HiQA: A Hierarchical Contextual Augmentation RAG for Multi-Documents QA
Nanjing University of Aeronautics and Astronautics（南京航空航天大学）；Southeast University（东南大学）；Hello World(Shanghai) Technology Co., Ltd.（Hello World（上海）科技有限公司）
Xinyue Chen, Pengyu Gao, Jiangjiang Song, Xinjian Chen, Xiaoyang Tan
备注：
Metadata correction: the arXiv author metadata has been updated to include Xinjian Chen, consistent with the author list already present in the v3 manuscript files. The manuscript content is unchanged
Retrieval-Augmented Generation (RAG) significantly improves document-based question answering by integrating external documents during generation. However, retrieval accuracy can degrade when the knowledge base contains many semantically and structurally similar documents. We introduce HiQA, a practical hierarchical contextual augmentation framework for multi-document question answering (MDQA). HiQA enriches text chunks with cascading document metadata, such as document titles and section paths, so that retrieval can use both local content and document structure. The framework also uses a multi-route retriever that combines semantic, lexical, and keyword/entity signals. We further introduce MasQA, a benchmark designed to evaluate MDQA systems in realistic similar-document settings. Experiments show that HiQA improves retrieval and answer quality on MasQA and remains competitive on public MDQA benchmarks, while its benefits are strongest for structured, domain-specific, highly similar document collections.
Personalizing Student-Agent Interactions Using Log-Contextualized Retrieval-Augmented Generation (RAG)
1Department of Computer Science, Vanderbilt University, Nashville, USA 2College of Engineering \& Science, University of Detroit Mercy, Detroit, USA 3The School for Science；Math, Vanderbilt University, Nashville, USA
PACE-RAG: Patient-Aware Contextual and Evidence-Constrained RAG for Clinical Drug Recommendation
Korea Advanced Institute of Science and Technology（韩国科学技术院）；Department of Neurology, Seoul National University Hospital（首尔国立大学医院神经科）；Asan Medical Center, University of Ulsan, College of Medicine（釜山大学医学院阿桑医院）；Haeundae Paik Hospital, Inje University（庆尚大学医院海undae医院）
Privacy-Preserving RAG via Multi-Agent Semantic Rewriting: Achieving Confidentiality Without Compromising Contextual Fidelity
School of Control and Computer Engineering, North China Electric Power University（华北电力大学控制与计算机工程学院）；Department of Computer & Information Science & Engineering, University of Florida（佛罗里达大学计算机与信息科学与工程系）；NLP2CT Lab, Department of Computer and Information Science, University of Macau（澳门大学计算机与信息科学系NLP2CT实验室）；Institute of International Language Services Studies, Macau Millennium College（澳门千禧学院国际语言服务研究所）
Culinary Crossroads: A RAG Framework for Enhancing Diversity in Cross-Cultural Recipe Adaptation
Aarhus University（奥胡斯大学）；University of Copenhagen（哥本哈根大学）；Dept. of Computer Science and Artificial Intelligence, University of Granada（格拉纳达大学计算机科学与人工智能系）；Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））
DioR: Adaptive Cognitive Detection and Contextual Retrieval Optimization for Dynamic Retrieval-Augmented Generation
Zhejiang Key Laboratory of Intelligent Education Technology and Application, Zhejiang Normal University（浙江智能教育技术与应用重点实验室，浙江师范大学）；School of Computer Science and Technology, Zhejiang Normal University（浙江师范大学计算机科学与技术学院）；School of Computer Science and Engineering, Southeast University（东南大学计算机科学与工程学院）；Department of Computer Science and Engineering, Hong Kong University of Science and Technology（香港科技大学计算机科学与工程系）；School of Computer Science and Technology, Soochow University（苏州大学计算机科学与技术学院）
KG-CQR: Leveraging Structured Relation Representations in Knowledge Graphs for Contextual Query Retrieval
Viettel AI, Viettel Group（越南 Viettel 集团 AI 部门）；University of Engineering and Technology, Vietnam National University, Hanoi（越南国家大学河内工程大学）；Department of Computer Engineering, Chung-Ang University（Chung-Ang 大学计算机工程系）
Automating Bibliometric Analysis with Sentence Transformers and Retrieval-Augmented Generation (RAG): A Pilot Study in Semantic and Contextual Search for Customized Literature Characterization for High-Impact Urban Research
Oak Ridge National Laboratory（橡树岭国家实验室）；The University of Tennessee（田纳西大学）
RAG-HAR: Retrieval Augmented Generation-based Human Activity Recognition
University of Sydney Australia（悉尼大学澳大利亚分校）；University of Sri Jayewardenepura Sri Lanka（Sri Jayewardenepura大学 Sri Lanka）；Curtin University Australia（Curtin大学澳大利亚分校）；Colorado State University USA（科罗拉多州立大学美国分校）
Agentic Retrieval-Augmented Generation: A Survey on Agentic RAG
Cleveland State University（克利夫兰州立大学）；Kent State University（肯特州立大学）；Northeastern University（东北大学）；Roux Institute at Northeastern University（东北大学鲁克斯研究所）；University of Agder (UiA)（阿格德尔大学）
Open-RAG: Enhanced Retrieval-Augmented Reasoning with Open-Source Large Language Models
Bangladesh University of Engineering and Technology（孟加拉工程技术大学）；Cohere For AI（Cohere AI研究团队）；University of North Texas（北得克萨斯大学）；York University（约克大学）；Salesforce Research（Salesforce研究院）；Qatar Computing Research Institute (QCRI)（卡塔尔计算研究所）
CausalRAG: Integrating Causal Graphs into Retrieval-Augmented Generation
Department of Computer and Data Sciences, Case Western Reserve University（计算机与数据科学系，凯斯西储大学）；Department of Design and Innovation, Case Western Reserve University（设计与创新系，凯斯西储大学）
Position: LLMs Must Use Functor-Based and RAG-Driven Bias Mitigation for Fairness
Knight Foundation School of Computing and Information Sciences（骑士基金会计算与信息科学学院）；Florida International University（佛罗里达国际大学）；College of Engineering（工程学院）；University of South Florida（佛罗里达州立大学）
Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；The Hong Kong University of Science and Technology（香港科技大学）
