RAG / 检索增强生成

SynthAI: A Multi Agent Generative AI Framework for Automated Modular HLS Design Generation

Seyed Arash Sheikholeslam, Andre Ivanov

专题命中 图谱与结构化RAG ：retrieval-augmented generation(abstract)；RAG(abstract)；分类 cs.AI

Comments This work is in progress and we will be updating it

A Reliable Knowledge Processing Framework for Combustion Science using Foundation Models

Vansh Sharma, Venkat Raman

专题命中 图谱与结构化RAG ：retrieval-augmented generation(abstract)；RAG(abstract)；分类 cs.AI

Comments 38 pages and 10 figures; Fixed figure resolution

Cross-Data Knowledge Graph Construction for LLM-enabled Educational Question-Answering System: A Case Study at HCMUT

Tuan Bui, Oanh Tran, Phuong Nguyen, Bao Ho, Long Nguyen, Thang Bui, Tho Quan

专题命中 图谱与结构化RAG ：retrieval-augmented generation(abstract)；RAG(abstract)；分类 cs.CL

Comments 8 pages, 7 figures, Accepted at AIQAM '24: Proceedings of the 1st ACM Workshop on AI-Powered Q&A Systems for Multimedia

Reconciling Methodological Paradigms: Employing Large Language Models as Novice Qualitative Research Assistants in Talent Management Research

Sreyoshi Bhaduri, Satya Kapoor, Alex Gil, Anshul Mittal, Rutu Mulkar

专题命中 图谱与结构化RAG ：retrieval augmented generation(abstract)；RAG(abstract)；分类 cs.AI

Comments Accepted to KDD '24 workshop on Talent Management and Computing (TMC 2024). 9 pages

MetaOpenFOAM: an LLM-based multi-agent framework for CFD

Yuxuan Chen, Xu Zhu, Hua Zhou, Zhuyin Ren

专题命中 图谱与结构化RAG ：retrieval-augmented generation(abstract)；RAG(abstract)；分类 cs.AI

Comments 31 pages,11 figures, 11 tables

Decoding BACnet Packets: A Large Language Model Approach for Packet Interpretation

Rashi Sharma, Hiroyuki Okada, Tatsumi Oba, Karthikk Subramanian, Naoto Yanai, Sugiri Pranata

专题命中 图谱与结构化RAG ：retrieval augmented generation(abstract)；RAG(abstract)；分类 cs.AI

Comments 12 pages

PRAGyan -- Connecting the Dots in Tweets

Rahul Ravi, Gouri Ginde, Jon Rokne

专题命中 图谱与结构化RAG ：retrieval-augmented generation(abstract)；RAG(abstract)；分类 cs.IR

Comments 9 pages, ASONAM

LLMs in Biomedicine: A study on clinical Named Entity Recognition

Masoud Monajatipoor, Jiaxin Yang, Joel Stremmel, Melika Emami, Fazlolah Mohaghegh, Mozhdeh Rouhsedaghat, Kai-Wei Chang

专题命中 图谱与结构化RAG ：retrieval-augmented generation(abstract)；RAG(abstract)；分类 cs.CL

Privacy-Preserved Neural Graph Databases

Qi Hu, Haoran Li, Jiaxin Bai, Zihao Wang, Yangqiu Song

专题命中 图谱与结构化RAG ：retrieval augmented generation(abstract)；RAG(abstract)；分类 cs.DB

Artificial Intelligence as the New Hacker: Developing Agents for Offensive Security

Leroy Jacob Valencia

专题命中 图谱与结构化RAG ：retrieval augmented generation(abstract)；RAG(abstract)；分类 cs.AI

Toward Conversational Agents with Context and Time Sensitive Long-term Memory

Nick Alonso, Tomás Figliolia, Anthony Ndirango, Beren Millidge

专题命中 图谱与结构化RAG ：retrieval-augmented generation(abstract)；RAG(abstract)；分类 cs.CL

Can Github issues be solved with Tree Of Thoughts?

