While existing time series foundation models primarily rely on large-scale unimodal pretraining, they lack complementary modalities to enhance time series understanding. Building multimodal foundation models is a natural next step, but it faces key challenges: 1) lack of a unified multimodal pretraining paradigm and large-scale multimodal corpora for time series analysis; 2) how to effectively integrate heterogeneous modalities and enhance model generalization. To address these challenges, we take an early step toward multimodal foundation models for time series analysis. We first propose a multimodal pretraining paradigm that leverages time series with endogenous modalities (derived images and text) and exogenous knowledge (real-world news), providing a comprehensive multi-view perspective for time series analysis. To support this, we develop an automated data construction pipeline to curate MM-TS, the first large-scale multimodal time series dataset spanning six domains, with up to one billion points. Then we propose HORAI, a frequency-enhanced multimodal foundation model. It integrates two core components: the Frequency-enhanced Cross-Modality Encoder and the Time-Frequency Decoder, designed to effectively fuse multimodal features and enhance model generalization across modalities and domains. After pretraining on MM-TS, HORAI achieves state-of-the-art zero-shot performance on time series forecasting and anomaly detection tasks, demonstrating strong generalization.
ForgerySleuth: Empowering Multimodal Large Language Models for Image Manipulation Detection
Shanghai Key Lab of Intell. Info. Processing, School of CS, Fudan University（上海智能信息处理关键实验室，复旦大学计算机学院）；Shanghai Collaborative Innovation Center of Intelligent Visual Computing（上海智能视觉计算协同创新中心）
Empowering Time Series Analysis with Synthetic Data: A Survey and Outlook in the Era of Foundation Models
Salesforce AI Research（Salesforce AI研究院）；National University of Singapore（新加坡国立大学）；Squirrel Ai Learning（松鼠人工智能学习）；Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））
PRIME: Prototype-Driven Multimodal Pretraining for Cancer Prognosis with Missing Modalities
University of Minnesota（明尼苏达大学）；University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；North Carolina State University（北卡罗来纳州立大学）；National University of Singapore（新加坡国立大学）；Institute of High Performance Computing, Agency for Science, Technology and Research（高性能计算研究所，新加坡科技研究局）
Empowering VLMs for Few-Shot Multimodal Time Series Classification via Tailored Agentic Reasoning
Sun Yat-sen University（中山大学）；Xiaomi Corporation（小米公司）；University of Science and Technology of China（中国科学技术大学）；National University of Singapore（新加坡国立大学）
CVSearch: Empowering Multimodal LLMs with Cognitive Visual Search for High-Resolution Image Perception
Harbin Institute of Technology, Shenzhen, China（哈尔滨工业大学（深圳））；Peng Cheng Laboratory, Shenzhen, China（鹏城实验室）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China（深圳先进技术研究院）
A Practitioner's Guide to Continual Multimodal Pretraining
Tübingen AI Center（蒂宾根人工智能中心）；University of Tübingen（蒂宾根大学）；Helmholtz Munich（亥姆霍兹慕尼黑中心）；University of Cambridge（剑桥大学）；LAION；Jülich Supercomputing Center (JSC)（于利希超级计算中心）；Research Center Jülich (FZJ)（于利希研究中心）；Helmholtz Association（亥姆霍兹联合会）；Google DeepMind（谷歌DeepMind）；Munich Center for ML（慕尼黑机器学习中心）；Technical University of Munich（慕尼黑工业大学）
EEG-Language Pretraining for Highly Label-Efficient Clinical Phenotyping
Charité – Universitätsmedizin Berlin, Department of Psychiatry and Psychotherapy, Berlin, Germany（柏林查理医院医学大学精神病与心理治疗系）；Hertie Institute for AI in Brain Health, University of Tübingen, Germany（图宾根大学健康人工智能研究所）
Explore the Limits of Omni-modal Pretraining at Scale
The Chinese University of Hong Kong（香港中文大学）；University of Chinese Academy of Sciences（中国科学院大学）；Chinese Academy of Science（中国科学院）；Shanghai AI Laboratory（上海人工智能实验室）
A Step Toward Federated Pretraining of Multimodal Large Language Models
MAIS, Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所多模态人工智能系统实验室）；King Abdullah University of Science and Technology (KAUST)（阿卜杜拉国王科技大学）；Pengcheng Laboratory（鹏城实验室）；School of Artificial Intelligence, University of Chinese Academy of Sciences (UCAS)（中国科学院大学人工智能学院）
SpectralEarth-FM: Bringing Hyperspectral Imagery into Multimodal Earth Observation Pretraining
Chair of Data Science in Earth Observation（地球观测数据科学主任）；Technical University of Munich（慕尼黑技术大学）；Remote Sensing Technology Institute（遥感技术研究所）；German Aerospace Center (DLR)（德国航空航天中心）；Department of Aerospace Engineering（航空航天工程系）；University of the Bundeswehr Munich（联邦国防军慕尼黑大学）；LEAP；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心）；Univ. Grenoble Alpes（格勒诺布尔阿尔卑斯大学）；Inria（法国国家信息与自动化技术研究院）；CNRS（法国国家科学研究中心）；Grenoble INP（格勒诺布尔INP）；LJK
Multimodal Molecular Pretraining via Modality Blending
Institute for AI Industry Research, Tsinghua University（清华大学人工智能产业研究院）；Harbin Institute of Technology（哈尔滨工业大学）；Academy of Mathematics and Systems Science, Chinese Academy of Sciences（中国科学院数学与系统科学研究院）
VALOR: Vision-Audio-Language Omni-Perception Pretraining Model and Dataset
School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Nanjing University of Science and Technology（南京理工大学）
International Digital Economy Academy (IDEA)（国际数字经济学院（IDEA））；Hong Kong University of Science and Technology（香港科技大学）；Yale University（耶鲁大学）；National University of Singapore（新加坡国立大学）
LVRPO: Language-Visual Alignment with GRPO for Multimodal Understanding and Generation
Department of Machine Learning, CMU, USA（卡内基梅隆大学机器学习系，美国）；Department of Machine Learning, MBZUAI, UAE（穆罕默德·本·扎耶德人工智能大学机器学习系，阿联酋）；Department of Artificial Intelligence, Hanyang University ERICA, South Korea（汉阳大学ERICA校区人工智能系，韩国）
