DeepRAG: Thinking to Retrieve Step by Step for Large Language Models
Chinese Information Processing Laboratory, Institute of Software, Chinese Academy of Sciences（中国科学院软件研究所信息处理实验室）；University of Chinese Academy of Sciences（中国科学院大学）；Pattern Recognition Center, WeChat AI, Tencent Inc, China（腾讯人工智能部模式识别中心）
Xinyan Guan, Jiali Zeng, Fandong Meng, Chunlei Xin, Yaojie Lu, Hongyu Lin, Xianpei Han, Le Sun, Jie Zhou
Large Language Models (LLMs) have shown remarkable reasoning capabilities, while their practical applications are limited by severe factual hallucinations due to limitations in the timeliness, accuracy, and comprehensiveness of their parametric knowledge. Meanwhile, enhancing retrieval-augmented generation (RAG) with reasoning remains challenging due to ineffective task decomposition and redundant retrieval, which can introduce noise and degrade response quality. In this paper, we propose DeepRAG, a framework that models retrieval-augmented reasoning as a Markov Decision Process (MDP), enabling reasonable and adaptive retrieval. By iteratively decomposing queries, DeepRAG dynamically determines whether to retrieve external knowledge or rely on parametric reasoning at each step. Experiments show that DeepRAG improves retrieval efficiency and boosts answer accuracy by 26.4%, demonstrating its effectiveness in enhancing retrieval-augmented reasoning.
Exploring Knowledge Conflicts for Faithful LLM Reasoning: Benchmark and Method
School of Computer Science and Technology, Xi'an Jiaotong University（西安交通大学计算机科学与技术学院）；Department of Computer Science, The University of Manchester（曼彻斯特大学计算机科学系）；Hunan University（湖南大学）；National University of Singapore（新加坡国立大学）
S3C-LLM: Skill-Code Guided Agentic Language Models for Spectrum-to-Structure Elucidation
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）
Thinking Beyond Videos: Unifying Video Reasoning and Deep Research for Open-World Video Agents
Shandong University（山东大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Beihang University（北京航空航天大学）；City University of Hong Kong（香港城市大学）；Nanyang Technological University（南洋理工大学）；Kuaishou Technology（快手科技）；Southern University of Science and Technology（南方科技大学）
A State-Transition Framework for Efficient LLM Reasoning
School of Informatics, Xiamen University（厦门大学信息学院）；Alibaba International Digital Commerce（阿里巴巴国际数字商务）；Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan (Xiamen University), Ministry of Culture and Tourism, China（福建省和台湾非物质文化遗产数字化保护与智能处理重点实验室（厦门大学），文化和旅游部，中国）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）
Privacy-Preserving Local Language Models for Longitudinal Data Retrieval in Chronic Dermatologic Disease: Implementation in Pemphigus Patients
Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College London（系统医学系，代谢、消化与生殖部，帝国理工学院伦敦分校）；Department of Dermatology and Venereology, Istanbul Research and Training Hospital（皮肤科与性病科，伊斯坦布尔研究与培训医院）；Department of Dermatology and Venereology, Istanbul Medeniyet University（皮肤科与性病科，伊斯坦布尔梅德尼yet大学）；Department of Dermatology and Venereology, Istanbul Medicana Atakoy Hospital（皮肤科与性病科，伊斯坦布尔Medicana阿塔科伊医院）
