Temporal knowledge graph completion (TKGC) has become a popular approach for reasoning over the event and temporal knowledge graphs, targeting the completion of knowledge with accurate but missing information. In this context, tensor decomposition has successfully modeled interactions between entities and relations. Their effectiveness in static knowledge graph completion motivates us to introduce Time-LowFER, a family of parameter-efficient and time-aware extensions of the low-rank tensor factorization model LowFER. Noting several limitations in current approaches to represent time, we propose a cycle-aware time-encoding scheme for time features, which is model-agnostic and offers a more generalized representation of time. We implement our methods in a unified temporal knowledge graph embedding framework, focusing on time-sensitive data processing. The experiments show that our proposed methods perform on par or better than the state-of-the-art semantic matching models on two benchmarks.
Rule Learning for Knowledge Graph Reasoning under Agnostic Distribution Shift
College of Computer Science and Technology, National University of Defense Technology（计算机科学与技术学院，国防科技大学）；Laboratory for Big Data and Decision, National University of Defense Technology（大数据与决策实验室，国防科技大学）；School of Information, Renmin University of China（信息学院，中国人民大学）；National Key Laboratory of Information Systems Engineering, National University of Defense Technology（信息系统工程国家重点实验室，国防科技大学）；Department of Computer Science and Technology, Tsinghua University（计算机科学与技术系，清华大学）；Department of Intelligent Data Science, College of Computer Science and Technology, National University of Defense Technology（智能数据科学系，计算机科学与技术学院，国防科技大学）
The Markovian Thinker: Architecture-Agnostic Linear Scaling of Reasoning
Mila；Microsoft Research（微软研究院）；McGill University（麦吉尔大学）；ServiceNow Research（ServiceNow研究院）；Canada CIFAR AI Chair（加拿大CIFAR人工智能主席）；Chandar Research Lab（Chandar研究实验室）；Polytechnique Montréal（蒙特利尔理工学院）；Université de Montréal（蒙特利尔大学）
Metric-agnostic Learning-to-Rank via Boosting and Rank Approximation
Dept. of Statistics \& Data Science University of Central Florida Orlando, USA；Department of CIS, SKL-IOTSC University of Macau Macao, China；School of Computing；AI Arizona State University Tempe, USA
Re-ranking Reasoning Context with Tree Search Makes Large Vision-Language Models Stronger
School of Artificial Intelligence, University of Chinese Academy of Sciences, China（中国科学院大学人工智能学院）；MAIS, Institute of Automation, Chinese Academy of Sciences, China（中国科学院自动化研究所MAIS部）；Alibaba Cloud Computing, China（阿里巴巴云计算）
LIFT the Veil for the Truth: Principal Weights Emerge after Rank Reduction for Reasoning-Focused Supervised Fine-Tuning
University of California, Berkeley, CA, USA（加州大学伯克利分校）；International Computer Science Institute, CA, USA（国际计算机科学研究所）；Lawrence Berkeley National Laboratory, CA, USA（伯克利国家实验室）；Dartmouth College, NH, USA（达特茅斯学院）；University of Exeter, Exeter, UK（埃克塞特大学）；University of Oxford, Oxford, UK（牛津大学）；University of Surrey, Guildford, UK（萨里大学）；Tsinghua University, China（清华大学）；Eindhoven University of Technology, the Netherlands（埃因霍温理工大学）
