Time-series foundation models (TSFMs) have demonstrated strong generalization capabilities across diverse datasets and tasks. However, existing foundation models are typically pre-trained to enhance performance on specific tasks and often struggle to generalize to unseen tasks without fine-tuning. To address this limitation, we propose augmenting TSFMs with In-Context Learning (ICL) capabilities, enabling them to perform test-time inference by dynamically adapting to input-output relationships provided within the context. Our framework, In-Context Time-series Pre-training (ICTP), restructures the original pre-training data to equip the backbone TSFM with ICL capabilities, enabling adaptation to unseen tasks. Experiments demonstrate that ICT improves the performance of state-of-the-art TSFMs by approximately 11.4% on unseen tasks without requiring fine-tuning.
Breaking the Reasoning Horizon in Entity Alignment Foundation Models
Nanjing University of Information Science and Technology（南京信息工程大学）；State Key Laboratory for Novel Software Technology, Nanjing University（南京大学软件新技术国家重点实验室）；National Institute of Healthcare Data Science, Nanjing University（南京大学健康数据科学国家研究院）；University of Queensland（昆士兰大学）；Engineering Research Center of Digital Forensics, Ministry of Education, Nanjing University of Information Science and Technology（南京信息工程大学数字取证工程研究中心）
How Foundational are Foundation Models for Time Series Forecasting?
Univ. Rennes（里昂大学）；CNRS（法国国家科学研究中心）；Inria（法国国家信息与自动化技术研究所）；IRISA - UMR 6074（国际信息与自动化研究所）；Univ. Grenoble Alpes（格勒诺布尔阿尔卑斯大学）；Univ. Savoie Mont Blanc（萨瓦伊-蒙布莱大学）；LPNC（实验室）；CEA（法国原子能委员会）；LIST（法国国家信息与自动化技术研究所）
TimeFound: A Foundation Model for Time Series Forecasting
Baidu Research（百度研究院）；University of Science and Technology of China（中国科学技术大学）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））
Selective Test-Time Adaptation for Unsupervised Anomaly Detection using Neural Implicit Representations
School of Computation, Information and Technology, Technical University of Munich（慕尼黑工业大学计算、信息与技术学院）；Institute of Machine Learning in Biomedical Imaging, Helmholtz Munich（亥姆霍兹慕尼黑生物医学成像机器学习研究所）；School of Biomedical Engineering and Imaging Sciences, King’s College London（伦敦国王学院生物医学工程与影像科学学院）
Towards Foundation Models for Zero-Shot Time Series Anomaly Detection: Leveraging Synthetic Data and Relative Context Discrepancy
Department of Industrial Engineering, Tsinghua University, Beijing, China（清华大学工业工程系）；Datadog AI Research, Paris, France. This work was completed prior to joining Datadog（Datadog AI 研究院）；Lab, Huawei Technologies, ShenZhen, China（华为技术2012实验室）
X-Prompt: Towards Universal In-Context Image Generation in Auto-Regressive Vision Language Foundation Models
Shanghai Jiao Tong University（上海交通大学）；Shanghai AI Laboratory（上海人工智能实验室）；Tsinghua University（清华大学）；The Chinese University of Hong Kong（香港中文大学）；CPII under InnoHK（创新香港科技促进会）；MThreads AI
A Multi-Center Study on the Adaptability of a Shared Foundation Model for Electronic Health Records
Program in Child Health Evaluative Sciences, The Hospital for Sick Children, Toronto, ON, 1 Canada（儿童健康评估科学计划，多伦多医院儿童医学中心，多伦多，加拿大）；Stanford Center for Biomedical Informatics Research, Stanford University, Palo Alto, CA, USA（斯坦福大学生物医学信息学研究中心，斯坦福大学，帕洛阿尔托，美国）；Universidad del Norte, Barranquilla, Columbia（北方大学，巴兰基利亚，哥伦比亚）；Division of Haematology/Oncology, The Hospital for Sick Children, Toronto, ON, Canada（血液肿瘤科，多伦多医院儿童医学中心，多伦多，加拿大）
