Knowledge graphs represent known facts using triplets. While existing knowledge graph embedding methods only consider the connections between entities, we propose considering the relationships between triplets. For example, let us consider two triplets $T_1$ and $T_2$ where $T_1$ is (Academy_Awards, Nominates, Avatar) and $T_2$ is (Avatar, Wins, Academy_Awards). Given these two base-level triplets, we see that $T_1$ is a prerequisite for $T_2$. In this paper, we define a higher-level triplet to represent a relationship between triplets, e.g., $\langle T_1$, PrerequisiteFor, $T_2\rangle$ where PrerequisiteFor is a higher-level relation. We define a bi-level knowledge graph that consists of the base-level and the higher-level triplets. We also propose a data augmentation strategy based on the random walks on the bi-level knowledge graph to augment plausible triplets. Our model called BiVE learns embeddings by taking into account the structures of the base-level and the higher-level triplets, with additional consideration of the augmented triplets. We propose two new tasks: triplet prediction and conditional link prediction. Given a triplet $T_1$ and a higher-level relation, the triplet prediction predicts a triplet that is likely to be connected to $T_1$ by the higher-level relation, e.g., $\langle T_1$, PrerequisiteFor, ?$\rangle$. The conditional link prediction predicts a missing entity in a triplet conditioned on another triplet, e.g., $\langle T_1$, PrerequisiteFor, (Avatar, Wins, ?)$\rangle$. Experimental results show that BiVE significantly outperforms all other methods in the two new tasks and the typical base-level link prediction in real-world bi-level knowledge graphs.
xERTE: Explainable Reasoning on Temporal Knowledge Graphs for Forecasting Future Links
Institute of Informatics, LMU Munich（慕尼黑大学信息学研究所）；Corporate Technology, Siemens AG（西门子股份公司中央研究院）；Department of Informatics, Technical University of Munich（慕尼黑工业大学信息学系）
Beyond Dense States: Sparse Transcoders as Causally Testable Operators for LLM Latent Reasoning
College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics（南京航空航天大学计算机科学与技术学院）；MIIT Key Laboratory of Pattern Analysis and Machine Intelligence（信息产业部模式分析与机器智能重点实验室）；Institute for AI, Tsinghua University, Beijing, China（清华大学人工智能研究院）
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）
Beyond the Commitment Boundary: Probing Epiphenomenal Chain-of-Thought in Large Reasoning Models
CLCG, University of Groningen（格罗宁根大学CLCG）；University of Milano-Bicocca（米兰-布雷拉大学）；University of Trieste（特里耶大学）；Khoury College of Computer Sciences, Northeastern University（东北大学Khoury计算机科学学院）
Self-Exploring Language Models for Explainable Link Forecasting on Temporal Graphs via Reinforcement Learning
University of Cambridge（剑桥大学）；Mila - Quebec AI Institute（魁北克人工智能研究院）；McGill University（麦吉尔大学）；University of Oxford（牛津大学）；National University of Singapore（新加坡国立大学）；University of Stuttgart（斯图加特大学）；Amazon（亚马逊公司）；AITHYRA
Scaling and Beyond: Advancing Spatial Reasoning in MLLMs Requires New Recipes
Microsoft Research（微软研究院）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Language Technology Lab, University of Cambridge（剑桥大学语言技术实验室）；Nanjing University（南京大学）
Rethinking Reasoning with MDLMs: Early Exits, Post-hoc Reasoning, and Beyond
Columbia University（哥伦比亚大学）；Department of Computer Science, New York University（纽约大学计算机科学系）；Microsoft Research（微软研究院）；Center for Data Science, New York University（纽约大学数据科学中心）
Beyond representational alignment with brain-guided language models for robust reasoning
State Key Lab of General AI, School of Intelligence Science and Technology, Peking University（北京大学通用人工智能国家重点实验室、智能科学与技术学院）；Department of Psychological and Cognitive Sciences, Tsinghua University（清华大学心理与认知科学系）；Microsoft Research Asia（微软亚洲研究院）
Beyond Forgetting: Diagnosing and Harnessing Shared Reasoning in Continual RLVR
State Key Laboratory of General Artificial Intelligence, BIGAI（通用人工智能国家重点实验室（BIGAI））；Beijing Institute of Technology（北京理工大学）；Institute for Artificial Intelligence, Peking University（北京大学人工智能研究院）；State Key Lab of General AI, School of Intelligence Science and Technology, Peking University（通用人工智能国家重点实验室、北京大学智能科学与技术学院）
LINKER: Learning Interactions Between Functional Groups and Residues With Chemical Knowledge-Enhanced Reasoning and Explainability
Department of Computer Science, University of Alabama at Birmingham（阿拉巴马大学伯明翰分校计算机科学系）；Faculty of Computer Science and Engineering, Ho Chi Minh University of Technology（胡志明技术大学计算机科学与工程学院）
Beyond Visual Cues: CoT-Enhanced Reasoning for Semi-supervised Medical Image Segmentation
School of Computer Science and Engineering, Southeast University（东南大学计算机科学与工程学院）；Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications, Ministry of Education（教育部新一代人工智能技术及其跨学科应用重点实验室）；Nanjing University of Science and Technology（南京理工大学）
