The bifurcation of generative modeling into autoregressive approaches for discrete data (text) and diffusion approaches for continuous data (images) hinders the development of truly unified multimodal systems. While Masked Language Models (MLMs) offer efficient bidirectional context, they traditionally lack the generative fidelity of autoregressive models and the semantic continuity of diffusion models. Furthermore, extending masked generation to multimodal settings introduces severe alignment challenges and training instability. In this work, we propose \textbf{CoM-DAD} (\textbf{Co}upled \textbf{M}anifold \textbf{D}iscrete \textbf{A}bsorbing \textbf{D}iffusion), a novel probabilistic framework that reformulates multimodal generation as a hierarchical dual-process. CoM-DAD decouples high-level semantic planning from low-level token synthesis. First, we model the semantic manifold via a continuous latent diffusion process; second, we treat token generation as a discrete absorbing diffusion process, regulated by a \textbf{Variable-Rate Noise Schedule}, conditioned on these evolving semantic priors. Crucially, we introduce a \textbf{Stochastic Mixed-Modal Transport} strategy that aligns disparate modalities without requiring heavy contrastive dual-encoders. Our method demonstrates superior stability over standard masked modeling, establishing a new paradigm for scalable, unified text-image generation.
ChatTime: A Unified Multimodal Time Series Foundation Model Bridging Numerical and Textual Data
Beijing University of Posts and Telecommunications（北京邮电大学）；Pengcheng Laboratory（鹏城实验室）；China Unicom Network Communications Corporation Limited（中国联合网络通信股份有限公司）
Bridging Text and Molecule: A Survey on Multimodal Frameworks for Molecule
Center for Research on Intelligent Perception and Computing (CRIPAC)（智能感知与计算研究中心）；State Key Laboratory of Multimodal Artificial Intelligence Systems (MAIS)（多模态人工智能系统国家重点实验室）；Institute of Automation, Chinese Academy of Sciences (CASIA)（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）
AlignVLM: Bridging Vision and Language Latent Spaces for Multimodal Document Understanding
ServiceNow；York University（约克大学）；Mila – Quebec AI Institute（魁北克人工智能研究院）；École de Technologie Supérieure（魁北克高等技术学院）；Université de Montréal（蒙特利尔大学）；McGill University（麦吉尔大学）；University of Waterloo（滑铁卢大学）；CIFAR AI Chair（CIFAR人工智能 chair）；Polytechnique Montréal（蒙特利尔理工学院）；University of British Columbia（不列颠哥伦比亚大学）
ChatBridge: Bridging Modalities with Large Language Model as a Language Catalyst
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Bytedance Inc.（字节跳动公司）
Bridging the Gap between Synthetic and Authentic Images for Multimodal Machine Translation
Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Beijing Language and Culture University（北京语言大学）
MELLA: Bridging Linguistic Capability and Cultural Groundedness for Low-Resource Language MLLMs
Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；East China Normal University（东华大学）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Institute of High Performance Computing, A*STAR（高性能计算研究所，A*STAR）
Interpretable probabilistic embeddings: bridging the gap between topic models and neural networks
National Research University Higher School of Economics（国立研究大学高等经济学院）；Lomonosov Moscow State University（莫斯科国立大学）；Moscow Institute of Physics and Technology（莫斯科物理技术学院）
Bridging Collaborative Filtering and Large Language Models with Dynamic Alignment, Multimodal Fusion and Evidence-grounded Explanations
Department of Software \& Microelectronics, Peking University, Beijing, China；Economic Law School, China University of Political Science；Financial Media, Peking University, ChangSha, China
HalluShift++: Bridging Language and Vision through Internal Representation Shifts for Hierarchical Hallucinations in MLLMs
Netaji Subhash Engineering College (NSEC)（奈尔贾伊·萨布哈工程学院）；TCG Crest；Electronics and Communication Sciences Unit (ECSU)（电子与通信科学单位）；Indian Statistical Institute（印度统计研究所）
ChartReasoner: Code-Driven Modality Bridging for Long-Chain Reasoning in Chart Question Answering
MAIS, Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所MAIS研究所）；Shenyang Institute of Computing Technology, Chinese Academy of Sciences（中国科学院沈阳计算技术研究所）；Beijing Wenge Technology Co., Ltd.（北京文耕科技有限公司）；University of Chinese Academy of Sciences（中国科学院大学）
Bridging the behavior-neural gap: A multimodal AI reveals the brain's geometry of emotion more accurately than human self-reports
State Key Laboratory of Brain Cognition and Brain-inspired Intelligence Technology, Institute of Automation, Chinese Academy of Sciences（脑认知与脑启发智能技术重点实验室，自动化研究所，中国科学院）；School of Artificial Intelligence, University of Chinese Academy of Sciences（人工智能学院，中国科学院大学）；School of Future Technology, University of Chinese Academy of Sciences（未来技术学院，中国科学院大学）；State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University（认知神经科学与学习国家重点实验室，北京师范大学）
Bridging the Gap between Reality and Ideality of Entity Matching: A Revisiting and Benchmark Re-Construction
Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences（中国科学院大学杭州高等研究院）；Beijing Academy of Artificial Intelligence（北京智源人工智能研究院）；Alibaba Group（阿里巴巴集团）；Institute of Software, Chinese Academy of Sciences（中国科学院软件研究所）
MusiLingo: Bridging Music and Text with Pre-trained Language Models for Music Captioning and Query Response
University of Pennsylvania（宾夕法尼亚大学）；Queen Mary University of London（伦敦大学玛丽皇后学院）；Carnegie Mellon University（卡内基梅隆大学）；University of Texas at Austin（德克萨斯大学奥斯汀分校）；University of Waterloo（滑铁卢大学）
MusiLingo 通过单一投影层将冻结音乐音频模型 MERT 与冻结 LLM 对齐，并构建 MusicInstruct 数据集，实现音乐描述生成与音乐查询响应的性能提升。