Ricardo La Rosa, Corey Hulse, Bangdi Liu

专题命中 图谱与结构化RAG ：retrieval augmented generation(abstract)；RAG(abstract)；分类 cs.AI

Comments 8 pages, 2 figures, 7 tables

Biomedical knowledge graph-optimized prompt generation for large language models

Karthik Soman, Peter W Rose, John H Morris, Rabia E Akbas, Brett Smith, Braian Peetoom, Catalina Villouta-Reyes, Gabriel Cerono, Yongmei Shi, Angela Rizk-Jackson, Sharat Israni, Charlotte A Nelson, Sui Huang, Sergio E Baranzini

专题命中 图谱与结构化RAG ：retrieval augmented generation(abstract)；RAG(abstract)；分类 cs.CL

Comments 29 pages, 5 figures, 1 table, 1 supplementary file

Towards Accurate and Efficient Document Analytics with Large Language Models

Yiming Lin, Madelon Hulsebos, Ruiying Ma, Shreya Shankar, Sepanta Zeigham, Aditya G. Parameswaran, Eugene Wu

专题命中 图谱与结构化RAG ：retrieval-augmented generation(abstract)；RAG(abstract)；分类 cs.DB

Enhancing Q&A with Domain-Specific Fine-Tuning and Iterative Reasoning: A Comparative Study

Zooey Nguyen, Anthony Annunziata, Vinh Luong, Sang Dinh, Quynh Le, Anh Hai Ha, Chanh Le, Hong An Phan, Shruti Raghavan, Christopher Nguyen

专题命中 图谱与结构化RAG ：retrieval-augmented generation(abstract)；RAG(abstract)；分类 cs.AI

Comments Fixed typo of OODA's score on harder-question set in Table 2

Enhancing Software-Related Information Extraction via Single-Choice Question Answering with Large Language Models

Wolfgang Otto, Sharmila Upadhyaya, Stefan Dietze

专题命中 图谱与结构化RAG ：retrieval-augmented generation(abstract)；RAG(abstract)；分类 cs.CL

Comments Accepted at: 1st Workshop on Natural Scientific Language Processing and Research Knowledge Graphs (NSLP 2024) Co-located with Extended Semantic Web Conference (ESWC 2024)

Qilin-Med: Multi-stage Knowledge Injection Advanced Medical Large Language Model

Qichen Ye, Junling Liu, Dading Chong, Peilin Zhou, Yining Hua, Fenglin Liu, Meng Cao, Ziming Wang, Xuxin Cheng, Zhu Lei, Zhenhua Guo

专题命中 图谱与结构化RAG ：retrieval augmented generation(abstract)；RAG(abstract)；分类 cs.CL

S3LLM: Large-Scale Scientific Software Understanding with LLMs using Source, Metadata, and Document

Kareem Shaik, Dali Wang, Weijian Zheng, Qinglei Cao, Heng Fan, Peter Schwartz, Yunhe Feng

专题命中 图谱与结构化RAG ：retrieval augmented generation(abstract)；RAG(abstract)；分类 cs.AI

Beyond Extraction: Contextualising Tabular Data for Efficient Summarisation by Language Models

Uday Allu, Biddwan Ahmed, Vishesh Tripathi

专题命中 图谱与结构化RAG ：retrieval-augmented generation(abstract)；RAG(abstract)；分类 cs.CL

Comments Submitted to IEEE

Glitter or Gold? Deriving Structured Insights from Sustainability Reports via Large Language Models

Marco Bronzini, Carlo Nicolini, Bruno Lepri, Andrea Passerini, Jacopo Staiano

专题命中 图谱与结构化RAG ：retrieval-augmented generation(abstract)；RAG(abstract)；分类 cs.CL

DB-GPT: Empowering Database Interactions with Private Large Language Models

Siqiao Xue, Caigao Jiang, Wenhui Shi, Fangyin Cheng, Keting Chen, Hongjun Yang, Zhiping Zhang, Jianshan He, Hongyang Zhang, Ganglin Wei, Wang Zhao, Fan Zhou, Danrui Qi, Hong Yi, Shaodong Liu, Faqiang Chen

