Retrieval-Augmented Generation

Also known as RAG

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49 papers in the last four weeks, up 36% on the four weeks before. 0.5% of all new papers.

Jul 13Week of Sep 28

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TriShieldRAG: A Three-Ring Defense-in-Depth Framework Against Knowledge Corruption in Retrieval-Augmented Generation
Jul 26, 2026Susil Kumar Mohanty, Rohit Patel, Kosuru Yuvaraj +2Retrieval-Augmented Generation RAG Poisoning Attacks
Department of Computer Science and Engineering, Indian Institute of Technology Jodhpur, Jodhpur, India
HVM-GraphRAG: Holistic-View Multimodal Graph Retrieval-Augmented Generation on Complex Document
Jul 26, 2026Xin He, Yili Wang, Wenqi Fan +4Multimodal RAG Retrieval-Augmented Generation
Jilin University Changchun, China · The Hong Kong Polytechnic University Hong Kong SAR, China
Language-Routed RAG and Direct Option Scoring for Multilingual Financial QA: DS@GT at FinMMEval
Jul 24, 2026Justice Ayela, Kabir SahniMultilingual Language Model Evaluation Retrieval-Augmented Generation
Georgia Institute of Technology, North Ave NW, Atlanta, GA 30332
Search-G1: Grounded Search Agents via Representation-Based Intrinsic Rewards
Jul 24, 2026Cheng Ruoxi, Ma Haoxuan, Zhang Hongyi +7Retrieval-Augmented Generation LLM Grounding
1Fudan University · 2Tencent · 3Nanjing University +2
Towards Trustworthy and Cost-Efficient Data Integration: From Naïve RAG to Agentic RAG
Jul 24, 2026Chuangtao Ma, Arijit KhanRetrieval-Augmented Generation Multi-Agent LLM Systems
Aalborg University, Denmark · Bowling Green State University, USA
Benchmarking Fine-tuning and Retrieval Strategies for a Multimodal Language Model on the NRC Reactor Operator Licensing Examination
Jul 24, 2026Isak Hwang, Yoon Pyo LeeRetrieval-Augmented Generation LLM Fine-Tuning
aDepartment of Nuclear Engineering, Hanyang University, 222 Wangsimni-ro, 04763, Seongdong-gu, Seoul, South Korea · bThe Grainger College of Engineering, Nuclear, Plasma & Radiological Engineering, University of Illinois Urbana-Champaign, Urbana, IL, USA
Leveraging External Knowledge for Historical Document Restoration via Retrieval-Augmented Large Language Models
Jul 24, 2026Gabeen Kim, Kyeongpil KangRetrieval-Augmented Generation Historical Document Processing
Department of AI Convergence, Kangwon National University · Department of Computer Science and Engineering, Kangwon National University
Reinforcement Learning for Large Language Model Selective Evidence Adoption from Contaminated Retrieval Results
Jul 22, 2026Yanyu Chen, Yue Li, Yongyi Cui +2RL for Language Models Retrieval-Augmented Generation
East China Normal University · Shandong University
VizRAG: Enhancing Retrieval-Augmented Generation with Hypergraph Visualization
Jul 22, 2026Yanbin Wei, Yang Chen, Renling Gan +7Multimodal RAG Retrieval-Augmented Generation
1Southern University of Science and Technology · 2Hong Kong University of Science and Technology · 3Huawei Research +1
Selective State-Space Adaptation and Retrieval for Language Model Reasoning
Jul 21, 2026Atahan Dokme, Larry HeckRetrieval-Augmented Generation Selective SSMs
AI Virtual Assistant (AVA) Lab Georgia Institute of Technology
TAP-RAG: Task-Aware Policy Control for Long-Document Multimodal Question Answering
Jul 21, 2026Zhong Ji, Keqi Jin, Yan Zhang +1Multimodal RAG Retrieval-Augmented Generation
School of Electrical and Information Engineering, Tianjin University · The International Joint Institute of Tianjin University, Fuzhou, Tianjin University · Department of Automation, Tsinghua University
AILQA: Evaluating AI-Driven Legal Question Answering Systems for the Indian Legal System
Jul 21, 2026Shubham Kumar Nigam, Shubham Kumar Mishra, Noel Shallum +2Retrieval-Augmented Generation Legal NLP
Computer Science and Engineering, Indian Institute of Technology, Kanpur, 208016, Uttar Pradesh, India. · School of Computer Science, University of Birmingham, Dubai, UAE. · Law, Symbiosis Law School, Pune, 411014, Maharashtra, India. +1
RF-Agent: A Practical Framework for Building Language Agents for RFIC Design
Jul 21, 2026Yueqi Xing, Houbo He, Jolie Wang +5Retrieval-Augmented Generation LLM Fine-Tuning
ECE Department, Rice University, Houston, TX, USA · ECE Department, The George Washington University, Washington, D.C., USA
RAGAL: A Frugal, Fully Local Retrieval-Augmented Assistant for Technical Support at a Government Agency
Jul 21, 2026Dan MusetoiuRetrieval-Augmented Generation Fine-Tuning
IT Department, Agency for Financing Rural Investments (AFIR), Romania
Vector Search As Nearest Neighbor Matching: RAG-based Policy Learning in Causal Inference
Jul 20, 2026Masahiro Kato, Taka KatoRetrieval-Augmented Generation Causal Inference
∗The University of Tokyo. · †NP-hard Inc.
