Knowledge Tracing (KT) is a critical task in online education systems, aiming to monitor students' knowledge states throughout a learning period. Common KT approaches involve predicting the probability of a student correctly answering the next question based on their exercise history. However, these methods often suffer from performance degradation when faced with the scarcity of student interactions in new education systems. To address this, we leverage student interactions from existing education systems to mitigate performance degradation caused by limited training data. Nevertheless, these interactions exhibit significant differences since they are derived from different education systems. To address this issue, we propose a domain generalization approach for knowledge tracing, where existing education systems are considered source domains, and new education systems with limited data are considered target domains. Additionally, we design a domain-generalizable knowledge tracing framework (DGKT) that can be applied to any KT model. Specifically, we present a concept aggregation approach designed to reduce conceptual disparities within sequences of student interactions from diverse domains. To further mitigate domain discrepancies, we introduce a novel normalization module called Sequence Instance Normalization (SeqIN). Moreover, to fully leverage exercise information, we propose a new knowledge tracing model tailored for the domain generalization KT task, named Domain-Generalizable Relation-based Knowledge Tracing (DGRKT). Extensive experiments across five benchmark datasets demonstrate that the proposed method performs well despite limited training data.
FETS Benchmark: Foundation Models Enable Scalable and Generalizable Energy Time Series Forecasting
Julius-Maximilians-Universität Würzburg, Modeling and Simulation Lab（乌尔姆-马克斯·普朗克大学，建模与仿真实验室）；Weihenstephan-Triesdorf University of Applied Sciences, Smart Farming（魏因施泰因-特里尔夫应用科学大学，智能农业）；Weihenstephan-Triesdorf University of Applied Sciences, Digital Energy Transition（魏因施泰因-特里尔夫应用科学大学，数字能源转型）
Understanding Task Aggregation for Generalizable Ultrasound Foundation Models
Research Ireland Centre for Research Training in Machine Learning Departament de Matem\`atiques i Inform\`atica, Universitat de Barcelona, Barcelona, Spain School of Medicine, University College Dublin, Dublin, Ireland School of Computer Science, University College Dublin, Dublin, Ireland Instituci\'o Catalana de Recerca i Estudis Avan c ats (ICREA) Department of Cardiovascular Surgery, The First Affiliated Hospital of Jinan University, Jinan University, Guangzhou, China Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand Equal contribution
Tracing the Roots: A Multi-Agent Framework for Uncovering Data Lineage in Post-Training LLMs
Shanghai Artificial Intelligence Laboratory, OpenDataLab（上海人工智能实验室，OpenDataLab）；University of Science and Technology of China（中国科学技术大学）；Shanghai Jiao Tong University（上海交通大学）