专题命中 图谱与结构化RAG ：retrieval augmented generation(abstract)；RAG(abstract)；分类 cs.DB

A Unified [CII] Morpho-Kinematic Corpus for 31 Star-Forming Galaxies at z = 4.26-5.68: The High-z Kinematic Corpus Z1

统一 [CII] 运动学形态数据集：31 个 z=4.26-5.68 恒星形成星系的高红移运动学语料库 Z1

David C. Flynn

专题命中 图谱与结构化RAG ：RAG(abstract,abstract_cn)

AI总结 基于 ALPINE 巡天数据，构建了 31 个高红移恒星形成星系的 [CII] 形态运动学结构化数据集，包含旋转曲线、动力学质量及分类信息，并公开了多格式数据与示例分析。

Comments 8 pages, 4 tables, 3 figures. Data descriptor for the High-z Kinematic Corpus Z1, the fourth corpus in the EPS Research RAG Astrophysics Corpus Series. Corpus deposited at Zenodo DOI: 10.5281/zenodo.20369285. Jupyter notebooks and figures included as ancillary files

Copyright Is the Headline; Capability Is the Blind Spot: AI Technology in the Book-Publishing Trade Press, November 2025--August 2026

版权是头条，能力是盲区：2025年11月—2026年8月图书出版行业媒体中的人工智能技术

Fred Zimmerman

专题命中 图谱与结构化RAG ：RAG(abstract,abstract_cn)

AI总结 本研究通过分析多国行业媒体的89篇文章，指出图书出版领域AI报道存在能力盲区，提出设立常设AI报道板块等改进建议。

Comments 7 pages, 2 figures. 15-page Supplemental Information, coded 89-item corpus, crosstabs, BibTeX database, and citation audit included as ancillary files

TabRank: Chain-of-Thought Distillation for Table Re-Rankers

TabRank：表格重排器的思维链蒸馏

Adarsh Singh, Kushal Raj Bhandari, Jianxi Gao, Soham Dan, Vivek Gupta

机构 * Arizona State University（亚利桑那州立大学） ； Rensselaer Polytechnic Institute（伦斯勒理工学院） ； Scale AI 纠错

专题命中 图谱与结构化RAG ：dense retrieval(abstract)；分类 cs.IR、cs.CL、cs.AI

AI总结 研究针对表格检索训练推理重排器的问题，提出TabRank框架，通过构建数据集并探索两种训练紧凑推理模型的变体，经压力测试，该方法在多表格检索数据集上显著提升性能，有效推广到多表格推理。

Comments 8 pages, 3 figures

Cognitive-YOLO: LLM-Driven Architecture Synthesis from First Principles of Data for Object Detection

认知YOLO：基于数据第一性原理的大语言模型驱动的架构合成用于目标检测

Jiahao Zhao

机构 * Xi’an University of Posts and Telecommunications（西安邮电大学） 纠错

专题命中 图谱与结构化RAG ：retrieval-augmented generation(abstract)；RAG(abstract)

AI总结 针对通用目标检测算法在垂直场景的问题，认知YOLO利用大语言模型与自主智能体协作，构建“分析-合成-编译”管道，合成轻量级模型，显著压缩参数数量，在mAP@0.5:0.95上表现良好，平衡了模型紧凑性和特征表示能力。

Comments 11 pages, 3 figures, 6 tables

BugRepro: Enhancing Android Bug Reproduction with Domain-Specific Knowledge Integration

BugRepro: 通过领域特定知识整合提升Android bug重现

Hongrong Yin, Jinhong Huang, Yao Li, Yunwei Dong, Tao Zhang

专题命中 图谱与结构化RAG ：retrieval-augmented generation(abstract)；RAG(abstract)

