Retrieval-Augmented Generation

Also known as RAG

Follow this topic Ask about this topic

Momentum

54 papers in the last four weeks, up 10% on the four weeks before. 0.6% of all new papers.

Jul 6Week of Sep 21

Latest papers 503

All topics

Grounded Decoding: Retrieval-Anchored Probability Fusion for Faithful RAG
May 29, 2026Ibne Farabi Shihab, Fariya Afrin, Sanjeda Akter +1Retrieval-Augmented Generation Language Model Decoding
Department of Computer Science, Iowa State University · Department of Computer Science, Kalinga Institute of Industrial Technology · Department of Civil, Construction & Environmental Engineering, Iowa State University
A Systems-Level Analysis of Sensitivity, Robustness, and Stability in Retrieval-Augmented Generation
May 29, 2026Bharath Simha Reddy MuthyamRetrieval-Augmented Generation Retrieval Robustness
Independent Researcher
Point-in-Time Financial RAG with Frozen LLMs and Market-Feedback Adaptive Retrieval
May 29, 2026Zijie Zhao, Roy E. WelschRetrieval-Augmented Generation Quantitative Finance
Massachusetts Institute of Technology · Cambridge, MA, USA
Retriever Portfolios: A Principled Approach to Adaptive RAG
May 29, 2026Miltiadis Stouras, Vincent Cohen-Addad, Silvio Lattanzi +1Retrieval-Augmented Generation Adaptive RAG
1EPFL · 2Google Research
Fighting Numerical Hallucinations via Data-centric Compilation for Online Financial QA
May 29, 2026Hao Chen, Xing Tang, Qirui Liu +6Retrieval-Augmented Generation Hallucination in Language Models
Shenzhen Technology University FiT, Tencent Shenzhen, China · South China University of Technology Guangzhou, China · The Hong Kong University of Science and Technology Hong Kong SAR, China +2
MoG: Mixture of Experts for Graph-based Retrieval-Augmented Generation
May 29, 2026Zheng Yuan, Chuang Zhou, Linhao Luo +4Retrieval-Augmented Generation Graph Retrieval-Augmented Generation
The Hong Kong Polytechnic University · Monash University · Tencent Youtu Lab
On the impact of retrieved content representations in RAG Pipelines
May 29, 2026Jonathan J Ross, Bevan Koopman, Anton van der Vegt +1Retrieval-Augmented Generation RAG Evaluation
The University of Queensland · CSIRO / The University of Queensland
CanLegalRAGBench: Evaluating Retrieval-Augmented Generation on Canadian Case Law
May 28, 2026Ethan Zhao, Maksym Taranukhin, Wei Cui +2Retrieval-Augmented Generation Legal QA
Department of Computer Science, University of British Columbia · Vector Institute · Peter A. Allard School of Law, University of British Columbia +1
CRITIC-R1: Learning Structured Critics for Retrieval-Augmented Generation
May 28, 2026Wenhan Xiao, Ziwei Zhang, Chuanyue Yu +4LLM Self-Correction Retrieval-Augmented Generation
Nankai University · Beihang University · Guangxi Normal University
CogniVerse: Revolutionizing Multi-Modal Retrieval-Augmented Generation with Cognitive Reflection and Geometric Reasoning
May 28, 2026Xiang Fang, Wanlong Fang, Changshuo WangMultimodal RAG Retrieval-Augmented Generation
School of Software Engineering, Huazhong University of Science and Technology · Nanyang Technological University, Singapore · University College London
K-FinHallu: A Hallucination Detection Benchmark for Multi-Turn RAG in Korean Finance
May 28, 2026Eunbyeol Cho, Yunseung Lee, Mirae Kim +3Retrieval-Augmented Generation Hallucination in Language Models
KAIST AI · Financial Tech Lab, KakaoBank Corp.
Anytime-Valid Federated Conformal RAG for LLM Swarms
May 27, 2026Prasanjit Dubey, Xiaoming HuoRetrieval-Augmented Generation Anytime-Valid Inference
H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA 30332, U.S.A.
Same Question, Different Source, Different Answer: Auditing Source-Dependence in Medical Multi-Source RAG
May 27, 2026Yubo Li, Rema Padman, Ramayya KrishnanRetrieval-Augmented Generation Healthcare
Carnegie Mellon University
Analyzing Quality-Latency-Resource Trade-offs in a Technical Documentation RAG Assistant Using LoRA Adaptation
May 27, 2026Evgenii Palnikov, Elizaveta GavrilovaLLM Inference Efficiency Retrieval-Augmented Generation
1HSE University
A Wolf in Sheep's Clothing: Targeted Routing Hijacking in Federated RAG
May 27, 2026Junjie Mu, Qiongxiu LiRetrieval-Augmented Generation Cybersecurity
Politecnico di Milano Milan, Italy · Aalborg University Aalborg, Denmark
ConRAG: Consensus-Driven Multi-View Retrieval for Multi-Hop Question Answering
May 27, 2026Yikai Zhu, Kunfeng Chen, Qihuang Zhong +2Multi-Hop QA Retrieval-Augmented Generation
School of Computer Science, Wuhan University Wuhan, China
SilentRetrieval: Hijacking Retrieval-Augmented Generation via Semantically-Preserving Adversarial Data Poisoning
May 27, 2026Jiachen QianRetrieval-Augmented Generation RAG Poisoning Attacks
City University of Hong Kong · Hong Kong, Hong Kong
DiagramRAG: A Lightweight Framework to Retrieve Scientific Diagram for Figure Generation
May 27, 2026Xinjiang Yu, Junyi Han, Zhuofan Chen +7Retrieval-Augmented Generation Graph-Based Retrieval
Beijing Institute of Technology
C-MIG: Multi-view Information Gain-based Retrieval-Augmented Generation for Clinical Diagnosis Reasoning
May 27, 2026Yuwei Miao, Gen Li, Yunsheng Zeng +8Retrieval-Augmented Generation Healthcare
1Baidu Inc.
A Fixed-Budget, Cluster-Aware Standard for LLM-as-a-Judge Evaluation: A Multi-Hop RAG Stress Test
May 27, 2026Camilo Chacón Sartori, José H. GarcíaRetrieval-Augmented Generation LLM-as-a-Judge
Catalan Institute of Nanoscience and Nanotechnology (ICN2), CSIC and BIST, Campus UAB, Bellaterra, Barcelona, Spain
Simorgh at SemEval-2026 task 7: Region-Aware Hybrid Retrieval for Low-Resource Cultural Reasoning in Multilingual Question Answering
May 26, 2026Hadi Bayrami Asl Tekanlou, Mahdi Bakhtiyarzadeh, Jafar RazmaraRetrieval-Augmented Generation Cross-Lingual Reasoning in Language Models
University of Tabriz, Tabriz, Iran
Natural Language Query to Configuration for Retrieval Agents
May 26, 2026Melissa Z. Pan, Negar Arabzadeh, Mathew Jacob +3Retrieval-Augmented Generation Cost-Aware Inference
UC Berkeley · University of Washington · Microsoft Azure Research - Systems
Separating Semantic Competition from Context Length in RAG Reading
May 26, 2026Vyzantinos Repantis, Ameya Gawde, Harshvardhan Singh +4Long-Context Retrieval Retrieval-Augmented Generation
Meta Platforms
Grounded Cache Routing for Retrieval-Augmented Generation: When Is It Safe to Reuse an Answer?
May 26, 2026Syed Huma ShahRetrieval-Augmented Generation RAG Security
Duke University · Syed Huma Shah$^*$
The Coverage Illusion: From Pre-retrieval Routing Failure to Post-retrieval Cascades in a Production RAG System
May 26, 2026Zafar Hussain, Kristoffer NielboLLM Inference Efficiency Retrieval-Augmented Generation
Aarhus University, Denmark
LitSeg: Narrative-Aware Document Segmentation for Literary RAG
May 26, 2026Ruikang Zhang, Zhanni Chen, Yiqiao Cai +1Document Chunking Retrieval-Augmented Generation
Peking University, Beijing, China
REVERSE: Reinforcing Evidence Verification and Search for Agentic Image geo-localization
May 26, 2026Yong Li, Furong Jia, Dacheng Yin +4Retrieval-Augmented Generation Reinforcement Learning
Peking University · The Hong Kong University of Science and Technology · WeChat Vision, Tencent Inc
FAB-Bench: A Framework for Adaptive RAG Benchmarking in Semiconductor Manufacturing
May 26, 2026Jingbin Qian, Congwen Yi, Min Xia +3Retrieval-Augmented Generation Benchmark Design
FutureFab.AI
In-Context Optimization for Retrieval-Augmented Generation: A Gradient-Descent Perspective
May 25, 2026Mingchen Li, Jiatan Huang, Chuxu Zhang +2Retrieval-Augmented Generation Test-Time Adaptation
University of Massachusetts, Amherst · University of Connecticut · Emory University
Retrieval-Augmented Detection of Potentially Abusive Clauses in Chilean Terms of Service
May 25, 2026Christoffer Loeffler, Tomás Rey Pizarro, Daniel Ignacio Miranda Vásquez +1Retrieval-Augmented Generation Legal NLP
School of Computer Engineering, Pontificia Universidad Católica de Valparaíso, Brasil 2950, Valparaíso, 2340025, Chile. · Faculty of Law, Universidad Adolfo Ibáñez, Av. Padre Hurtado 750,2026 Viña del Mar, 2581793, Chile.

