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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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.
What Makes a Medical Checker Trainable? Diagnosing Signal Collapse and Reward Hacking in Checker-Guided RAG for Biomedical QA
May 25, 2026Yuelyu Ji, Min Gu Kwak, Hang Zhang +3Reward Hacking Retrieval-Augmented Generation
University of Pittsburgh, Pittsburgh, PA, USA

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

A Systems-Level Analysis of Sensitivity, Robustness, and Stability in Retrieval-Augmented Generation

Bharath Simha Reddy Muthyam

Independent Researcher

Retrieval-Augmented Generation Retrieval Robustness RAG Evaluation RAG Robustness

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Retrieval-Augmented Generation (RAG) systems are often evaluated using final answer accuracy, even though their failures can originate from preprocessing, retrieval, context packing, or generation. This paper presents a controlled empirical study of RAG sensitivity, robustness, and stability across 56 experimental runs. We evaluate how chunk size, retrieval depth (top k), embedding-based reranking, probabilistic retrieval noise, and repeated seeded runs affect retrieval, context packing, and generation behavior. Using a fixed 500-question QA subset mapped to 20,958 unique corpus contexts, we analyze both final answer metrics and intermediate failure modes. Across these experiments, retrieval-oriented metrics improved under broader retrieval settings, while downstream exact-match and F1 scores often behaved non-monotonically. We also observe preprocessing-induced answer loss under smaller chunk sizes, progressive degradation under retrieval corruption, and higher observed variance in broader retrieval regimes. These findings suggest that RAG evaluation should include sensitivity, robustness, stability, and multi-stage failure analysis rather than relying only on final answer accuracy.

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Latest papers 503