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

Latest papers 503

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LERA: LLM-Enhanced RAG for Ad Auction in Generative Chatbots
May 15, 2026Haoran Sun, Xinrui Song, Xinyu Zhang +7Retrieval-Augmented Generation Mechanism Design
Peking University · Shandong University · Alibaba Group
Retrieval-Augmented Large Language Models for Schema-Constrained Clinical Information Extraction
May 14, 2026A H M Rezaul Karim, Ozlem UzunerRetrieval-Augmented Generation Healthcare
George Mason University, VA, USA
MeMo: Memory as a Model
May 14, 2026Ryan Wei Heng Quek, Sanghyuk Lee, Alfred Wei Lun Leong +6Retrieval-Augmented Generation Memory-Augmented Language Models
Institute of Data Science, National University of Singapore, Singapore · Integrative Sciences and Engineering Programme, NUSGS, Singapore · Agency for Science, Technology, Research (A*STAR), Singapore +6
When Retrieval Hurts Code Completion: A Diagnostic Study of Stale Repository Context
May 14, 2026Haojun Weng, Qianqian Yang, Hao Fu +2Retrieval-Augmented Generation Retrieval Robustness
Independent Researcher, California, USA · Independent Researcher, Beijing, China
Does RAG Know When Retrieval Is Wrong? Diagnosing Context Compliance under Knowledge Conflict
May 14, 2026Yihang Chen, Pin Qian, Su Wang +4Retrieval-Augmented Generation Knowledge Conflicts in Language Models
1Georgia Institute of Technology · 2Carnegie Mellon University · University of California San Diego
Why Retrieval-Augmented Generation Fails: A Graph Perspective
May 13, 2026Kai Guo, Xinnan Dai, Zhibo Zhang +5Retrieval-Augmented Generation LLM Grounding
Michigan State University · Massachusetts Institute of Technology
Thinking Ahead: Prospection-Guided Retrieval of Memory with Language Models
May 13, 2026Harshita Chopra, Krishna Kant Chintalapudi, Suman Nath +2Retrieval-Augmented Generation Long-Term Conversational Memory
University of Washington, Seattle, USA · 2Microsoft Research, Redmond, USA
Derivation Prompting: A Logic-Based Method for Improving Retrieval-Augmented Generation
May 13, 2026Ignacio Sastre, Guillermo Moncecchi, Aiala RosáLogical Reasoning Retrieval-Augmented Generation
Instituto de Computación, Facultad de Ingeniería, Universidad de la República Montevideo, Uruguay
Retrieval-Augmented Tutoring for Algorithm Tracing and Problem-Solving in AI Education
May 13, 2026Mragisha Jain, Tirth Bhatt, Griffin Pitts +6Retrieval-Augmented Generation Intelligent Tutoring Systems
North Carolina State University · University of Pittsburgh · University of California, Berkeley +1
Retrieval is Cheap, Show Me the Code: Executable Multi-Hop Reasoning for Retrieval-Augmented Generation
May 13, 2026Jiashuo Sun, Jimeng Shi, Yixuan Xie +10Multi-Hop QA Retrieval-Augmented Generation
University of Illinois Urbana-Champaign · Hong Kong University of Science and Technology · Texas A&M University +1
LongMemEval-V2: Evaluating Long-Term Agent Memory Toward Experienced Colleagues
May 12, 2026Di Wu, Zixiang Ji, Asmi Kawatkar +4Computer-Use Agent Benchmarks Retrieval-Augmented Generation
University of California, Los Angeles
LegalCheck: Retrieval- and Context-Augmented Generation for Drafting Municipal Legal Advice Letters
May 12, 2026Virgill van der Meer, Julien RossiRetrieval-Augmented Generation Legal Reasoning
Municipality of Amsterdam · University of Amsterdam
Adversarial SQL Injection Generation with LLM-Based Architectures
May 11, 2026Ali Karakoc, H. Birkan YilmazRetrieval-Augmented Generation Adversarial Attacks
Department of Computer Engineering, NETLAB, Bogazici University, Istanbul, Turkey.
RUBEN: Rule-Based Explanations for Retrieval-Augmented LLM Systems
May 11, 2026Joel Rorseth, Parke Godfrey, Lukasz Golab +2Retrieval-Augmented Generation Language Model Safety Evaluation
University of Waterloo · York University · AT&T Chief Data Office
Qwen Goes Brrr: Off-the-Shelf RAG for Ukrainian Multi-Domain Document Understanding
May 11, 2026Anton Bazdyrev, Ivan Bashtovyi, Ivan Havlytskyi +2Retrieval-Augmented Generation LLM Reranking
National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute"
Knowledge Poisoning Attacks on Medical Multi-Modal Retrieval-Augmented Generation
May 11, 2026Peiru Yang, Haoran Zheng, Tong Ju +6Multimodal RAG Adversarial Attacks on VLMs
1Tsinghua University · 2Beijing University of Posts and Telecommunications · 3Northwestern Polytechnical University +1
Trust or Abstain? A Self-Aware RAG Approach
May 11, 2026Xi Zhu, Ziqi Wang, Kai Mei +5Retrieval-Augmented Generation Knowledge Conflicts in Language Models
Rutgers University · University of Illinois Urbana-Champaign
Federated Language Models Under Bandwidth Budgets: Distillation Rates and Conformal Coverage
May 11, 2026Prasanjit Dubey, Xiaoming HuoRetrieval-Augmented Generation Communication-Efficient Distributed Training
H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA 30332, U.S.A.
An LLM-RAG Approach for Healthy Eating Index-Informed Personalized Food Recommendations
May 11, 2026Yibin Wang, Yanjie Yang, Grace Melo Guerrero +2Retrieval-Augmented Generation Recommender Systems
Department of Biological and Agricultural Engineering, Texas A&M AgriLife Research, Dallas, TX, USA · Department of Agricultural Economics, Texas A&M University, College Station, TX, USA
Byte-Exact Deduplication in Retrieval-Augmented Generation: A Three-Regime Empirical Analysis Across Public Benchmarks
May 10, 2026Sietse SchelpeLLM Inference Efficiency Retrieval-Augmented Generation
Corbenic AI, Inc.
EpiGraph: Building Generalists for Evidence-Intensive Epilepsy Reasoning in the Wild
May 10, 2026Yuyang Dai, Zheng Chen, Jathurshan Pradeepkumar +4KG Construction Retrieval-Augmented Generation
Florida State University · The University of Osaka · University of Illinois Urbana-Champaign
GRC: Unifying Reasoning-Driven Generation, Retrieval and Compression
May 9, 2026Zhongtao Miao, Qiyu Wu, Yoshimasa TsuruokaRetrieval-Augmented Generation Memory-Augmented Language Models
The University of Tokyo · Tsuruoka Lab. · Independent Researcher
CDS4RAG: Cyclic Dual-Sequential Hyperparameter Optimization for RAG
May 8, 2026Pengzhou Chen, Tao ChenRetrieval-Augmented Generation Hyperparameter Optimization
School of Computer Science and Engineering, UESTC, Chengdu, China · IDEAS Lab, University of Birmingham, Birmingham, UK
Towards Highly-Constrained Human Motion Generation with Retrieval-Guided Diffusion Noise Optimization
May 8, 2026Hanchao Liu, Fang-Lue Zhang, Shining Zhang +2Constrained Generative Modeling Retrieval-Augmented Generation
Tsinghua University · University of New South Wales
GRACE-RAG: Governed Retrieval Architecture for Canonical Evidence Synthesis, Enabling Lightweight Deployment in Closed-Domain Institutional Settings
May 8, 2026Asit Desai, Aman Kumar, Prashant DevadigaRetrieval-Augmented Generation Graph Retrieval-Augmented Generation
National Payments Corporation of India
PRA-RAG: Provably Robust Aggregation in Retrieval-Augmented Generation against Retrieval Corruption
May 8, 2026Xue Tan, Yi Zheng, Chang Huo +7Retrieval-Augmented Generation RAG Poisoning Attacks
School of Computer Science, Fudan University, Shanghai, China · Institute of Big Data, Fudan University, Shanghai, China · Department of Computer Science, Worcester Polytechnic Institute, MA, USA
LARAG: Link-Aware Retrieval Strategy for RAG Systems in Hyperlinked Technical Documentation
May 8, 2026Giorgia Bolognesi, Claudio Estatico, Ulderico Fugacci +3Document Understanding Retrieval-Augmented Generation
1Rulex s.r.l., Genova, Italy · Department of Mathematics (DIMA), University of Genoa, Italy · Institute of Applied Mathematics and Information Technologies “Enrico Magenes” (IMATI), National Research Council, Italy +1
From Clouds to Hallucinations: Atmospheric Retrieval Hijacking in Remote Sensing Vision-Language RAG
May 8, 2026Jiaju Han, Chao Li, Chengyin Hu +8Multimodal RAG Retrieval-Augmented Generation
China University of Petroleum, Beijing at Karamay, Karamay, Xinjiang 834000, China
HPC-LLM: Practical Domain Adaptation and Retrieval-Augmented Generation for HPC Support
May 8, 2026Nourin Shahin, Izzat AlsmadiRetrieval-Augmented Generation Domain Adaptation
Texas A&M University San Antonio
Can LLMs Take Retrieved Information with a Grain of Salt?
May 7, 2026Behzad Shayegh, Mohamed Osama Ahmed, Fred Tung +1Retrieval-Augmented Generation LLM Evaluation
RBC Borealis

