港中文大学）；Peking University（北京大学）；Tsinghua University（清华大学）；University of Edinburgh（爱丁堡大学）
