RAG / 检索增强生成

AirRAG: Autonomous Strategic Planning and Reasoning Steer Retrieval Augmented Generation

Wenfeng Feng, Chuzhan Hao, Yuewei Zhang, Guochao Jiang, Jingyi Song, Hao Wang

机构 * Alibaba Cloud Computing（阿里巴巴云 computing） 纠错

专题命中 检索器与排序 ：retrieval augmented generation(title)；retrieval-augmented generation(abstract)；RAG(abstract)；分类 cs.AI

Comments 20 pages, EMNLP25 Accepted

Evaluating Retrieval-Augmented Generation Strategies for Large Language Models in Travel Mode Choice Prediction

Yiming Xu, Junfeng Jiao

机构 * School of Architecture, The University of Texas at Austin（德克萨斯大学建筑学院） 纠错

专题命中 检索器与排序 ：retrieval-augmented generation(title,abstract)；RAG(abstract)；分类 cs.AI

Taming the Untamed: Graph-Based Knowledge Retrieval and Reasoning for MLLMs to Conquer the Unknown

Bowen Wang, Zhouqiang Jiang, Yasuaki Susumu, Shotaro Miwa, Tianwei Chen, Yuta Nakashima

机构 * Osaka University（大阪大学） ； Mitsubishi Electric Corp.（三菱电机公司） 纠错

专题命中 检索器与排序 ：knowledge retrieval(title,abstract)；retriever(abstract)；分类 cs.AI

Comments Aligned with ICCV 2025 camera-ready version

An Embodied AR Navigation Agent: Integrating BIM with Retrieval-Augmented Generation for Language Guidance

Hsuan-Kung Yang, Tsu-Ching Hsiao, Ryoichiro Oka, Ryuya Nishino, Satoko Tofukuji, Norimasa Kobori

机构 * Woven by Toyota, Inc., Japan（丰田公司，日本） 纠错

专题命中 检索器与排序 ：retrieval-augmented generation(title,abstract)；RAG(abstract)；分类 cs.AI

Comments 11 pages, 9 figures, accepted to IEEE ISMAR 2025

QU-NLP at QIAS 2025 Shared Task: A Two-Phase LLM Fine-Tuning and Retrieval-Augmented Generation Approach for Islamic Inheritance Reasoning

Mohammad AL-Smadi

机构 * Qatar University（卡塔尔大学） 纠错

专题命中 检索器与排序 ：retrieval-augmented generation(title,abstract)；RAG(abstract)；分类 cs.CL

Understanding the Impact of Confidence in Retrieval Augmented Generation: A Case Study in the Medical Domain

Shintaro Ozaki, Yuta Kato, Siyuan Feng, Masayo Tomita, Kazuki Hayashi, Wataru Hashimoto, Ryoma Obara, Masafumi Oyamada, Katsuhiko Hayashi, Hidetaka Kamigaito, Taro Watanabe

机构 * Nara Institute of Science and Technology（奈良科学技术研究所） ； The University of Tokyo（东京大学） ； NEC Corporation（日本电气株式会社） 纠错

专题命中 检索器与排序 ：retrieval augmented generation(title,abstract)；RAG(abstract)；分类 cs.CL

Comments Accepted to BioNLP2025 (Workshop colocated with ACL2025)

GridCodex: A RAG-Driven AI Framework for Power Grid Code Reasoning and Compliance

Jinquan Shi, Yingying Cheng, Fan Zhang, Miao Jiang, Jun Lin, Yanbai Shen

专题命中 检索器与排序 ：RAG(title,abstract)；retrieval-augmented generation(abstract)；分类 cs.AI

RAG for Geoscience: What We Expect, Gaps and Opportunities

Runlong Yu, Shiyuan Luo, Rahul Ghosh, Lingyao Li, Yiqun Xie, Xiaowei Jia

专题命中 检索器与排序 ：RAG(title,abstract)；retrieval-augmented generation(abstract)；分类 cs.IR

Transforming Questions and Documents for Semantically Aligned Retrieval-Augmented Generation

Seokgi Lee

机构 * Seokgi Lee 纠错

专题命中 检索器与排序 ：retrieval-augmented generation(title,abstract)；RAG(abstract)；分类 cs.CL

