RAG / 检索增强生成

FRAG: A Flexible Modular Framework for Retrieval-Augmented Generation based on Knowledge Graphs

Zengyi Gao, Yukun Cao, Hairu Wang, Ao Ke, Yuan Feng, Xike Xie, S Kevin Zhou

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

Unimib Assistant: designing a student-friendly RAG-based chatbot for all their needs

Chiara Antico, Stefano Giordano, Cansu Koyuturk, Dimitri Ognibene

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

Comments Accepted for Italian Workshop on Artificial Intelligence for Human Machine Interaction (AIxHMI 2024), November 26, 2024, Bolzano, Italy

Advanced RAG Models with Graph Structures: Optimizing Complex Knowledge Reasoning and Text Generation

Yuxin Dong, Shuo Wang, Hongye Zheng, Jiajing Chen, Zhenhong Zhang, Chihang Wang

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

ConfusedPilot: Confused Deputy Risks in RAG-based LLMs

Ayush RoyChowdhury, Mulong Luo, Prateek Sahu, Sarbartha Banerjee, Mohit Tiwari

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

FRAG: Toward Federated Vector Database Management for Collaborative and Secure Retrieval-Augmented Generation

Dongfang Zhao

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

AI Assistants for Spaceflight Procedures: Combining Generative Pre-Trained Transformer and Retrieval-Augmented Generation on Knowledge Graphs With Augmented Reality Cues

Oliver Bensch, Leonie Bensch, Tommy Nilsson, Florian Saling, Bernd Bewer, Sophie Jentzsch, Tobias Hecking, J. Nathan Kutz

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

Comments Accepted for the ESA SPAICE Conference 2024: AI in and for Space

HybridRAG: Integrating Knowledge Graphs and Vector Retrieval Augmented Generation for Efficient Information Extraction

Bhaskarjit Sarmah, Benika Hall, Rohan Rao, Sunil Patel, Stefano Pasquali, Dhagash Mehta

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

Comments 9 pages, 2 figures, 5 tables

A Method for Parsing and Vectorization of Semi-structured Data used in Retrieval Augmented Generation

Hang Yang, Jing Guo, Jianchuan Qi, Jinliang Xie, Si Zhang, Siqi Yang, Nan Li, Ming Xu

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

Comments 20 pages,4 figures, 5 tables

A Fine-tuning Enhanced RAG System with Quantized Influence Measure as AI Judge

Keshav Rangan, Yiqiao Yin

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

Comments 17 pages, 4 figures

Retrieving Semantics from the Deep: an RAG Solution for Gesture Synthesis

M. Hamza Mughal, Rishabh Dabral, Merel C. J. Scholman, Vera Demberg, Christian Theobalt

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

Comments CVPR 2025. Project page: https://vcai.mpi-inf.mpg.de/projects/RAG-Gesture/

SchemaLink: An Intelligent Web Editor for LinkML Schema Curation

SchemaLink：用于LinkML模式管理的智能Web编辑器

Emanuele Cavalleri, Paolo Perlasca, J. Harry Caufield, Justin Reese, Christopher J. Mungall, Marco Mesiti

专题命中 图谱与结构化RAG ：RAG(summary_cn,abstract)；分类 cs.AI、cs.DB

AI总结 本文提出基于Web的SchemaLink，通过图形化语言和RAG方法简化LinkML模式的设计与管理，生成高质量模式，相关资源开源提供。

MinerU-Popo: Universal Post-Processing Model for Structured Document Parsing

MinerU-Popo：结构化文档解析的通用后处理模型

Bangrui Xu, Ziyang Miao, Xuanhe Zhou, Yiming Lin, Zirui Tang, Xiaomeng Zhao, Fan Wu, Cheng Tan, Fan Wu, Bin Wang, Conghui He

机构 * Shanghai Jiao Tong University（上海交通大学） ； Shanghai Artificial Intelligence Laboratory, OpenDataLab（上海人工智能实验室，OpenDataLab） ； University of California, Berkeley（加州大学伯克利分校） 纠错

