Retrieval-Augmented Generation

Also known as RAG

Retrieval-augmented generation (RAG) studies how to give language models relevant information from documents, databases, or other sources at answer time, improving access to knowledge that may be missing or outdated in model parameters. Common approaches combine dense or sparse search with hybrid retrieval, reranking, and document chunking; GraphRAG and agentic methods add structure or iterative search for multi-hop questions and long documents. Work spans open-domain QA benchmarks as well as specialized settings such as legal, medical, code, and multimodal retrieval. A central tension is that more retrieved context can improve evidence coverage but also introduce noise, conflicts, cost, and unsupported answers, so systems increasingly focus on selective retrieval, verification, citation fidelity, and realistic evaluation.

Latest papers

796 papers72 papers in the last four weeks, down 14% on the four weeks before. 0.4% of all new papers.

Differentiable Retrieval-Augmented Generation for Predicting Cellular Responses to Gene Perturbation
Mar 7, 2026Andrea Giuseppe Di Francesco, Andrea Rubbi, Rishabh Jain +1Single-Cell Perturbation Modeling Retrieval-Augmented Generation
University of Cambridge Wellcome Sanger Institute
Hit-RAG: Learning to Reason with Long Contexts via Preference Alignment
Mar 7, 2026Junming Liu, Yuqi Li, Shiping Wen +2Retrieval-Augmented Generation Multimodal RAG
Tongji University, Shanghai, China · The City University of New York, New York, USA · University of Technology Sydney, Sydney, Australia +2
SecureRAG-RTL: A Retrieval-Augmented, Multi-Agent, Zero-Shot LLM-Driven Framework for Hardware Vulnerability Detection
Mar 5, 2026Touseef Hasan, Blessing Airehenbuwa, Nitin Pundir +2LLMs for Cybersecurity Retrieval-Augmented Generation
School of Computing, Wichita State University · Department of Electrical and Computer Engineering, Auburn University · IBM
InfoFlow KV: Information-Flow-Aware KV Recomputation for Long Context
Mar 5, 2026Xin Teng, Canyu Zhang, Shaoyi Zheng +3Retrieval-Augmented Generation KV Caching
New York University · New York University Shanghai · Duke University +1
CBR-to-SQL: Rethinking Retrieval-based Text-to-SQL using Case-based Reasoning in the Healthcare Domain
Mar 5, 2026Hung Nguyen, Hans Moen, Pekka MarttinenCase-Based Reasoning Text-to-SQL
Department of Computer Science Aalto University · Department of Clinical Medicine Aalborg University
MedCoRAG: Interpretable Hepatology Diagnosis via Hybrid Evidence Retrieval and Multispecialty Consensus
Mar 5, 2026Zheng Li, Jiayi Xu, Zhikai Hu +4Medical Diagnosis Clinical Decision Support
School of Cyber Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China · School of Artificial Intelligence, Jilin University, Changchun, 130015, China · Department of Neurology, China-Japan Union Hospital of Jilin University, Changchun, 130033, China +2
Coordinated Semantic Alignment and Evidence Constraints for Retrieval-Augmented Generation with Large Language Models
Mar 4, 2026Xin Chen, Saili Uday Gadgil, Jiarong QiuRetrieval-Augmented Generation Evidence-Grounded Generation
Cornell University, Ithaca, USA · Virginia polytechnic institute and state University, Blacksburg, USA · University of Southern California, Los Angeles, USA
SGR3 Model: Scene Graph Retrieval-Reasoning Model in 3D
Mar 4, 2026Zirui Wang, Ruiping Liu, Yufan Chen +73D SGG Retrieval-Augmented Generation
Karlsruhe Institute of Technology, 76131 Karlsruhe, Germany. · Shenzhen University, Shenzhen, China. · Hunan University, Changsha, China.
Fast and Faithful: Real-Time Verification for Long-Document Retrieval-Augmented Generation Systems
Mar 4, 2026Xunzhuo Liu, Bowei He, Xue Liu +2LLM Answer Verification Retrieval-Augmented Generation
Red Hat · MBZUAI, McGill University
RAGNav: A Retrieval-Augmented Topological Reasoning Framework for Multi-Goal Visual-Language Navigation
Mar 4, 2026Ling Luo, Qiangian BaiVision-Language Navigation Visual Spatial Reasoning
Southwestern University of Finance and Economics, Chengdu, 611130, China
RAG-X: Systematic Diagnosis of Retrieval-Augmented Generation for Medical Question Answering
Mar 3, 2026Aswini Sivakumar, Vijayan Sugumaran, Yao QiangRetrieval-Augmented Generation RAG Evaluation
Oakland University, Rochester, MI, USA
Type-Aware Retrieval-Augmented Generation with Dependency Closure for Solver-Executable Industrial Optimization Modeling
Mar 3, 2026Y. Zhong, R. Huang, M. Wang +4Natural Language to Optimization Modeling Retrieval-Augmented Generation
School of Petroleum, China University of Petroleum (Beijing) at Karamay, Karamay, 834000, China
An experimental study of KV cache reuse strategies in chunk-level caching systems
Mar 3, 2026Samuel Cestola, Tianxiang Xia, Zheng Weiyan +2KV-Cache Reuse LLM Inference Acceleration
