Modern multimodal large language models (MLLMs) typically keep the language model fixed and train a visual projector that maps the pixels into a sequence of tokens in its embedding space, so that images can be presented in essentially the same form as text. However, the language model has been optimized to operate on discrete, semantically meaningful tokens, while prevailing visual projectors transform an image into a long stream of continuous and highly correlated embeddings. This causes the visual tokens to behave differently from the word-like units that LLMs are originally trained to understand. We propose a novel Disentangled Visual Tokenization (DiVT) that clusters patch embeddings into coherent semantic units, so each token corresponds to a distinct visual concept instead of a rigid grid cell. DiVT further adapts its token budget to image complexity, providing an explicit accuracy-compute trade-off modifying neither the vision encoder nor the language model. Across diverse multimodal benchmarks, DiVT matches or surpasses baselines with significantly fewer visual tokens, demonstrating robustness under limited token budgets, significantly reducing memory cost and latency while making visual inputs more compatible with LLMs. Our code is available at https://github.com/snuviplab/DiVT.
Beyond Fertility: Analyzing STRR as a Metric for Multilingual Tokenization Evaluation
University of Alberta（阿尔伯塔大学）；Princess Nourah Bint Abdulrahman University（努拉·阿卜杜勒拉赫曼王后大学）；Dialpad Qatar Computing Research Institute（阿联酋迪拜计算研究所）；Amazon AGI（亚马逊人工智能研究所）
AdaTok: Adaptive Token Compression with Object-Aware Representations for Efficient Multimodal LLMs
Institute of Medical Technology, Peking University Health Science Center（北京大学医学部医学技术研究所）；Department of Biomedical Engineering, Peking University（北京大学生物医学工程系）；National Biomedical Imaging Center, Peking University（北京大学国家生物医学成像中心）
Grounding Everything in Tokens for Multimodal Large Language Models
MoE Key Lab of Artificial Intelligence（人工智能混合专家模型关键实验室）；AI Institute, Shanghai Jiao Tong University（上海交通大学人工智能研究院）；Central Research Institute, Huawei（华为中央研究院）
GMoT: Gated Motion-Aware Tokenization for Fine-Grained Micro-Gesture Video Reasoning with Multimodal LLMs
Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳））；Great Bay University（大湾区大学）；Dongguan Key Laboratory for Intelligence and Information Technology（东莞市智能信息技术重点实验室）
MLLM-HWSI: A Multimodal Large Language Model for Hierarchical Whole Slide Image Understanding
Department of Computer Science, Khalifa University of Science and Technology（卡利法科技大学计算机科学系）；Information Technology University（信息技术大学）；KAU（卡乌大学）；University of the Western Australia（西澳大学）
University of Science and Technology of China (USTC)（中国科学技术大学）；Institute of Artificial Intelligence, Hefei Comprehensive National Science Center（合肥综合性国家科学中心人工智能研究院）；Huawei Inc.（华为公司）
In this work, we are dedicated to leveraging the BERT pre-training success and modeling the domain-specific statistics to fertilize the sign language recognition~(SLR) model. Considering the dominance of hand and body in sign language expression, we organize them as pose triplet units and feed them into the Transformer backbone in a frame-wise manner. Pre-training is performed via reconstructing the masked triplet unit from the corrupted input sequence, which learns the hierarchical correlation context cues among internal and external triplet units. Notably, different from the highly semantic …
School of Computer Science and Engineering, Nanjing University of Science and Technology（南京理工大学计算机科学与工程学院）；School of Computer Science and Engineering, Central South University（中南大学计算机科学与工程学院）；College of Information Science and Technology and Artificial Intelligence, Nanjing Forestry University（南京林业大学信息科学技术与人工智能学院）
Harnessing the Power of MLLMs for Transferable Text-to-Image Person ReID
South China University of Technology（华南理工大学）；Pazhou Lab（琶洲实验室）；JD Explore Academy（京东探索研究院）；Yunnan University（云南大学）；Yunnan United Vision Technology Co., Ltd.（云南联慧视觉科技有限公司）
LongHalQA: Long-Context Hallucination Evaluation for MultiModal Large Language Models
Nanyang Technological University（南洋理工大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Zhejiang University of Technology（浙江工业大学）；UCAS（中国科学院大学）