May 15, 2026, cs.IRJ/K move · Enter open · S save

LERA: LLM-Enhanced RAG for Ad Auction in Generative Chatbots

Haoran Sun, Xinrui Song, Xinyu Zhang, Zhaohua Chen+6

Peking University · Shandong University · Alibaba Group

Retrieval-Augmented Generation Mechanism Design Online Advertising LLM Reranking Conversational Agents

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The integration of advertising auction mechanisms into large language model (LLM)-based chatbots presents a significant opportunity for commercialization, yet poses unique challenges in balancing relevance, efficiency, and user experience. Recently, Feizi et al.~~\citep{feizi2023online} and Hajiaghayi et al.~~\citep{hajiaghayi2024ad} outlined a retrieve-then-generate paradigm that decouples retrieval and generation, offering lightweight ad insertion and payment determination. However, current retrieval relies solely on text embedding similarity, which may lead to commercial misinterpretation and issues such as repetitive insertions. In this paper, we propose LERA, a two-stage retrieve-then-generate auction framework tailored for LLM chatbots. In the first stage, embedding-based coarse filtering pre-selects a small set of candidate advertisers. In the second stage, the LLM itself is queried with a carefully designed prompt to produce logits over candidates, which serve as refined organic relevance scores. These scores are combined with bids, and a critical-value payment rule accounts for both the coarse-filtering and fine-ranking thresholds, ensuring truthfulness for utility-maximizing advertisers. The framework naturally extends to multiple ad insertions within dynamic dialogue flows and long responses. Experiments on a synthetic advertiser-query benchmark show that LERA substantially improves ad selection accuracy and insertion diversity while incurring only controllable latency overhead.

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Latest papers 503