Think-on-Graph: Deep and Responsible Reasoning of Large Language Model on Knowledge Graph
IDEA Research（IDEA研究院）；International Digital Economy Academy（国际数字经济学院）；Xiamen University（厦门大学）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Microsoft Research Asia（微软亚洲研究院）；University of Southern California（南加州大学）；The Hong Kong University of Science and Technology（香港科技大学）
Although large language models (LLMs) have achieved significant success in various tasks, they often struggle with hallucination problems, especially in scenarios requiring deep and responsible reasoning. These issues could be partially addressed by introducing external knowledge graphs (KG) in LLM reasoning. In this paper, we propose a new LLM-KG integrating paradigm ``$\hbox{LLM}\otimes\hbox{KG}$'' which treats the LLM as an agent to interactively explore related entities and relations on KGs and perform reasoning based on the retrieved knowledge. We further implement this paradigm by introducing a new approach called Think-on-Graph (ToG), in which the LLM agent iteratively executes beam search on KG, discovers the most promising reasoning paths, and returns the most likely reasoning results. We use a number of well-designed experiments to examine and illustrate the following advantages of ToG: 1) compared with LLMs, ToG has better deep reasoning power; 2) ToG has the ability of knowledge traceability and knowledge correctability by leveraging LLMs reasoning and expert feedback; 3) ToG provides a flexible plug-and-play framework for different LLMs, KGs and prompting strategies without any additional training cost; 4) the performance of ToG with small LLM models could exceed large LLM such as GPT-4 in certain scenarios and this reduces the cost of LLM deployment and application. As a training-free method with lower computational cost and better generality, ToG achieves overall SOTA in 6 out of 9 datasets where most previous SOTAs rely on additional training.
Rewarding How Models Think Pedagogically: Integrating Pedagogical Reasoning and Thinking Rewards for LLMs in Education
Chosun University（昌原大学）；Korea University（韩国大学）；Seoul National University（首尔国立大学）；Korea Institute for Curriculum and Evaluation（韩国课程与评价研究院）；Upstage；Indiana University Bloomington（印第安纳大学布卢明顿分校）；Nanyang Technological University（南洋理工大学）
THINK-Bench: Evaluating Thinking Efficiency and Chain-of-Thought Quality of Large Reasoning Models
School of Artificial intelligence, JiLin University（吉林大学人工智能学院）；Engineering Research Center of Knowledge-Driven Human-Machine Intelligence, JiLin University（吉林大学知识驱动人机智能工程研究中心）；International Center of Future Science, JiLin University（吉林大学未来科学国际中心）
ThinkLess: A Training-Free Inference-Efficient Method for Reducing Reasoning Redundancy
National Key Laboratory for Multimedia Information Processing, Peking University（国家多媒体信息处理重点实验室，北京大学）；School of Software and Microelectronics, Peking University（软件与微电子学院，北京大学）；School of Computer Science, Peking University（计算机学院，北京大学）
StreamingThinker: Large Language Models Can Think While Reading
Shanghai Jiao Tong University（上海交通大学）；Eastern Institute of Technology, Ningbo（宁波东部技术研究院）；Ningbo Key Laboratory of Spatial Intelligence and Digital Derivative, Institute of Digital Twin（宁波空间智能与数字衍生关键实验室，数字孪生研究院）；Hong Kong Polytechnic University（香港理工大学）；Munich Center for Machine Learning, LMU（慕尼黑机器学习中心，莱茵-慕尼黑大学）
Latent Thinking Optimization: Your Latent Reasoning Language Model Secretly Encodes Reward Signals in Its Latent Thoughts
Department of Computer Science and Engineering, The Ohio State University, USA（计算机科学与工程系，俄亥俄州立大学）；Department of Biomedical Informatics, The Ohio State University, USA（生物医学信息学系，俄亥俄州立大学）；Translational Data Analytics Institute, The Ohio State University, USA（转化数据分析研究所，俄亥俄州立大学）
Distilling Reasoning Ability from Large Language Models with Adaptive Thinking
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）；College of Intelligence Science and Technology, National University of Defense Technology（智能科学与技术学院，国防科技大学）
Demystifying Reasoning Dynamics with Mutual Information: Thinking Tokens are Information Peaks in LLM Reasoning
Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学北京校区人工智能学院）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；University College London, University of London（伦敦大学大学学院）；Dalian University of Technology（大连理工大学）
Rethinking External Slow-Thinking: From Snowball Errors to Probability of Correct Reasoning
Gaoling School of Artificial Intelligence, Renmin University of China, Beijing, China（中国人民大学人工智能学院）；Beijing Key Laboratory of Research on Large Models（北京大型模型研究关键实验室）；Engineering Research Center of Next-Generation Intelligent Search（下一代智能搜索工程研究中心）；College of Artificial Intelligence, Tianjin University of Science（天津科技大学人工智能学院）
Soft Thinking: Unlocking the Reasoning Potential of LLMs in Continuous Concept Space
University of California, Santa Barbara（加州大学圣芭芭拉分校）；University of California, Santa Cruz（加州大学圣克ruz分校）；University of California, Los Angeles（加州大学洛杉矶分校）；Purdue University（普渡大学）；LMSYS Org（LMSYS组织）；Microsoft（微软）
Think or Not? Exploring Thinking Efficiency in Large Reasoning Models via an Information-Theoretic Lens
Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学高瓴人工智能学院）；Beijing Key Laboratory of Research on Large Models and Intelligent Governance（北京大模型与智能治理研究重点实验室）；Engineering Research Center of Next-Generation Intelligent Search and Recommendation, MOE（教育部新一代智能搜索与推荐工程研究中心）；Tencent Jarvis Lab（腾讯杰维斯实验室）；Medical Artificial Intelligence Lab, Westlake University（西湖大学医学人工智能实验室）
Incentivizing Dual Process Thinking for Efficient Large Language Model Reasoning
Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学人工智能学院）；Department of Computer Science, National University of Singapore（新加坡国立大学计算机科学系）；Ant Group（蚂蚁集团）
CAT: Confidence-Adaptive Thinking for Efficient Reasoning of Large Reasoning Models
Laboratory of Intelligent Collaborative Computing, University of Electronic Science and Technology of China（电子科技大学智能协同计算实验室）；Ubiquitous Intelligence and Trusted Services Key Laboratory of Sichuan Province（四川省泛在智能与可信服务重点实验室）