AI总结 本文提出BugRepro，通过整合领域知识提升Android应用bug重现的准确性和效率，实验表明其在S2R实体提取准确率和重现成功率上均显著优于现有方法。

Journal ref Autom Softw Eng 33, 89 (2026)

eMEM: A Hybrid Spatio-Temporal Memory System For Embodied Agents

eMEM：一种面向具身智能体的混合时空记忆系统

A. Haroon Rasheed, Maria Kabtoul

专题命中 图谱与结构化RAG ：RAG(abstract,abstract_cn)

AI总结 提出eMEM混合图记忆系统，通过多索引架构和分层整合管道实现具身智能体在空间、时间和语义上的高效记忆检索，并在ProcTHOR-10K基准测试中达到80.8加权平均分。

PRAXIS: Case-distilled and code-verified AI agents for biological research

PRAXIS：用于生物学研究的案例提炼与代码验证的AI智能体

Zhenyu Ma, Yuyang Song, Chunyi Yang, Jingyi Zhu, Limei Xu, Min Xiao, Xukai Jiang

专题命中 图谱与结构化RAG ：RAG(abstract,abstract_cn)

AI总结 提出PRAXIS框架，通过文献学习和案例提炼将研究经验转化为结构化长期记忆，支持生物计算任务中的问题定义、对象验证、方法选择等，实验表明基于案例的学习提升了复杂生物研究任务的方法选择、错误抑制和工作流组织。

Language-Native Materials Processing Design by Lightly Structured Text Database and Reasoning Large Language Model

基于轻结构化文本数据库和推理大语言模型的自然语言材料加工设计

Yuze Liu, Zhaoyuan Zhang, Xiangsheng Zeng, Yihe Zhang, Leping Yu, Liu Yang, Lejia Wang, Xi Yu

机构 * State Key Laboratory of Advanced Materials for Intelligent Sensing, Key Laboratory of Organic Integrated Circuit, Ministry of Education & Tianjin Key Laboratory of Molecular Optoelectronic Sciences, Department of Chemistry, School of Science, Tianjin University（智能传感先进材料国家重点实验室、有机集成电路重点实验室、教育部、天津分子光电子科学重点实验室、化学系、天津大学） ； Language Intelligence Technology Co., Ltd.（语言智能技术有限公司） ； College of Intelligence and Computing, Tianjin University（智能与计算学院、天津大学） ； School of Materials and Chemical Engineering, Ningbo University of Technology（材料与化学工程学院、宁波工业大学） 纠错

专题命中 图谱与结构化RAG ：retrieval-augmented generation(abstract)；分类 cs.CL、cs.AI、cs.DB

AI总结 将材料合成规划重构为文本推理问题，通过轻结构化知识基底结合检索增强生成与经验增强推理，在氮化硼纳米片剥离中三轮迭代获得高质量协议。

CORE-T: COherent REtrieval of Tables for Text-to-SQL

CORE-T: 面向文本到SQL的表格连贯检索

Hassan Soliman, Vivek Gupta, Dan Roth, Iryna Gurevych

机构 * Ubiquitous Knowledge Processing Lab (UKP Lab), Department of Computer Science TU Darmstadt and National Research Center for Applied Cybersecurity ATHENE, Germany（普适知识处理实验室（UKP实验室），计算机科学系 TU Darmstadt 和应用网络安全国家研究中心 ATHENE，德国） ； Arizona State University（亚利桑那州立大学） ； University of Pennsylvania（宾夕法尼亚大学） 纠错

专题命中 图谱与结构化RAG ：dense retrieval(abstract)；分类 cs.IR、cs.CL、cs.AI

AI总结 提出CORE-T框架，通过LLM生成元数据和预计算兼容性缓存，在无需训练的情况下从异构表集合中高效检索连贯可连接的表集合，提升表选择F1最多22.7点并减少40%的表数量。

Comments Preprint is revised and under review. Code and data available at: https://github.com/UKPLab/arxiv2026-core-t