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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MEGRAG: Multi-Granular Evidence Graphs for Answer-Aware Multi-Hop RAG
Aug 3, 2026Weidong Bao, Yingying Sun, Jun Yang +7Multi-Hop QA Retrieval-Augmented Generation
Northeastern University, Shenyang, China
Before Reasoning Can Fail: Pre-Evidence Procedural Failures in Agentic RAG
Aug 3, 2026Daeyoung Roh, Donghee HanRetrieval-Augmented Generation Agentic RAG
Independent Researcher · KAIST
HALT: Verification-Aware Stopping for Retrieval-Augmented Search Agents
Aug 3, 2026Daeyoung Roh, Donghee HanMulti-Hop QA Retrieval-Augmented Generation
Independent Researcher · KAIST
Effective and Efficient Context Retrieval via Partial Dependency Graph for Repository-Level Code Generation
Aug 3, 2026Zhongxin Liu, Zhonghao Jiang, Zhifan Ye +3Retrieval-Augmented Generation Graph-Based Retrieval
College of Computer Science and Technology and the State Key Laboratory of Blockchain and Data Security, Zhejiang University · School of Computer and Computing Science, Hangzhou City University · Faculty of Computing, Harbin Institute of Technology +1
MITRE-SAGE: A Multi-Agent Cybersecurity Question-Answering Model
Aug 3, 2026Ali Habibzadeh, Farid Feyzi, Reza Ebrahimi AtaniRetrieval-Augmented Generation Multi-Agent LLM Systems
Department of Computer Engineering University of Guilan Rasht, Iran
RING: Retrieval-Internalized Generation for Continual Large-Scale Knowledge Injection
Aug 3, 2026Shicheng Xu, Liang Pang, Liyi Chen +7Retrieval-Augmented Generation Continual Learning for LLMs
State Key Laboratory of AI Safety, Institute of Computing Technology, CAS · University of Chinese Academy of Sciences · Xiaohongshu Inc.
EDATracer: An Agentic Framework for Large-Scale EDA Artifact Analysis
Aug 2, 2026Phat Tieu, Sayanti Jana, Matthew DeLorenzo +5Retrieval-Augmented Generation Electronic Design Automation
Texas A&M University, Department of Electrical and Computer Engineering College Station, Texas 77840, USA
Retrieval Augmented Biomedical Question Answering with Weak Question Recovery and Neural Reranking for BioASQ Task 14b
Aug 2, 2026Xueying Zhao, Lee Mai, Balaji AnandganeshRetrieval-Augmented Generation Learning to Rank
Georgia Institute of Technology, North Ave NW, Atlanta, GA 30332
RH-RAG: Trustworthy Long-Form Generation for Privacy-Constrained Settings
Aug 2, 2026Raj Shekhar SinghRetrieval-Augmented Generation Long-Form Document Generation
Indian Institute of Technology, Roorkee Roorkee, Uttarakhand, India
GeoArbiter: Verifiability-Guided Grounding for Remote-Sensing Multimodal LLMs
Aug 1, 2026Xuechen LiRemote Sensing Image Understanding Retrieval-Augmented Generation
University of Minnesota, Twin Cities
Large language models improve physician accuracy but lead to false reliance
Aug 1, 2026Tirtha Chanda, Christoph Wies, Franziska Schramm +10Retrieval-Augmented Generation LLM Grounding
Division of Digital Prevention, Diagnostics and Therapy Guidance, German Cancer Research Center (DKFZ), Heidelberg, Germany · Medical Faculty, University Heidelberg, Heidelberg, Germany · Skin Cancer Unit, German Cancer Research Center (DKFZ), Heidelberg, Germany +6
RAGOCR: Optical Compression of Retrieval-Augmented Text via Visual Representation
Aug 1, 2026Jiayang Yu, Jialun Zhong, Lei ZouMultimodal RAG Retrieval-Augmented Generation
Wangxuan Institute of Computer Technology, Peking University Beijing, China
Select-And-Extract: A Lightweight Plugin for Retrieval-Augmented Generation
Aug 1, 2026Chenming Tang, Jiawei HanRetrieval-Augmented Generation LLM Reranking
Peking University · University of Illinois Urbana-Champaign
Unleashing the Potential of Large Language Models: A Blueprint for Real-Time, Enterprise-Ready Deployments
Aug 1, 2026Muhammad Faizan Raza, Shuo, Yang +2Retrieval-Augmented Generation Continual Learning for LLMs
Luna · ¹ Penn State Great Valley, Malvern, PA 19355, USA · ² Penn State Brandywine, Media, PA 19063, USA +1
Bridging the Question-Answer Gap in Retrieval-Augmented Generation: Hypothetical Prompt Embeddings
Jul 31, 2026Domen Vake, Jernej Vičič, Aleksandar TošićRetrieval-Augmented Generation Dense Retrieval
University of Primorska, Faculty of Mathematics, Natural Sciences and Information Technologies · InnoRenew CoE · Research Centre of the Slovenian Academy of Sciences and Arts, The Fran Ramovš Institute