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

AI总结 提出MinerU-Popo轻量级通用后处理框架，通过分解为文本/表格截断恢复、标题层级重建和图文关联四个子任务，并利用动态分块和重叠同步将OCR页面级结果重构为文档级逻辑结构，显著提升标题层级TEDS和RAG准确性。

Comments The code is available at https://github.com/opendatalab/MinerU-Popo

RAG-Enabled Intent Reasoning for Application-Network Interaction

支持应用-网络交互的意图推理（RAG）

Salwa Mostafa, Mohamed K. Abdel-Aziz, Mohammed S. Elbamby, Mehdi Bennis

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

AI总结 本文提出基于RAG的意图推理框架，用于自动网络操作与管理。通过结合机器推理、检索增强生成和生成式AI技术，实现不同应用的意图翻译，提升网络交互的智能化和领域适应性。

Unlocking Electronic Health Records: A Hybrid Graph RAG Approach to Safe Clinical AI for Patient QA

解锁电子健康记录：一种混合图RAG方法用于安全临床AI的患者问答

Samuel Thio, Matthew Lewis, Spiros Denaxas, Richard JB Dobson

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

AI总结 本文提出MediGRAF，一种结合图检索与向量嵌入的混合RAG系统，通过整合结构化数据与非结构化文本，提升临床AI在患者问答中的安全性和准确性。

Comments 26 pages, 5 figures, 2 tables

Journal ref Frontiers in Digital Health, vol. 8, 2026

Effective and Unsupervised Social Event Detection and Evolution via RAG and Structural Entropy

基于RAG和结构熵的有效且无监督的社会事件检测与演化

Qitong Liu, Hao Peng, Zuchen Li, Xihang Meng, Ziyu Yang, Jiting Li, Li Sun, Philip S. Yu

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

AI总结 RagSEDE通过引入代表性和多样性驱动的采样策略、基于RAG的新范式以及结构信息理论，有效解决了社交媒体中社会事件检测与演化中的三大挑战。

Comments 12 pages, 7 figures, accepted for The Web Conference (WWW) 2026

From Issues to Insights: RAG-based Explanation Generation from Software Engineering Artifacts

从问题到洞察：基于RAG的软件工程制品解释生成

Daniel Pöttgen, Mersedeh Sadeghi, Max Unterbusch, Andreas Vogelsang

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

AI总结 本文首次应用RAG方法从问题跟踪数据生成解释，实现90%的人工解释一致性，展示了结构化问题数据在提升软件系统可解释性方面的潜力。

Comments Accepted at NLBSE 2026, Rio de Janeiro, Brazil

Explainable Lane Change Prediction for Near-Crash Scenarios Using Knowledge Graph Embeddings and Retrieval Augmented Generation

基于知识图谱嵌入与检索增强生成的可解释性变道预测用于近碰撞场景

M. Manzour, A. Ballardini, R. Izquierdo, M. Á. Sotelo

机构 * Department of Computer Engineering, University of Alcalá（阿尔卡拉大学计算机工程系） 纠错

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

AI总结 本文提出利用知识图谱嵌入与检索增强生成技术，预测危险和安全变道，提升模型可解释性并提高预判准确性。

Journal ref 2025 IEEE Intelligent Vehicles Symposium (IV)

SafeHumanoid: VLM-RAG-driven Control of Upper Body Impedance for Humanoid Robot

SafeHumanoid: 通过VLM-RAG驱动的人形机器人上半身阻抗控制

Yara Mahmoud, Jeffrin Sam, Nguyen Khang, Marcelino Fernando, Issatay Tokmurziyev, Miguel Altamirano Cabrera, Muhammad Haris Khan, Artem Lykov, Dzmitry Tsetserukou

机构 * Skolkovo Institute of Science and Technology（斯克洛尔沃科学与技术研究所） 纠错

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

AI总结 SafeHumanoid通过VLM-RAG结合自身视觉，实现人形机器人上半身阻抗控制，提升人机交互的安全性与任务成功率。

PROPEX-RAG: Enhanced GraphRAG using Prompt-Driven Prompt Execution

Tejas Sarnaik, Manan Shah, Ravi Hegde

机构 * Indian Institute of Technology, Gandhinagar, Gujarat, India（印度理工学院冈丁加尔分校，古吉拉特邦，印度） 纠错

