RAG / 检索增强生成

Larger Is Not Always Better: Exploring Small Open-source Language Models in Logging Statement Generation

Renyi Zhong, Yichen Li, Guangba Yu, Wenwei Gu, Jinxi Kuang, Yintong Huo, Michael R. Lyu

专题命中 检索器与排序 ：retrieval-augmented generation(abstract)；RAG(abstract)

Tether: A Personalized Support Assistant for Software Engineers with ADHD

Aarsh Shah, Cleyton Magalhaes, Kiev Gama, Ronnie de Souza Santos

专题命中 检索器与排序 ：retrieval-augmented generation(abstract)；RAG(abstract)

RepoTransAgent: Multi-Agent LLM Framework for Repository-Aware Code Translation

Ziqi Guan, Xin Yin, Zhiyuan Peng, Chao Ni

专题命中 检索器与排序 ：retrieval-augmented generation(abstract)；RAG(abstract)

Agentic AI Empowered Multi-UAV Trajectory Optimization in Low-Altitude Economy Networks

Feibo Jiang, Li Dong, Xitao Pan, Kezhi Wang, Cunhua Pan

专题命中 检索器与排序 ：retrieval-augmented generation(abstract)；RAG(abstract)

Research on intelligent generation of structural demolition suggestions based on multi-model collaboration

Zhifeng Yang, Peizong Wu

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

CROP: Circuit Retrieval and Optimization with Parameter Guidance using LLMs

Jingyu Pan, Isaac Jacobson, Zheng Zhao, Tung-Chieh Chen, Guanglei Zhou, Chen-Chia Chang, Vineet Rashingkar, Yiran Chen

机构 * Electrical and Computer Engineering, Duke University（电气与计算机工程系，杜克大学） ； Synopsys Inc.（Synopsys公司） 纠错

专题命中 检索器与排序 ：retrieval-augmented generation(abstract)；RAG(abstract)

Comments Accepted by ICCAD 2025

Human-in-the-Loop Systems for Adaptive Learning Using Generative AI

Bhavishya Tarun, Haoze Du, Dinesh Kannan, Edward F. Gehringer

机构 * Department of Computer Science（计算机科学系） ； North Carolina State University（北卡罗来纳州立大学） 纠错

专题命中 检索器与排序 ：retrieval-augmented generation(abstract)；RAG(abstract)

Comments Accepted for presentation at the Frontiers in Education Conference, Nashville, Tennessee, USA, 2-5 November 2025

LLM-Lasso: A Robust Framework for Domain-Informed Feature Selection and Regularization

Erica Zhang, Ryunosuke Goto, Naomi Sagan, Jurik Mutter, Nick Phillips, Ash Alizadeh, Kangwook Lee, Jose Blanchet, Mert Pilanci, Robert Tibshirani

机构 * Stanford University School of Engineering（斯坦福大学工程学院） ； Stanford University School of Medicine（斯坦福大学医学院） ； Stanford University Department of Statistics（斯坦福大学统计学系） ； University of Wisconsin-Madison（威斯康星大学麦迪逊分校） 纠错

专题命中 检索器与排序 ：retrieval-augmented generation(abstract)；RAG(abstract)

Comments 21 pages, 16 figures

HierSearch: A Hierarchical Enterprise Deep Search Framework Integrating Local and Web Searches

Jiejun Tan, Zhicheng Dou, Yan Yu, Jiehan Cheng, Qiang Ju, Jian Xie, Ji-Rong Wen

机构 * Baichuan（百川） 纠错

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

Comments Code and datasets are available at https://github.com/plageon/HierSearch

SwarmVLM: VLM-Guided Impedance Control for Autonomous Navigation of Heterogeneous Robots in Dynamic Warehousing

Malaika Zafar, Roohan Ahmed Khan, Faryal Batool, Yasheerah Yaqoot, Ziang Guo, Mikhail Litvinov, Aleksey Fedoseev, Dzmitry Tsetserukou

机构 * Intelligent Space Robotics Laboratory, Skolkovo Institute of Science and Technology（智能空间机器人实验室，斯克洛科沃科学与技术研究所） 纠错

专题命中 检索器与排序 ：retrieval-augmented generation(abstract)；RAG(abstract)

Multi-Modal Requirements Data-based Acceptance Criteria Generation using LLMs

Fanyu Wang, Chetan Arora, Yonghui Liu, Kaicheng Huang, Chakkrit Tantithamthavorn, Aldeida Aleti, Dishan Sambathkumar, David Lo

