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.

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Is Memorization Context-Sensitive? Prefix-Based Extraction Beyond Isolated Prefixes
Oct 8, 2026Ali Satvaty, Narjes Sharafi, Jirui Qi +2Memorization in Language Models Privacy Leakage in Language Models
University of Groningen · Leiden University
SignRAG: Unified Retrieval-Augmented Gloss-Free Sign Language Translation
Oct 8, 2026Zhi Rao, Yucheng Zhou, Qianran Sun +8Sign Language Translation Retrieval-Augmented Generation
Faculty of Innovation Engineering, Macau University of Science and Technology, Macau, China · SKL-IOTSC, CIS, University of Macau, Macau, China · MAIS, Institute of Automation, Chinese Academy of Sciences, Beijing, China +2
RIT-RAG: Navigating Document Corpora with Retrieval-Induced Trees
Oct 8, 2026Meghanadh Pulivarthi, Swaraj Kumar Biswal, Kushagra Bhushan +3Retrieval-Augmented Generation Agentic RAG
IBM · IIT Kharagpur
RAG-Stress: Probing the Limits of Evidence Reliance in Retrieval-Augmented Generation
Oct 8, 2026Shunyuan Zhou, Hao Chen, Tianyu Wang +3Retrieval-Augmented Generation RAG Evaluation
Beijing University of Posts and Telecommunications · Humanlaya Data · Beijing Key Laboratory of Key Technologies for AI+ Domain Applications +1
RECAST: Learning to Compute the Right Context through Adaptive Evidence Routing
Oct 7, 2026Yilun Hao, Krishna Sayana, Isabella Ye +4Retrieval-Augmented Generation Agentic RAG
MIT · Google Research
EntroPrefill: Renyi-Guided Context Pruning with Conditional Stability Guarantees for Retrieval-Augmented Generation
Oct 7, 2026Inbasekaran SToken Pruning Transformer Inference
SRM Institute of Science and Technology, India
The Attribution Blind Spot: Layerwise Trajectory Diagnostics for Source Reliance in Retrieval-Augmented Language Models
Oct 7, 2026Zhe Yu, Wenpeng Xing, Yunzhao Wei +4LLM Interpretability Retrieval-Augmented Generation
Zhejiang University · Binjiang Institute of Zhejiang University · East China Normal University +3
Finding the Right Balance: Relevance and Diversity in LLM Retrieval
Oct 7, 2026Guillaume Brouillette, Faustin Kagabo, Usef Faghihi +1Information Retrieval Retrieval-Augmented Generation
Université du Québec à Trois-Rivières, Trois-Rivières, Canada
From Chunks to Functional Evidence: Function-Aware Retrieval for EDA Documentation QA
Oct 7, 2026Xiaotian Qiu, Kairui Liu, Shi Chenyi +3Evidence Retrieval Retrieval-Augmented Generation
Zhejiang University · Shanghai Innovation Institute
UNREAL: Unifying Retrieval and Long-Context with a Single Model
Oct 6, 2026Edan Kinderman, Elad Hoffer, Yochai Blau +4Retrieval-Augmented Generation Knowledge Augmentation for Language Models
NVIDIA · Technion
Agentic AutoRAG: RAG Pipeline Optimization through Reasoning-Driven Agents
Oct 6, 2026Lasse B. Strand, Robert Jakob, Kevin O'Sullivan +1Retrieval-Augmented Generation Large Language Model-Guided Optimization
ETH Zurich
Mapping the RAG Landscape: A Four Axis Taxonomy of Efficiency, Defense, Interactivity, and Reasoning
Oct 1, 2026Meghana Sunil, Shravya V, Shravan Venkatraman +1Retrieval-Augmented Generation RAG Security
School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, India. · Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, UAE.
A Matryoshka Hierarchical RAG for Efficient Multi-Hop Question Answering
Oct 1, 2026Gianluca Bonifazi, Christopher Buratti, Michele Marchetti +5Multi-Hop QA Retrieval-Augmented Generation
Polytechnic University of Marche, Ancona, Italy · University of Modena and Reggio Emilia, Modena, Italy
Evaluating Biomedical Reranking for LLM-Based Question Answering over Longitudinal Clinical Notes
Oct 1, 2026Maryam Shahbaz Ali, Laura B. Strachan, Caitlin Sherman +3Cross-Encoder Reranking Retrieval-Augmented Generation
Children’s National Hospital, Washington, DC, USA · University of Florida, Gainesville, FL, USA · George Washington University, Washington DC, USA
MOMAT: Mixture of Multiple Atlases for Low-Power Jailbreak Defense of Quantized LLMs
Oct 1, 2026Boyang Li, Bingyu Shen, Weihao Hong +6Retrieval-Augmented Generation LLM Safety
Department of Computer Science, Kean University, 1000 Morris Ave, Union, 07083, NJ, USA · Department of Computer Science and Engineering, University of Notre Dame, 257 Fitzpatrick Hall of Engineering, Notre Dame, 46556, IN, USA · McGill University, 845 Sherbrooke Street, Montréal, H3A 0G4, Québec, Canada +2
BITEM at the NTCIR-19 R2C2 Task: Predicting Confidence from Agentic RAG Pipeline Signals
Sep 29, 2026Julien Knafou, Luc Mottin, Alexandre Flament +3Retrieval-Augmented Generation Agentic RAG
HES-SO, Geneva, Switzerland · SIB Swiss Institute of Bioinformatics, Geneva, Switzerland
TAEC: Trajectory-Aware Evidence Coordination for Multi-Step Visual RAG
Sep 29, 2026Yalun Wu, Bingzhou Wang, Boyang Wang +5Multimodal RAG Visual Question Answering
NExT++ Lab, National University of Singapore
Bridging Semantic Gaps in RAG through Generated Context Knowledge Fusion
Sep 29, 2026Xinkai Du, Chao Lv, Yalin Sun +3Retrieval-Augmented Generation Semantic Search
Beijing Wanlian Zhilian Technology Corporation Limited, Beijing, China · Department of Computer Science and Technology, Tsinghua University, Beijing, China · Sunshine Digital Intelligence Tech Co., Ltd., Beijing, China
Lost in Conversation or Lost in Translation? Diagnosing Multi-Turn Degradation in RAG
Sep 29, 2026Pranav Handa, Ariful AzadRetrieval-Augmented Generation LLM Reliability
Texas A&M University
PILLAR: Private Inverted-Index Lexical Lookup for Augmented Retrieval
Sep 28, 2026Truong Son Nguyen, Daniel Blackley, Ni Trieu +1Retrieval-Augmented Generation Lexical Retrieval
Arizona State University · Department of Computer Science Arizona State University Tempe, AZ 85281, USA · George Mason University +1
CacheRepair: Learning to Repair Cross-Chunk Context in RAG for KV Cache Fusion
Sep 28, 2026Genglin Wang, Wangsong Yin, Yeerzhati Abudunuer +3Retrieval-Augmented Generation KV-Cache Management
The Chinese University of Hong Kong · Peking University · The Hong Kong University of Science and Technology
Page-Aware Retrieval-Augmented Generation for EvalLLM 2026: A Five-Variant Study on French PDFs
Sep 28, 2026Abdelhak keliousRetrieval-Augmented Generation Question Decomposition
Capsens, France
RAGWarrant: Evidence-Preserving Governance for RAG Policy Promotion Under Quality, Cost, Latency, and Risk Constraints
Sep 28, 2026Richard Krueger, Lucas Krause, Zach PocquetteRetrieval-Augmented Generation RAG Evaluation
Independent open-source research project
STITCH-RAG: Spatio-Temporal Influence Tracing over Topic Hypergraphs for Multi-Hop Retrieval-Augmented Generation
Sep 28, 2026Haodong Yang, Mengzhu Chen, Jia CaiMulti-Hop QA Retrieval-Augmented Generation
School of Statistics And Data Science, Guangdong University of Finance & Economics Guangzhou, China
RelaxKV: Recomputation Guided by the Query with Sparse Context Attention for Efficient KV Cache Reuse
Sep 27, 2026Ruoling Qi, Yirui Liu, Xuaner Wu +5Retrieval-Augmented Generation KV-Cache Management
Shanghai Jiao Tong University · Institute of Artificial Intelligence, China Telecom (TeleAI) · State University of New York at Buffalo
QuPID: Quantum Parameter-Efficient Input-Dependent Retrieval Adaptation for Medical RAG
Sep 27, 2026Hyojun Ahn, Emily Jimin Roh, Soohyun Park +3Quantum Feature Maps Retrieval-Augmented Generation
Korea University · Sookmyung Women’s University · Virginia Tech +2
Byzantine-Robust Federated RAG via Aligned Calibration and Fixed-Membership Conformal Prediction
Sep 27, 2026Prasanjit Dubey, Aritra Guha, Xiaoming HuoRetrieval-Augmented Generation Byzantine-Robust Federated Learning
H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA 30332, U.S.A. · AT&T Chief Data Office
AdaTutoRank: Learning to Rerank Document Sets via Adaptive Tutoring Optimization for RAG and Deep Research
Sep 26, 2026Kailin Jiang, Lei Liu, Jian Xi +6Retrieval-Augmented Generation Learning to Rank
University of Science and Technology of China · Yuanbao Team, Tencent
To Trust or Not to Trust: Retrieval-Augmented Fact Checking in Speech
Sep 24, 2026Debajyoti Mazumder, Mamta, Abhirama Subramanyam PenamakuriAudio-Language Model Evaluation Retrieval-Augmented Generation
IISER Bhopal · King’s College London · MBZUAI
Return or Revise? Learning When Revision Helps Retrieval-Augmented QA
Sep 24, 2026Nicholas Kashani Motlagh, Tim Anderson, Jeremy Gwinnup +1Retrieval-Augmented Generation LLM Answer Verification
DCS Corp · Air Force Research Laboratory