RankArena: A Unified Platform for Evaluating Retrieval, Reranking and RAG with Human and LLM Feedback

Abdelrahman Abdallah, Mahmoud Abdalla, Bhawna Piryani, Jamshid Mozafari, Mohammed Ali, Adam Jatowt

专题命中 检索器与排序 ：RAG(title,abstract)；retrieval-augmented generation(abstract)；分类 cs.IR

Comments Accept at CIKM 2025

Can Language Models Critique Themselves? Investigating Self-Feedback for Retrieval Augmented Generation at BioASQ 2025

Samy Ateia, Udo Kruschwitz

机构 * Information Science, University of Regensburg, Universitätsstraße 31, 93053, Regensburg, Germany（信息科学，莱茵河畔雷根斯堡大学） 纠错

专题命中 检索器与排序 ：retrieval augmented generation(title,abstract)；RAG(abstract)；分类 cs.CL

Comments Version as accepted at the BioASQ Lab at CLEF 2025

Benchmarking Retrieval-Augmented Generation in Multi-Modal Contexts

Zhenghao Liu, Xingsheng Zhu, Tianshuo Zhou, Xinyi Zhang, Xiaoyuan Yi, Yukun Yan, Ge Yu, Maosong Sun

机构 * Northeastern University, China（东北大学） ； Microsoft Research Asia（微软亚洲研究院） ； Tsinghua University（清华大学） 纠错

专题命中 检索器与排序 ：retrieval-augmented generation(title,abstract)；RAG(abstract)；分类 cs.AI

RAIDX: A Retrieval-Augmented Generation and GRPO Reinforcement Learning Framework for Explainable Deepfake Detection

Tianxiao Li, Zhenglin Huang, Haiquan Wen, Yiwei He, Shuchang Lyu, Baoyuan Wu, Guangliang Cheng

机构 * University of Liverpool（利物浦大学） ； Beihang University（北航大学） ； The Chinese University of Hong Kong（香港中文大学） 纠错

专题命中 检索器与排序 ：retrieval-augmented generation(title,abstract)；RAG(abstract)；分类 cs.AI

PAIRS: Parametric-Verified Adaptive Information Retrieval and Selection for Efficient RAG

Wang Chen, Guanqiang Qi, Weikang Li, Yang Li, Deguo Xia, Jizhou Huang

专题命中 检索器与排序 ：RAG(title,abstract)；retrieval-augmented generation(abstract)；分类 cs.CL

Towards Temporal-Aware Multi-Modal Retrieval Augmented Generation in Finance

Fengbin Zhu, Junfeng Li, Liangming Pan, Wenjie Wang, Fuli Feng, Chao Wang, Huanbo Luan, Tat-Seng Chua

机构 * National University of Singapore（新加坡国立大学） ； Peking University（北京大学） ； University of Science and Technology of China（中国科学技术大学） ； Estates Pte Ltd（6Estates私人有限公司） 纠错

专题命中 检索器与排序 ：retrieval augmented generation(title)；retrieval-augmented generation(abstract)；RAG(abstract)；分类 cs.AI

Comments Accepted by MM 2025

GETALP@AutoMin 2025: Leveraging RAG to Answer Questions based on Meeting Transcripts

Jeongwoo Kang, Markarit Vartampetian, Felix Herron, Yongxin Zhou, Diandra Fabre, Gabriela Gonzalez-Saez

机构 * Univ. Grenoble Alpes, CNRS, Grenoble INP, LIG（格勒诺布尔阿尔卑斯大学、国家科学研究中心、格勒诺布尔理工大学、LIG实验室） 纠错

专题命中 检索器与排序 ：RAG(title,abstract)；retrieval augmented generation(abstract)；分类 cs.CL

Efficacy of AI RAG Tools for Complex Information Extraction and Data Annotation Tasks: A Case Study Using Banks Public Disclosures

Nicholas Botti, Flora Haberkorn, Charlotte Hoopes, Shaun Khan

专题命中 检索器与排序 ：RAG(title,abstract)；retrieval augmented generation(abstract)；分类 cs.AI

Advanced System Integration: Analyzing OpenAPI Chunking for Retrieval-Augmented Generation

