This work proposes DyExpert, a dynamic graph model for cross-domain link prediction. It can explicitly model historical evolving processes to learn the evolution pattern of a specific downstream graph and subsequently make pattern-specific link predictions. DyExpert adopts a decode-only transformer and is capable of efficiently parallel training and inference by \textit{conditioned link generation} that integrates both evolution modeling and link prediction. DyExpert is trained by extensive dynamic graphs across diverse domains, comprising 6M dynamic edges. Extensive experiments on eight untrained graphs demonstrate that DyExpert achieves state-of-the-art performance in cross-domain link prediction. Compared to the advanced baseline under the same setting, DyExpert achieves an average of 11.40% improvement Average Precision across eight graphs. More impressive, it surpasses the fully supervised performance of 8 advanced baselines on 6 untrained graphs.
Estimating link level traffic emissions: enhancing MOVES with open-source data
Department of Civil and Environmental Engineering（土木与环境工程系）；Northeastern University（东北大学）；Massachusetts Institute of Technology（麻省理工学院）；Laboratory for Information & Decision Systems（信息与决策系统实验室）
Beyond Correctness: Confidence-Aware Reward Modeling for Enhancing Large Language Model Reasoning
Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Shanghai Key Laboratory of Data Science, School of Computer Science, Fudan University（上海数据科学关键实验室，计算机科学学院，复旦大学）
Enhancing LLM Reliability via Explicit Knowledge Boundary Modeling
X-LANCE Lab, School of Computer Science MoE Key Lab of Artificial Intelligence, SJTU AI Institute Shanghai Jiao Tong University, Shanghai, China（X-LANCE实验室，计算机科学学院，人工智能关键实验室，上海交通大学人工智能研究院，上海，中国）；Jiangsu Key Lab of Language Computing, Suzhou, China（语言计算重点实验室，苏州，中国）；AISpeech Co., Ltd., Suzhou, China（AISpeech公司，苏州，中国）；Center for Artificial Intelligence Research (CAiRE) Hong Kong University of Science and Technology, Hong Kong, China（人工智能研究中心（CAiRE）香港科技大学，香港，中国）；Suzhou Laboratory, Suzhou, China（苏州实验室，苏州，中国）
Enhancing Clinical Predictive Modeling through Model Complexity-Driven Class Proportion Tuning for Class Imbalanced Data: An Empirical Study on Opioid Overdose Prediction
Enhancing Retrieval-Augmented Generation with Entity Linking for Educational Platforms
Department of Mathematics and Computer Science, University of Catania, Italy（卡塔尼亚大学数学与计算机科学系）；Institute of Cognitive Sciences and Technologies (ISTC), National Research Council of Italy (CNR)（意大利国家研究委员会认知科学与技术研究所（ISTC））
EDELINE: Enhancing Memory in Diffusion-based World Models via Linear-Time Sequence Modeling
Department of Computer Science, National Tsing Hua University（国立清华大学计算机科学系）；Department of Computer Science, National Taiwan University（国立台湾大学计算机科学系）；NVIDIA AI Technology Center (NVAITC), NVIDIA Corporation（NVIDIA人工智能技术中心）
Leveraging Large Language Models for Automated Causal Loop Diagram Generation: Enhancing System Dynamics Modeling through Curated Prompting Techniques
MGH Institute for Technology Assessment, Harvard Medical School（哈佛医学院MGH技术评估研究所）；Melbourne Business School, University of Melbourne（墨尔本大学墨尔本商学院）；Sloan School of Management, Massachusetts Institute of Technology（麻省理工学院斯隆管理学院）
Multivariate Wireless Link Quality Prediction Based on Pre-trained Large Language Models
Beijing University of Posts and Telecommunications（北京邮电大学）；Purple Mountain Laboratories（紫金山实验室）；Institute for Communication Systems, University of Surrey（Surrey大学通信系统研究所）