May 29, 2026, cs.LGJ/K move · Enter open · S save

Grounded Decoding: Retrieval-Anchored Probability Fusion for Faithful RAG

Ibne Farabi Shihab, Fariya Afrin, Sanjeda Akter, Anuj Sharma

Department of Computer Science, Iowa State University · Department of Computer Science, Kalinga Institute of Industrial Technology · Department of Civil, Construction & Environmental Engineering, Iowa State University

Retrieval-Augmented Generation Language Model Decoding Knowledge Conflicts in Language Models Evidence-Grounded Generation

Open paper AskPDF arXiv

As retrieval-augmented generation (RAG) systems scale, it becomes increasingly challenging to ensure faithful grounding in external evidence. Large language models may still prioritize parametric knowledge over retrieved information when conflicts arise. We propose a novel training-free decoding framework, \emph{Grounded Decoding}, designed to improve factual consistency in RAG without modifying model parameters. Unlike standard approaches that rely on a single conditional distribution, our method constructs two matched-prompt distributions at every generation step: (1) a full RAG distribution conditioned on the query, retrieved documents, and generated prefix, and (2) a retrieval-only distribution conditioned solely on retrieved evidence and the same prefix. The final next-token distribution is derived as the unique solution to a KL-barycenter objective over the probability simplex, yielding a normalized geometric fusion of the two distributions.This formulation naturally recovers standard RAG when the grounding weight is zero and smoothly shifts probability mass toward retrieved evidence as grounding strength increases. We further introduce a conflict-aware adaptive weighting scheme that dynamically adjusts grounding based on distributional disagreement and retriever confidence. Experiments on ALCE, Natural Questions, and FActScore demonstrate consistent improvements in factual accuracy and citation quality over standard RAG and competitive decoding-time baselines, while maintaining fluency. Our results indicate that probability-level fusion provides a strong and efficient alternative to logit-level intervention methods for faithful RAG decoding.

Momentum

Related topics

Latest papers 503