Robin D. Pesl, Jerin G. Mathew, Massimo Mecella, Marco Aiello

专题命中 检索器与排序 ：retrieval-augmented generation(title)；retrieval augmented generation(abstract)；RAG(abstract)；分类 cs.AI

Comments This preprint has not undergone peer review (when applicable) or any post-submission improvements or corrections. The Version of Record of this contribution is published in Advanced Information Systems Engineering. CAiSE 2025. Lecture Notes in Computer Science, vol 15702. Springer, Cham., and is available online at https://doi.org/10.1007/978-3-031-94571-7_8

Journal ref Advanced Information Systems Engineering. CAiSE 2025. Lecture Notes in Computer Science, vol 15702. Springer, Cham

Large Language Model Agent for Structural Drawing Generation Using ReAct Prompt Engineering and Retrieval Augmented Generation

Xin Zhang, Lissette Iturburu, Juan Nicolas Villamizar, Xiaoyu Liu, Manuel Salmeron, Shirley J. Dyke, Julio Ramirez

机构 * Lyles School of Civil and Construction Engineering（莱尔斯土木与建设工程学院） ； Purdue University（普渡大学） ； School of Mechanical Engineering（机械工程学院） 纠错

专题命中 检索器与排序 ：retrieval augmented generation(title)；retrieval-augmented generation(abstract)；RAG(abstract)；分类 cs.AI

KnowShiftQA: How Robust are RAG Systems when Textbook Knowledge Shifts in K-12 Education?

Tianshi Zheng, Weihan Li, Jiaxin Bai, Weiqi Wang, Yangqiu Song

机构 * Department of Computer Science and Engineering, HKUST, Hong Kong SAR, China（计算机科学与工程系，香港科技大学，香港特别行政区，中国） ； Department of Mechanical Engineering, The University of Tokyo, Tokyo, Japan（机械工程系，东京大学，东京，日本） 纠错

专题命中 检索器与排序 ：RAG(title,abstract)；retrieval-augmented generation(abstract)；分类 cs.CL

Comments ACL 2025 Main

A Library of LLM Intrinsics for Retrieval-Augmented Generation

Marina Danilevsky, Kristjan Greenewald, Chulaka Gunasekara, Maeda Hanafi, Lihong He, Yannis Katsis, Krishnateja Killamsetty, Yulong Li, Yatin Nandwani, Lucian Popa, Dinesh Raghu, Frederick Reiss, Vraj Shah, Khoi-Nguyen Tran, Huaiyu Zhu, Luis Lastras

机构 * IBM Research（IBM研究院） 纠错

专题命中 检索器与排序 ：retrieval-augmented generation(title,abstract)；RAG(abstract)；分类 cs.AI

Comments This (June 2025) is the second version of this paper (the first was published in April 2025). Intrinsics implemented as LoRAs are now trained on IBM Granite 3.3 8b instruct (previously 3.2)

HedraRAG: Coordinating LLM Generation and Database Retrieval in Heterogeneous RAG Serving

Zhengding Hu, Vibha Murthy, Zaifeng Pan, Wanlu Li, Xiaoyi Fang, Yufei Ding, Yuke Wang

机构 * University of California San Diego（加州大学圣地亚哥分校） ； Rice University（里士满大学） 纠错

专题命中 检索器与排序 ：RAG(title,abstract)；retrieval-augmented generation(abstract)；分类 cs.DB

Comments Accepted by SOSP 2025

Good/Evil Reputation Judgment of Celebrities by LLMs via Retrieval Augmented Generation

Rikuto Tsuchida, Hibiki Yokoyama, Takehito Utsuro

机构 * Grad. Sch. Sci.&Tech., University of Tsukuba（大学理工学院） 纠错

专题命中 检索器与排序 ：retrieval augmented generation(title,abstract)；RAG(abstract)；分类 cs.CL

Evaluating Retrieval-Augmented Generation Agents for Autonomous Scientific Discovery in Astrophysics

Xueqing Xu, Boris Bolliet, Adrian Dimitrov, Andrew Laverick, Francisco Villaescusa-Navarro, Licong Xu, Íñigo Zubeldia

