Large time series models (LTMs) have emerged as powerful tools for universal forecasting, yet they often struggle with the inherent diversity and nonstationarity of real-world time series data, leading to an unsatisfactory trade-off between forecasting accuracy and generalization. Rather than continually finetuning new LTM instances for each domain, we propose a data-centric framework, time-series adaptive transformation optimization (TATO), that enables a single frozen pre-trained LTM to adapt to diverse downstream domains through an optimally configured transformation pipeline. Specifically, TATO constructs three representative types of transformations, including context slicing, scale normalization, and outlier correction, to help LTMs better align with target domain characteristics. To ensure robustness, we incorporate carefully selected time series augmentations and a two-stage ranking mechanism that filters out pipelines underperforming on specific metrics. Extensive experiments on state-of-the-art LTMs and widely used datasets demonstrate that TATO consistently and significantly improves domain-adaptive forecasting performance, achieving a maximum reduction in MSE of 65.4\% and an average reduction of 13.6\%. Moreover, TATO is highly efficient, typically completing optimization in under 2 minutes, making it practical for real-world deployment. The source code is available at https://github.com/thulab/TATO.
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）
HeTa: Relation-wise Heterogeneous Graph Foundation Attack Model
School of Cyberspace, Hangzhou Dianzi University（电子科技大学信息学院）；Data Security Governance Zhejiang Engineering Research Center, Hangzhou Dianzi University（浙江省数据安全治理工程研究中心）；Beihang University（北航）
针对异质图神经网络攻击难以跨模型与跨图泛化的问题，提出 HeTa 基础攻击模型，利用共享关系感知攻击单元和逐关系序列化攻击实现可迁移、易适配的强攻击。
TAFFY: A Task-Adaptive Tabular Foundation Model with In-Context Diversity
Carnegie Mellon University（卡内基梅隆大学）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；Guangdong University of Technology（广东工业大学）；University of California, San Diego（加利福尼亚大学圣迭戈分校）
H2Tune: Federated Foundation Model Fine-Tuning with Hybrid Heterogeneity
School of Artificial Intelligence, Beihang University（北京航空航天大学人工智能学院）；School of Computer Science and Technology, Heilongjiang University（黑龙江大学计算机科学与技术学院）；School of Computer Science and Engineering, Beihang University（北京航空航天大学计算机科学与工程学院）；School of Statistics, Renmin University of China（中国人民大学统计学院）；Zhongguancun Laboratory, China（中关村实验室）；School of Computer Science and Technology, Shandong University（山东大学计算机科学与技术学院）；WeBank Co., Ltd, Shenzhen, China（WeBank有限公司，深圳，中国）；Beijing Academy of Blockchain and Edge Computing（北京区块链与边缘计算研究院）
Low-Rank Adaptation for Foundation Models: A Comprehensive Review
Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Yale University（耶鲁大学）；Nanyang Technological University（南洋理工大学）；Central University of Finance and Economics（中央财经大学）；The Chinese University of Hong Kong（香港中文大学）；Xi’an University of Electronic Science and Technology（西安电子科技大学）；University of California, Davis（加州大学戴维斯分校）；The University of Cambridge（剑桥大学）；LOGS AI
LatentVerse: A Framework for Understanding Shared and Modality-Specific Information in Multimodal Latent Representations
Broad Institute of MIT and Harvard（麻省理工学院和哈佛大学布罗德研究所）；Massachusetts Institute of Technology（麻省理工学院）；The Schmidt Center, Broad Institute of MIT and Harvard（麻省理工学院和哈佛大学布罗德研究所施密特中心）
Advances in Multimodal Adaptation and Generalization: From Traditional Approaches to Foundation Models
Technical University of Munich（慕尼黑工业大学）；Hong Kong Baptist University（香港浸会大学）；Aalto University（阿尔托大学）；University of Oulu（奥卢大学）；University of Bonn（波恩大学）；EPFL（洛桑联邦理工学院）
ASLSL: Adaptive shared latent structure learning with incomplete multi-modal physiological data for multi-dimensional emotional feature selection
School of Information Science and Technology, Beijing University of Technology, Beijing 100124, China（信息科学与技术学院，北京理工大学，北京）；School of Artificial Intelligence, Anhui University, Beijing 100124, China（人工智能学院，安徽大学，北京）
Personalized Federated Sparse Adaptation of Time-Series Foundation Models
Robert Bosch Centre for Cyber-Physical Systems, Indian Institute of Science（罗伯特·博世网络物理系统中心，印度科学学院）；School of Computing, Newcastle University（纽卡斯尔大学计算学院）