Huawei, Switzerland. · Huawei, China.
MERIT: Memory-Enhanced Retrieval for Interpretable Knowledge Tracing
Mar 3, 2026Runze Li, Kedi Chen, Guwei Feng +3Knowledge Tracing Educational Technology
School of Computer Science and Technology, East China Normal University, China · Shanghai Innovation Institute, China · WeChat AI, Tencent, China
Scaling Retrieval Augmented Generation with RAG Fusion: Lessons from an Industry Deployment
Mar 2, 2026Luigi Medrano, Arush Verma, Mukul ChhabraRetrieval-Augmented Generation RAG Evaluation
Dell Technologies, Austin, TX, USA
RealRoute: Dynamic Query Routing System via Retrieve-then-Verify Paradigm
Mar 2, 2026Jiahe Liu, Qinkai Yu, Jingcheng Niu +5Retrieval-Augmented Generation LLM Routing
Technical University of Denmark · University of Exeter · University of Toronto +4
NICO-RAG: Multimodal Hypergraph Retrieval-Augmented Generation for Understanding the Nicotine Public Health Crisis
Mar 2, 2026Manuel Serna-Aguilera, Raegan Anderes, Page Dobbs +1Hypergraph-Based RAG Multimodal RAG
University of Arkansas, Fayetteville, AR, 72701, USA · University of Arkansas for Medical Sciences, Little Rock, AR, 72205, USA
GAM-RAG: Gain-Adaptive Memory for Evolving Retrieval in Retrieval-Augmented Generation
Mar 2, 2026Yifan Wang, Mingxuan Jiang, Zhihao Sun +5Retrieval-Augmented Generation Adaptive RAG
1Fudan University.
KGiRAG: An Iterative GraphRAG Approach for Responding Sensemaking Queries
Mar 2, 2026Isabela Iacob, Melisa Marian, Gheorghe Cosmin SilaghiIterative RAG Retrieval-Augmented Generation
Babes-Bolyai University, Business Informatics Research Center, Cluj-Napoca, Romania
Legal RAG Bench: an end-to-end benchmark for legal RAG
Mar 2, 2026Abdur-Rahman Butler, Umar ButlerRetrieval-Augmented Generation RAG Evaluation
Isaacus
URAG: A Benchmark for Uncertainty Quantification in Retrieval-Augmented Large Language Models
Mar 2, 2026Vinh Nguyen, Cuong Dang, Jiahao Zhang +6Retrieval-Augmented Generation RAG Evaluation
Uppsala University · Virginia Tech · The Pennsylvania State University +5
Individual Turing Test: A Case Study of LLM-based Simulation Using Longitudinal Personal Data
Mar 1, 2026Minghao Guo, Ziyi Ye, Wujiang Xu +3Large Language Model-Based Role-Play Simulation Personalized Language Models
Rutgers University, New Brunswick, NJ, USA · Fudan University, Shanghai, China · Microsoft, New York, NY, USA
A unified foundational framework for knowledge injection and evaluation of Large Language Models in Combustion Science
Feb 27, 2026Zonglin Yang, Runze Mao, Tianhao Wu +3Knowledge Augmentation for Language Models LLM Evaluation
School of Mechanics and Engineering Science, Peking University, Beijing, 100871, PRChina · AI for Science Institute, Beijing, 100080, China
PREBA: Surgical Duration Prediction via PCA-Weighted Retrieval-Augmented LLMs and Bayesian Averaging Aggregation
Feb 27, 2026Wanyin Wu, Kanxue Li, Baosheng Yu +4Bayesian Model Averaging Clinical Prediction
Wanyin Wu, Haoyun Zhao, and Dapeng Tao are with the School of Information Science and Engineering, Yunnan University, Kunming 650500, China. · Kanxue Li and Yibing Zhan are with the School of Computer Science, Wuhan University, Wuhan 430072, China. · Baosheng Yu is with the Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore 639798. +1
SAGE-LLM: Towards Safe and Generalizable LLM Controller with Fuzzy-CBF Verification and Graph-Structured Knowledge Retrieval for UAV Decision
Feb 27, 2026Wenzhe Zhao, Yang Zhao, Ganchao Liu +4Language Model-Based Control LLM Agent Safety
School of Artificial Intelligence, OPtics and ElectroNics (iOPEN), Northwestern Polytechnical University, Xi’an 710072, China · Institute of Artificial Intelligence (TeleAI), China Telecom, China
MTRAG-UN: A Benchmark for Open Challenges in Multi-Turn RAG Conversations
Feb 26, 2026Sara Rosenthal, Yannis Katsis, Vraj Shah +3Retrieval-Augmented Generation RAG Evaluation
IBM Research, USA
Towards Faithful Industrial RAG: A Reinforced Co-adaptation Framework for Advertising QA
Feb 26, 2026Wenwei Li, Ming Xu, Tianle Xia +7Question Answering Retrieval-Augmented Generation
Tencent
Retrieval Challenges in Low-Resource Public Service Information: A Case Study on Food Pantry Access
Feb 25, 2026Touseef Hasan, Laila Cure, Souvika SarkarInformation Retrieval Conversational Search
Wichita State University, Wichita, KS, USA
RMIT-ADM+S at the MMU-RAG NeurIPS 2025 Competition
Feb 24, 2026Kun Ran, Marwah Alaofi, Danula Hettiachchi +9Retrieval-Augmented Generation Adaptive RAG
RMIT University Melbourne, Australia
DynaRAG: Bridging Static and Dynamic Knowledge in Retrieval-Augmented Generation
Feb 24, 2026Penghao Liang, Mengwei Yuan, Jianan Liu +4Retrieval-Augmented Generation Adaptive RAG
Northeastern University Boston, USA, 02115 · Independent Researcher Milpitas, USA, 95035 · Independent Researcher Austin, USA, 78613 +3