专题命中 检索器与排序 ：retrieval-augmented generation(abstract)；RAG(abstract)

Feedback-Guided Extraction of Knowledge Base from Retrieval-Augmented LLM Applications

Changyue Jiang, Xudong Pan, Geng Hong, Chenfu Bao, Yang Chen, Min Yang

专题命中 检索器与排序 ：retrieval-augmented generation(abstract)；RAG(abstract)

Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learning

Bowen Jin, Hansi Zeng, Zhenrui Yue, Jinsung Yoon, Sercan Arik, Dong Wang, Hamed Zamani, Jiawei Han

机构 * Department of Computer Science, University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校计算机科学系） ； Center for Intelligent Information Retrieval, University of Massachusetts Amherst（马萨诸塞大学阿姆赫斯特分校智能信息检索中心） ； Google Cloud AI Research（谷歌云人工智能研究） 纠错

专题命中 检索器与排序 ：RAG(abstract)；分类 cs.IR、cs.CL、cs.AI

Comments 31 pages

Can Large Language Models Identify Materials from Radar Signals?

Jiangyou Zhu, Hongyu Deng, He Chen

机构 * 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(abstract)；RAG(abstract)

Talk with the Things: Integrating LLMs into IoT Networks

Alakesh Kalita

专题命中 检索器与排序 ：retrieval augmented generation(abstract)；RAG(abstract)

Comments arXiv admin note: text overlap with arXiv:2407.20970

Overview of the TREC 2022 deep learning track

Nick Craswell, Bhaskar Mitra, Emine Yilmaz, Daniel Campos, Jimmy Lin, Ellen M. Voorhees, Ian Soboroff

机构 * Microsoft（微软公司） ； University College London（伦敦大学学院） ； University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校） ； Neural Magic Inc（Neural Magic公司） ； University of Waterloo（滑铁卢大学） ； NIST（国家标准与技术研究院） 纠错

专题命中 检索器与排序 ：dense retrieval(abstract)；分类 cs.IR、cs.CL、cs.AI

Comments arXiv admin note: substantial text overlap with arXiv:2507.08191, arXiv:2507.08890

Compute Can't Handle the Truth: Why Communication Tax Prioritizes Memory and Interconnects in Modern AI Infrastructure

Myoungsoo Jung

专题命中 检索器与排序 ：retrieval-augmented generation(abstract)；RAG(abstract)

SymRAG: Efficient Neuro-Symbolic Retrieval Through Adaptive Query Routing

Safayat Bin Hakim, Muhammad Adil, Alvaro Velasquez, Houbing Herbert Song

机构 * Department of Information Systems, University of Maryland, Baltimore County, USA（信息系统系，马里兰大学巴尔的摩县分校） ； Department of Computer Science and Engineering, University at Buffalo, USA（计算机科学与工程系，布法罗大学） ； Department of Computer Science, University of Colorado Boulder, USA（计算机科学系，科罗拉多大学波德分校） 纠错

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

Comments Accepted at 19th International Conference on Neurosymbolic Learning and Reasoning (NeSy 2025)

Generative Retrieval and Alignment Model: A New Paradigm for E-commerce Retrieval

Ming Pang, Chunyuan Yuan, Xiaoyu He, Zheng Fang, Donghao Xie, Fanyi Qu, Xue Jiang, Changping Peng, Zhangang Lin, Ching Law, Jingping Shao

专题命中 检索器与排序 ：dense retrieval(abstract)；分类 cs.IR、cs.CL、cs.AI

Comments Accepted by WWW2025

Object Retrieval for Visual Question Answering with Outside Knowledge

Shichao Kan, Yuhai Deng, Jiale Fu, Lihui Cen, Zhe Qu, Linna Zhang, Yixiong Liang, Yigang Cen

机构 * School of Computer Science and Engineering, Central South University（中南大学计算机科学与工程学院） ； School of Automation, Central South University（中南大学自动化学院） ； Dundee International Institute, Central South University（中南大学邓迪国际学院） ； College of Mechanical Engineering, Guizhou University（贵州大学机械工程学院） ； Institute of Information Science, School of Computer and Information Technology, Beijing Jiaotong University（北京交通大学计算机与信息学院信息科学研究所） ； Beijing Key Laboratory of Advanced Information Science and Network Technology（北京高级信息科学与网络技术重点实验室） 纠错

专题命中 检索器与排序 ：retrieval-augmented generation(abstract)；RAG(abstract)