Semantically Similar, Yet Not Answerable: Diagnosing the Semantic-Answerability Gap in Table RAG
Jul 20, 2026Jiaming Tian, Liyao Li, Wentao Ye +6Table QA Retrieval-Augmented Generation
Zhejiang University · Bank of Hangzhou Co., Ltd. · Zhejiang Lab
MPR-CiteG: Enhancing RAG with Multi-Portfolio Retrieval and Citation-Grounded Generation
Jul 20, 2026Hyewon Lee, Minkyung Song, Junghyun Oh +2Retrieval-Augmented Generation LLM Hallucination Mitigation
Data Intelligence Laboratory, Department of Computer Science and Engineering, Chungnam National University, Daejeon 34134, Republic of Korea
SIRIN: A Unified Toolkit for Detecting Contextual Hallucinations in Retrieval-Augmented and Memory-Grounded LLM Systems
Jul 20, 2026Julia Belikova, Rauf Parchiev, Mikhail Filimonov +3Retrieval-Augmented Generation LLM Answer Verification
Sber AI Lab
D-NOVA: In-Storage Retrieval Accelerator via Dual-Bound 3D NAND-Optimized Similarity Search with Vector Adaptation
Jul 20, 2026Chang Eun Song, Sumukh Pinge, Tianqi Zhang +3Retrieval-Augmented Generation Dense Retrieval
University of California, San Diego La Jolla, USA
TurboVec: A Case Study in Cost-Efficient Private Retrieval for Enterprise RAG via Codebook-Oblivious Quantization
Jul 18, 2026Navnit Shukla, Kamal Pandey, Omsankar TiwariRetrieval-Augmented Generation Vector Quantization
Snowflake Inc. Tustin, California, USA · Rivian Automotive Inc. Irvine, United States of America · Google Deepmind Sunnyvale, California, USA
Chronofy: A Temporal-Logical Decay Architecture for Information Validity in Time-Aware Retrieval-Augmented Generation
Jul 17, 2026Muntaser Syed, Marius Silaghi, Sheikh Abujar +1Retrieval-Augmented Generation Temporal Consistency
Florida Institute of Technology · University of Alabama at Birmingham · Daffodil International University
RIMS: Preference Optimization via Smoothed Multi-pair Aggregation for Small-Scale LLM Retrieval-Augmented Generation
Jul 17, 2026Pei Tian, Zihan Dong, Tianci Liu +2Retrieval-Augmented Generation Small Language Models
Department of Computer Science, Columbia University · Department of Statistics, Rutgers University · School of Electrical and Computer Engineering, Purdue University +1
Auditing Retrieval-Augmented LLM Hypotheses for Longitudinal Cell Painting Morphology
Jul 17, 2026Gilchan Park, Guang Zhao, Byung-Jun Yoon +1Retrieval-Augmented Generation LLM Auditing
Brookhaven National Laboratory Upton, New York, USA
Retrieval-Augmented Generation in LLMs for Mental Health: Quantifying the Incremental Contribution of Retrieval Within a Layered Safety Architecture
Jul 17, 2026Anand Gupta, Akshat Surolia, Shubham Mishra +2Retrieval-Augmented Generation Intent Classification
Wysa Inc., Boston, MA, United States
DS@GT ARC at LongEval: Citation Integrity and Factual Grounding in Scientific QA
Jul 15, 2026Brandon Michaels, Brendon JohnsonRetrieval-Augmented Generation Citation Verification
Georgia Institute of Technology, North Ave NW, Atlanta, GA 30332
Chat2Scenic: An Iterative RAG-Based Framework for Scenario Generation in Autonomous Driving
Jul 15, 2026Yuan Gao, Wenting Miao, Mattia Piccinini +3Autonomous Driving Benchmarks Retrieval-Augmented Generation
Professorship of Autonomous Vehicle Systems, TUM School of Engineering and Design, Technical University of Munich, 85748 Garching, Germany; Munich Institute of Robotics and Machine Intelligence (MIRMI) · University College London, London, United Kingdom
MamaBench: Benchmarking LLM Robustness in Maternal and Child Health Diagnosis through Counterfactual Clinical Perturbation
Jul 15, 2026Thanni Adewuyi, Anuoluwa Sotome, Samuel Okoko +7Retrieval-Augmented Generation Healthcare
Helpmum Africa · University of Ibadan
Pezego-HITL: A policy-grounded large language model architecture for agricultural extension in Ghana
Jul 15, 2026Shunbao Li, Zhipeng Yuan, Amoako Ofori +5Retrieval-Augmented Generation LLM Evaluation
School of Computer Science, University of Sheffield, Regent Court (CS), 211 Portobello, Sheffield, S1 4DP, United Kingdom · Institute for Environment and Sanitation Studies, College of Basic and Applied Sciences, University of Ghana, P. O. Box LG 209, Legon, Accra, Ghana · Mutus Tech Ltd, 54 St. James Street, Liverpool, L1 0AB, England
Cost-Pragmatic Quality Gating and Selection-Fusion Multi-Model Combiners for BioASQ Phases A+ and B
Jul 15, 2026Dima Galat, Marian-Andrei RizoiuRetrieval-Augmented Generation Biomedical QA
University of Technology Sydney, Australia
Learning Engagement Assistant (LEA): Cross-Course Scalability and Classroom Evaluation of an Agentic AI Tutoring System
Jul 15, 2026Teri Rumble, Javad Zarrin, P. George Lovell +1Retrieval-Augmented Generation Intelligent Tutoring Systems
Faculty of Design, Informatics and Business, Abertay University, Dundee, United Kingdom.