Mar 7, 2026J/K move · Enter open · S save

Differentiable Retrieval-Augmented Generation for Predicting Cellular Responses to Gene Perturbation

Andrea Giuseppe Di Francesco, Andrea Rubbi, Rishabh Jain, Pietro Liò

University of Cambridge Wellcome Sanger Institute

Single-Cell Perturbation Modeling Retrieval-Augmented Generation Adaptive Retrieval

Open paper AskPDF arXiv

Predicting transcriptional responses to genetic perturbations is fundamental to functional genomics and therapeutic discovery. Recent deep learning models have shown promise in single-cell perturbation response prediction, but they typically generate each response in isolation, without explicitly leveraging related perturbations. We introduce PT-RAG (Perturbation-aware Two-stage Retrieval-Augmented Generation), a plug-in retrieval-and-conditioning module for generative cellular perturbation response. PT-RAG augments an existing perturbation-response backbone with learned access to related perturbation contexts. The key challenge is that relevance is not fixed in this setting: functionally related genes may elicit different effects across cell types. PT-RAG addresses this with a two-stage retrieval mechanism: GenePT-based semantic retrieval first identifies K candidate perturbations, after which a differentiable Gumbel-Softmax selector adaptively selects retrieved contexts conditioned on the control cell state, the query perturbation, and each candidate perturbation. We evaluate PT-RAG on two backbones, a STATE-style generator used as a frozen random reservoir and a fully trained scGPT, across cross-cell-type and cross-perturbation generalization tasks. PT-RAG consistently improves distributional similarity and often overall predictive quality; for example, on scGPT cross-cell-type results, the 2-Wasserstein distance drops by 5.9% relative to scGPT alone. The code to reproduce our experiments is available at https://github.com/difra100/PT-RAG_NIPS.