GoldenRetriever: Non-Interactive Homomorphic Encrypted Retrieval for Privacy-Preserving RAG
Jul 31, 2026Yang Gao, Gang Quan, Scott Piersall +3Retrieval-Augmented Generation Homomorphic Encryption
Department of Computer Science, University of Central Florida, Orlando, FL, USA · Electrical and Computer Engineering Department, Florida International University, Miami, FL, USA · College of Design, Construction and Planning, University of Florida, Gainesville, FL, USA
Hierarchical Reranking for Scalable Financial RAG System
Jul 29, 2026Joohyun Lee, Sungwoo HongFinancial Services Retrieval-Augmented Generation
1Financial Security Institute · 2Hanyang University
Models for minimalist RAG: B1ade 335M Embedding and 1B Parameter Small Language Models
Jul 29, 2026Shreyas Subramanian, Mecit Gungor, Vikram ElangoRetrieval-Augmented Generation LLM Grounding
Amazon, Seattle, USA
LayerRAG-Bench: A Cross-Layer Reliability Benchmark for Agentic Retrieval-Augmented Generation
Jul 29, 2026Musa ShamsAI Agent Reliability Retrieval-Augmented Generation
Independent Researcher
BM25 Wins at Scale: A Scaling Study of Retrieval-Augmented Generation Paradigms
Jul 29, 2026Pengyu Wang, Benfeng Xu, Shaohan Wang +5Retrieval-Augmented Generation Graph Retrieval-Augmented Generation
University of Science and Technology of China, Hefei, China · Metastone Technology, Beijing, China · Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
CMT-RAG: Complementary Memory Traces for Multi-turn Multi-hop RAG
Jul 29, 2026Lang Zhou, Yingjian Chen, Shuxuan Li +2Conversational Memory Retrieval-Augmented Generation
Sun Yat-sen University · Shenzhen Loop Area Institude · The University of Hong Kong
Knowledge before Reasoning: EC-Reason-Bench, a Training-Free Diagnostic Benchmark for LLM Enzyme Classification
Jul 29, 2026Linyu Li, Zhi Jin, Yichi Zhang +6Retrieval-Augmented Generation LLM Evaluation
School of Computer Science, Peking University, Beijing, China · College of Computer Science and Technology, Zhejiang University, Hangzhou, China · School of Computer Science, Tibet University, Lhasa, China
Beyond Self-Knowledge: Propagating Uncertainty Across Reasoning and Retrieval in LLMs
Jul 28, 2026Chandan Kumar Sah, Li Zhang, Xiaoli LianRetrieval-Augmented Generation Confidence Estimation in Language Models
Beihang University, Beijing, China
ScalableRAG: High-Quality RAG at Zero Ingestion Cost
Jul 27, 2026Hilaf Hasson, Aditya Chakravarty, Jayant Thomas +1Retrieval-Augmented Generation Question Answering
1Cohesity
Grounded in Consensus, In Step With Emerging Science: A Consensus-Anchored Multi-Corpus Clinical Chatbot for Long COVID
Jul 27, 2026Yining Wu, Philip DiGiacomo, Ying Ding +1Retrieval-Augmented Generation Clinical Decision Support
School of Information University of Texas at Austin Austin, TX, USA · Department of Computer Science University of Texas at Austin Austin, TX, USA · School of Information, Dell Medical School University of Texas at Austin Austin, TX, USA +1
Retrieval-Augmented Large Language Models as Components of Cognitive Computing architecture for Regulatory Knowledge Management
Jul 27, 2026Dariusz Nowak-NovaRetrieval-Augmented Generation Knowledge Augmentation for Language Models
1WSB University, Poland
Cross-Attention Calibrated Deduplication for Retrieval-Augmented Generation System
Jul 27, 2026Phuong Le Huy, Nam H. Nguyen, Quan V. DangRetrieval-Augmented Generation RAG Evaluation
Full Stack Data Science · Department of Computer Science, University College London
Beyond "What to Retrieve": Uncertainty in Retrieval-Augmented Code Generation
Jul 27, 2026Chandan Kumar Sah, Li Zhang, Xiaoli LianRetrieval-Augmented Generation LLM Uncertainty Estimation
Beihang University, Beijing, China
CAGE: Cognitive Attribution Graphs for Faithful Inline Citation Generation in Long-Form Question Answering
Jul 27, 2026Zhichao Yan, Shizhao Li, Jiapu Wang +5Retrieval-Augmented Generation Question Answering
Shanxi University, Taiyuan, China · Nanjing University of Science and Technology, Nanjing, China · Nanyang Technological University, Singapore +3
Agent-UCT: Upper Confidence Bounds Applied to Trees for Agentic Workflow Optimization with Cost-Awareness
Jul 27, 2026Yang Li, Hai Liu, Dian Shao +8Retrieval-Augmented Generation Cost-Aware Inference
1The University of Hong Kong · School of Artificial Intelligence, Jiangxi Science and Technology Normal University · 3The Hong Kong University of Science and Technology +2