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

Comments Accepted in PReMI 2025

Understanding Users' Privacy Perceptions Towards LLM's RAG-based Memory

Shuning Zhang, Rongjun Ma, Ying Ma, Shixuan Li, Yiqun Xu, Xin Yi, Hewu Li

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

Advancing Shared and Multi-Agent Autonomy in Underwater Missions: Integrating Knowledge Graphs and Retrieval-Augmented Generation

Michele Grimaldi, Carlo Cernicchiaro, Sebastian Realpe Rua, Alaaeddine El-Masri-El-Chaarani, Markus Buchholz, Loizos Michael, Pere Ridao Rodriguez, Ignacio Carlucho, Yvan R. Petillot

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

KGRAG-Ex: Explainable Retrieval-Augmented Generation with Knowledge Graph-based Perturbations

Georgios Balanos, Evangelos Chasanis, Konstantinos Skianis, Evaggelia Pitoura

机构 * University of Ioannina, Greece（希腊伊奥安妮亚大学） ； Archimedes, Athena Research Center, Greece（阿提卡研究中心） 纠错

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

Agent-based Condition Monitoring Assistance with Multimodal Industrial Database Retrieval Augmented Generation

Karl Löwenmark, Daniel Strömbergsson, Chang Liu, Marcus Liwicki, Fredrik Sandin

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

An Integrated Platform for LEED Certification Automation Using Computer Vision and LLM-RAG

Jooyeol Lee

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

SubGCache: Accelerating Graph-based RAG with Subgraph-level KV Cache

Qiuyu Zhu, Liang Zhang, Qianxiong Xu, Cheng Long, Jie Zhang

机构 * Nanyang Technological University（南洋理工大学） ； Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州）） 纠错

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

A Visual RAG Pipeline for Few-Shot Fine-Grained Product Classification

Bianca Lamm, Janis Keuper

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

EACO-RAG: Towards Distributed Tiered LLM Deployment using Edge-Assisted and Collaborative RAG with Adaptive Knowledge Update

Jiaxing Li, Chi Xu, Lianchen Jia, Feng Wang, Cong Zhang, Jiangchuan Liu

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

Can Long-Context Language Models Subsume Retrieval, RAG, SQL, and More?

Jinhyuk Lee, Anthony Chen, Zhuyun Dai, Dheeru Dua, Devendra Singh Sachan, Michael Boratko, Yi Luan, Sébastien M. R. Arnold, Vincent Perot, Siddharth Dalmia, Hexiang Hu, Xudong Lin, Panupong Pasupat, Aida Amini, Jeremy R. Cole, Sebastian Riedel, Iftekhar Naim, Ming-Wei Chang, Kelvin Guu

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

Comments 29 pages. Dataset available at https://github.com/google-deepmind/loft

GADR: Gathering Architecture Decision Records from Meeting Transcriptions

GADR：从会议转录文本中收集架构决策记录

Lucas Daniel Costa da Silva, Kiev Gama

专题命中 图谱与结构化RAG ：RAG(summary_cn,abstract)；分类 cs.AI

AI总结 本文提出多智能体自校正工作流GADR，从原始会议转录文本提取架构决策并生成Nygard格式ADR，经评估其效果优于零样本、少样本基线，还探讨了RAG丰富化的权衡及自动架构文档可追溯性问题。

Mandol: An Agglomerative Agent Memory System for Long-Term Conversations

Mandol：一种用于长期对话的聚合式智能体记忆系统

Yuhan Zhang, Zhiyuan Guo, Ziheng Zeng, Wei Wang, Wentao Wu, Lijie Xu

机构 * Institute of Software, Chinese Academy of Sciences（中国科学院软件研究所） ； Microsoft Research（微软研究院） 纠错

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

AI总结 提出Mandol聚合式记忆系统，通过统一内存原生架构整合碎片化记忆，结合层次化记忆模型、聚合语义数据结构与量化查询机制，在长期对话基准上取得最佳准确率并实现5.4倍检索加速。

Comments 10 pages, 3 figures