LASA: Enhancing SoC Security Verification with LLM-Aided Property Generation

Dinesh Reddy Ankireddy, Sudipta Paria, Aritra Dasgupta, Sandip Ray, Swarup Bhunia

专题命中 检索器与排序 ：retrieval-augmented generation(abstract)；RAG(abstract)

Comments 9 pages, 5 figures, 5 tables

Integrating Traditional Technical Analysis with AI: A Multi-Agent LLM-Based Approach to Stock Market Forecasting

Michał Wawer, Jarosław A. Chudziak

专题命中 检索器与排序 ：retrieval-augmented generation(abstract)；RAG(abstract)

Comments 12 pages, 8 figures, 1 table. This is the accepted version of the paper presented at the 17th International Conference on Agents and Artificial Intelligence (ICAART 2025), Porto, Portugal

Journal ref Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 1 (ICAART 2025), pages 100-111. SciTePress, 2025

Al-Khwarizmi: Discovering Physical Laws with Foundation Models

Christopher E. Mower, Haitham Bou-Ammar

机构 * Huawei's Noahs Ark Lab, London, UK.（华为诺亚实验室，伦敦，英国） 纠错

专题命中 检索器与排序 ：retrieval-augmented generation(abstract)；RAG(abstract)

HybridRAG-based LLM Agents for Low-Carbon Optimization in Low-Altitude Economy Networks

Jinbo Wen, Cheng Su, Jiawen Kang, Jiangtian Nie, Yang Zhang, Jianhang Tang, Dusit Niyato, Chau Yuen

专题命中 检索器与排序 ：retrieval augmented generation(abstract)；RAG(abstract)

Retrieval-augmented code completion for local projects using large language models

Marko Hostnik, Marko Robnik-Šikonja

机构 * Faculty of Computer and Information Science, University of Ljubljana（计算机与信息科学学院，卢布尔雅那大学） ； Faculty of Mathematics and Physics, University of Ljubljana（数学与物理学院，卢布尔雅那大学） 纠错

专题命中 检索器与排序 ：retrieval-augmented generation(abstract)；RAG(abstract)

Comments 30 pages, 15 figures; Accepted manuscript for Expert Systems with Applications

Retrieval-Augmented Code Review Comment Generation

Hyunsun Hong, Jongmoon Baik

专题命中 检索器与排序 ：retrieval-augmented generation(abstract)；RAG(abstract)

Test code generation at Ericsson using Program Analysis Augmented Fine Tuned LLMs

Sai Krishna, Balvinder Singh, Sujoy Roychowdhury, Giriprasad Sridhara, Sourav Mazumdar, Magnus Sandelin, Dimitris Rentas, Maciej Nalepa, Karol Sawicki, Jakub Gajda

专题命中 检索器与排序 ：retrieval augmented generation(abstract)；RAG(abstract)

Comments Accepted at International Conference on Evaluation and Assessment in Software Engineering (EASE), 2025

FreshStack: Building Realistic Benchmarks for Evaluating Retrieval on Technical Documents

Nandan Thakur, Jimmy Lin, Sam Havens, Michael Carbin, Omar Khattab, Andrew Drozdov

机构 * University of Waterloo（滑铁卢大学） ； Databricks（Databricks公司） 纠错

专题命中 检索器与排序 ：RAG(abstract)；分类 cs.IR、cs.CL、cs.AI

Comments 21 pages, 4 figures, 8 tables

Optimized Text Embedding Models and Benchmarks for Amharic Passage Retrieval

Kidist Amde Mekonnen, Yosef Worku Alemneh, Maarten de Rijke

机构 * University of Amsterdam（阿姆斯特丹大学） 纠错

专题命中 检索器与排序 ：dense retrieval(abstract)；分类 cs.IR、cs.CL、cs.AI

Comments 10 pages (excl. refs/appendix), 10 figures. Accepted to ACL 2025 Findings. Kidist and Yosef contributed equally to this work. Public resources: https://github.com/kidist-amde/amharic-ir-benchmarks

Retrieval-augmented systems can be dangerous medical communicators

Lionel Wong, Ayman Ali, Raymond Xiong, Shannon Zeijang Shen, Yoon Kim, Monica Agrawal

机构 * MIT CSAIL（麻省理工学院计算机科学与人工智能实验室） ； Duke University（杜克大学） ； Stanford University（斯坦福大学） 纠错

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

Comments Position paper in Proceedings of the 42 nd International Conference on Machine Learning