Aug 3, 2026, cs.AIJ/K move · Enter open · S save

MEGRAG: Multi-Granular Evidence Graphs for Answer-Aware Multi-Hop RAG

Weidong Bao, Yingying Sun, Jun Yang, Yilin Wang+6

Northeastern University, Shenyang, China

Multi-Hop QA Retrieval-Augmented Generation Graph Retrieval-Augmented Generation Adaptive RAG

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Multi-hop question answering is a fundamental challenge in retrieval-augmented generation (RAG), because deriving an answer requires integrating dispersed evidence. Iterative RAG (iRAG) is widely used for this challenge, but existing methods have two limitations. First, most methods still support each reasoning step with single-granularity evidence, making it difficult to balance information density and contextual noise. Second, existing methods often answer the original question only after aggregating evidence retrieved across intermediate steps, so redundant evidence and intermediate retrieval errors may accumulate and degrade the final answer. To address these limitations, we propose MEGRAG, an answer-aware framework that represents multi-hop reasoning as a path-structured multi-granular evidence graph. Offline, MEGRAG links passages to their sentences and extracted triples through a cross-granularity index. Online, it retrieves passages for the current query and selects aligned evidence, starting with compact triples and adding sentence or passage context as needed. MEGRAG uses the resulting intermediate answer and prior reasoning to decide whether the Initial Query has been resolved. If not, it identifies the missing information and formulates a focused next query; otherwise, it stops retrieval and returns the answer. Extensive experiments demonstrate consistent gains over a diverse set of RAG baselines.

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Latest papers 503