Knowledge-Augmented Long-CoT Generation for Complex Biomolecular Reasoning
The Polytechnic Institute, Zhejiang University（浙江大学Polytechnic学院）；College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University（浙江大学Hangzhou全球科技创新中心）；University of Oxford（牛津大学）；Ant Group（蚂蚁集团）；University of Aberdeen（阿伯丁大学）；ZJU-UIUC Institute, Zhejiang University（浙江大学UIUC研究院）
Understanding complex biomolecular mechanisms requires multi-step reasoning across molecular interactions, signaling cascades, and metabolic pathways. While large language models(LLMs) show promise in such tasks, their application to biomolecular problems is hindered by logical inconsistencies and the lack of grounding in domain knowledge. Existing approaches often exacerbate these issues: reasoning steps may deviate from biological facts or fail to capture long mechanistic dependencies. To address these challenges, we propose a Knowledge-Augmented Long-CoT Reasoning framework that integrates LLMs with knowledge graph-based multi-hop reasoning chains. The framework constructs mechanistic chains via guided multi-hop traversal and pruning on the knowledge graph; these chains are then incorporated into supervised fine-tuning to improve factual grounding and further refined with reinforcement learning to enhance reasoning reliability and consistency. Furthermore, to overcome the shortcomings of existing benchmarks, which are often restricted in scale and scope and lack annotations for deep reasoning chains, we introduce PrimeKGQA, a comprehensive benchmark for biomolecular question answering. Experimental results on both PrimeKGQA and existing datasets demonstrate that although larger closed-source models still perform well on relatively simple tasks, our method demonstrates clear advantages as reasoning depth increases, achieving state-of-the-art performance on multi-hop tasks that demand traversal of structured biological knowledge. These findings highlight the effectiveness of combining structured knowledge with advanced reasoning strategies for reliable and interpretable biomolecular reasoning.
CoT-RAG: Integrating Chain of Thought and Retrieval-Augmented Generation to Enhance Reasoning in Large Language Models
School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China（华中科技大学计算机科学与技术学院）；Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, China（华中科技大学光电实验室）；Department of Computer Science, Bowling Green State University, Ohio, USA（Bowling Green State University 计算机科学系）；Department of Computer Science, Aalborg University, Aalborg, Denmark（Aalborg University 计算机科学系）；Huawei Technologies Co., Ltd, Shenzhen, China（华为技术有限公司）
Integrating Chain-of-Thought and Retrieval Augmented Generation Enhances Rare Disease Diagnosis from Clinical Notes
Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children's Hospital of Philadelphia（费城儿童医院雷蒙德·G·佩雷尔曼细胞与分子治疗中心）；University of Pennsylvania（宾夕法尼亚大学）；Perelman School of Medicine, University of Pennsylvania（宾夕法尼亚大学佩雷尔曼医学院）
Knowledge Graph Augmented Large Language Models for Disease Prediction
Department of Computer Science, Emory University（埃默里大学计算机科学系）；Division of Medical Sciences, Oxford University（牛津大学医学系）；Department of Computer Science and Engineering, UCSC（UCSC计算机科学与工程系）；Nell Hodgson Woodruff School of Nursing, Emory University（埃默里大学内尔·霍根·伍德鲁夫护理学院）；School of Medicine, Emory University（埃默里大学医学院）；Rollins School of Public Health, Emory University（埃默里大学罗林斯公共卫生学院）
Bias-Augmented Consistency Training Reduces Biased Reasoning in Chain-of-Thought
Speechmatics, Apollo Research（Speechmatics与Apollo研究机构）；University of Oxford（牛津大学）；NYU, Anthropic（纽约大学与Anthropic）；NYU（纽约大学）；Anthropic, NYU（Anthropic与纽约大学）
Large Language Model for Discrete Optimization Problems: Evaluation and Step-by-step Reasoning
School of Mathematics, Southeast University（东南大学数学学院）；School of Computer Science and Engineering, Southeast University（东南大学计算机科学与工程学院）；School of Mathematical Sciences, Chongqing Normal University（重庆师范大学数学学院）
Are Tools All We Need? Unveiling the Tool-Use Tax in LLM Agents
University of Houston（休斯顿大学）；University of Southern California（南加州大学）；New York University（纽约大学）；Texas A&M University（德克萨斯农工大学）；University of California, San Diego（加州大学圣地亚哥分校）
MedAlign: A Synergistic Framework of Multimodal Preference Optimization and Federated Meta-Cognitive Reasoning
School of Automation, Guangdong University of Technology（广东工业大学自动化学院）；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）；State Key Laboratory of Traditional Chinese Medicine Syndrome, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangdong Provincial Hospital of Chinese Medicine, Guangdong Provincial Academy of Chinese Medical Sciences（广东省中医药科学院中医证候重点实验室，广州中医药大学第二附属医院，广东省中医院，广东省中医药科学院）；College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算机与数据科学学院）；Department of Electrical and Computer Engineering, Sungkyunkwan University（成均馆大学电子与计算机工程系）
Reflection Pretraining Enables Token-Level Self-Correction in Biological Sequence Models
Fudan University（复旦大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；University of British Columbia（不列颠哥伦比亚大学）；Zhejiang University（浙江大学）；The Chinese University of Hong Kong（香港中文大学）；Stony Brook University（石溪大学）
From PowerPoint UI Sketches to Web-Based Applications: Pattern-Driven Code Generation for GIS Dashboard Development Using Knowledge-Augmented LLMs, Context-Aware Visual Prompting, and the React Framework
LLMs are not Zero-Shot Reasoners for Biomedical Information Extraction
ASUS Intelligent Cloud Services (AICS)（ASUS智能云服务）；Imperial College London（伦敦帝国学院）；University of Manchester（曼彻斯特大学）；National University of Singapore（新加坡国立大学）