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TriShieldRAG: A Three-Ring Defense-in-Depth Framework Against Knowledge Corruption in Retrieval-Augmented Generation

Susil Kumar Mohanty, Rohit Patel, Kosuru Yuvaraj, Jeenal Chaudhary+1

Department of Computer Science and Engineering, Indian Institute of Technology Jodhpur, Jodhpur, India

Retrieval-Augmented Generation RAG Poisoning Attacks Retrieval Robustness RAG Security RAG Robustness

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Retrieval-Augmented Generation (RAG) lets a large language model answer questions using documents retrieved from an external knowledge base at query time. This makes RAG useful for private data, fast-changing information, and reducing hallucination, but it also means the model's answer is only as trustworthy as whatever the retriever hands it. If the knowledge base accepts writes from more than one party, an attacker needs only a handful of adversarial documents to steer the model toward a chosen wrong answer. PoisonedRAG demonstrated this: as few as five crafted documents flip an undefended system's answer roughly 90% of the time, and three natural single-stage defenses (perplexity filtering, query paraphrasing, knowledge-base expansion) leave attack success at 30% or higher. We built TriShieldRAG to close that gap. Rather than relying on one checkpoint, we place three independent, formally specified rings across the pipeline: an Ingest Guard that screens documents for lexical and statistical poisoning signatures; a Retrieval Scorer that re-ranks the retrieved set by a provenance and consistency-weighted trust score; and a Cross-LLM Consensus stage that polls three architecturally diverse language models (Claude, Mistral Small, Llama 3.2) and allows one bounded re-retrieval on disagreement. We derive the conditions under which Rings 2 and 3 are expected to work: a minority-poison assumption and an explicit provenance-tag assumption. Our reported configuration is consistent with this analysis, though we have not yet run the controlled poison-fraction sweep needed to confirm it independently. Evaluated against the non-adaptive attacker from the original PoisonedRAG, over a 5,000-document Wikipedia knowledge base with 10 target questions, the full pipeline reduces attack success rate from roughly 91% to roughly 13% while preserving accuracy on benign queries.

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