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Is Memorization Context-Sensitive? Prefix-Based Extraction Beyond Isolated Prefixes

Ali Satvaty, Narjes Sharafi, Jirui Qi, Suzan Verberne+1

University of Groningen · Leiden University

Memorization in Language Models Privacy Leakage in Language Models Retrieval-Augmented Generation

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Large language models (LLMs) can expose memorized training sequences under prefix-based extraction: given a prefix from a training example, the model may assign high probability to the original continuation. In deployed systems, however, prefixes are rarely evaluated in isolation. They often appear together with instructions, retrieved documents, or other task-specific context, as in retrieval-augmented generation (RAG). This motivates examining whether contextual conditioning mitigates memorization or merely changes the set of memorized samples that become extractable. We investigate this issue through paired item-level measurements of probabilistic suffix extraction. For each prefix-suffix pair, we score the target suffix under an empty prompt and under retrieved contexts of varying relevance, across three open-weight instruction-tuned models. We find that context does not simply erase memorization. Instead, extractable memorization consists of a context-robust core and a context-sensitive boundary. Many samples that are extractable without context remain extractable under the retrieved context, especially as the prefix length increases. At the same time, context mainly affects marginal samples near the extraction threshold: it suppresses some exposures, but also enables new ones that are missed by prefix-only evaluation. These findings qualify the view that RAG reduces memorization risk. Context can lower aggregate extraction by suppressing boundary cases, yet robustly extractable samples persist, and context-enabled extractability remains security-relevant.

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