机构 * Institute of Astronomy, University of Cambridge, Cambridge, United Kingdom（英国剑桥大学天文研究所） ； Kavli Institute for Cosmology, University of Cambridge, Cambridge, United Kingdom（英国剑桥大学卡弗里宇宙研究所） ； Department of Physics, University of Cambridge, Cambridge, United Kingdom（英国剑桥大学物理系） ； Center for Computational Astrophysics, Flatiron Institute, New York, NY, USA（Flatiron研究所计算天体物理学中心） ； Department of Astrophysical Sciences, Princeton University, Princeton, NJ, USA（普林斯顿大学天体物理学系） 纠错

专题命中 检索器与排序 ：retrieval-augmented generation(title)；retrieval augmented generation(abstract)；RAG(abstract)；分类 cs.AI

Comments Accepted contribution (spotlight) to the ICML 2025 Workshop on Machine Learning for Astrophysics; codes: https://huggingface.co/datasets/ASTROANTS/CosmoPaperQA, https://github.com/CMBAgents/cmbagent, https://github.com/CMBAgents/scirag

Investigating the Robustness of Retrieval-Augmented Generation at the Query Level

Sezen Perçin, Xin Su, Qutub Sha Syed, Phillip Howard, Aleksei Kuvshinov, Leo Schwinn, Kay-Ulrich Scholl

机构 * Technical University of Munich（慕尼黑技术大学） ； Intel Labs（英特尔实验室） ； Thoughtworks 纠错

专题命中 检索器与排序 ：retrieval-augmented generation(title,abstract)；RAG(abstract)；分类 cs.CL

Comments Accepted to Generation, Evaluation & Metrics (GEM) Workshop at ACL 2025

KERAG_R: Knowledge-Enhanced Retrieval-Augmented Generation for Recommendation

Zeyuan Meng, Zixuan Yi, Iadh Ounis

专题命中 检索器与排序 ：retrieval-augmented generation(title,abstract)；RAG(abstract)；分类 cs.IR

The Hidden Threat in Plain Text: Attacking RAG Data Loaders

Alberto Castagnaro, Umberto Salviati, Mauro Conti, Luca Pajola, Simeone Pizzi

机构 * University of Padua（帕多瓦大学） 纠错

专题命中 检索器与排序 ：RAG(title,abstract)；retrieval-augmented generation(abstract)；分类 cs.AI

Comments currently under submission

UrbanMind: Towards Urban General Intelligence via Tool-Enhanced Retrieval-Augmented Generation and Multilevel Optimization

Kai Yang, Zelin Zhu, Chengtao Jian, Hui Ma, Shengjie Zhao, Xiaozhou Ye, Ye Ouyang

机构 * Tongji University（同济大学） ； Xinjiang University（新疆大学） ； Asiainfo Technologies（亚洲信息科技） 纠错

专题命中 检索器与排序 ：retrieval-augmented generation(title,abstract)；RAG(abstract)；分类 cs.AI

From Query to Explanation: Uni-RAG for Multi-Modal Retrieval-Augmented Learning in STEM

Xinyi Wu, Yanhao Jia, Luwei Xiao, Shuai Zhao, Fengkuang Chiang, Erik Cambria

机构 * School of Education, Shanghai Jiao Tong University（上海交通大学教育学院） ； College of Computing and Data Science, Nanyang Technological University（南洋理工大学 computing and Data Science 学院） ； school of Computer Science and Technology, East China Normal University（华东师范大学计算机科学与技术学院） 纠错

专题命中 检索器与排序 ：RAG(title,abstract)；retrieval-augmented generation(abstract)；分类 cs.AI

EraRAG: Efficient and Incremental Retrieval Augmented Generation for Growing Corpora

Fangyuan Zhang, Zhengjun Huang, Yingli Zhou, Qintian Guo, Zhixun Li, Wensheng Luo, Di Jiang, Yixiang Fang, Xiaofang Zhou

机构 * Huawei Hong Kong Research Center（华为香港研究中心） ； The Hong Kong University of Science and Technology（香港理工大学） ； The Chinese University of Hong Kong-Shenzhen（香港中文大学（深圳）） ； The Chinese University of Hong Kong（香港中文大学） ； Hunan University（湖南大学） 纠错

专题命中 检索器与排序 ：retrieval augmented generation(title)；retrieval-augmented generation(abstract)；RAG(abstract)；分类 cs.IR

Comments Under review