RAG Robustness

RAG: Retrieval-Augmented Generation

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7 papers in the last four weeks, down 22% on the four weeks before. 0.1% 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
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
Detecting Is Not Resolving: The Monitoring Control Gap in Retrieval Augmented LLMs
May 26, 2026Zhe Yu, Wenpeng Xing, Chen Ye +4Language Model Safety Evaluation Knowledge Conflicts in Language Models
Binjiang Institute of Zhejiang University · Zhejiang University · Hangzhou Dianzi University +1
Generating Leakage-Free Benchmarks for Robust RAG Evaluation
May 9, 2026Jiayi Liu, Jiaxing Zhang, Bowen Jin +1Benchmark Contamination Synthetic Benchmark Generation
Department of Computer Science, Purdue University · New Jersey Institute of Technology · University of Illinois at Urbana-Champaign +1
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
Architecture Matters: Comparing RAG Systems under Knowledge Base Poisoning
May 7, 2026Samuel KornRetrieval-Augmented Generation RAG Poisoning Attacks
AdaGATE: Adaptive Gap-Aware Token-Efficient Evidence Assembly for Multi-Hop Retrieval-Augmented Generation
May 4, 2026Yilin Guo, Yinshan Wang, Yixuan WangMulti-Hop QA Retrieval-Augmented Generation
Center for Data Science New York University · Tandon School of Engineering New York University
Beyond Semantic Relevance: Counterfactual Risk Minimization for Robust Retrieval-Augmented Generation
May 2, 2026Peiyang Liu, Qiang Yan, Ziqiang Cui +3Retrieval-Augmented Generation LLM Hallucination Mitigation
National Engineering Research Center for Software Engineering, Peking University Beijing, China · PX Securities Shenzhen, China · City University of Hong Kong Hong Kong SAR, China +2
When Good OCR Is Not Enough: Benchmarking OCR Robustness for Retrieval-Augmented Generation
Apr 29, 2026Lin Sun, Wang Dexian, Jingang Huang +4RAG Evaluation Optical Character Recognition
Beijing Qiyuan Technology
RAGognizer: Hallucination-Aware Fine-Tuning via Detection Head Integration
Apr 17, 2026Fabian Ridder, Laurin Lessel, Malte SchillingRetrieval-Augmented Generation LLM Hallucination Mitigation
Computer Science Department, University of Münster, Münster, Germany
Improving Retrieval-Augmented Generation without Taxonomy-based Error Categorization
Apr 16, 2026Gongbo Zhang, Yifan Peng, Chunhua WengLLM Self-Correction Retrieval-Augmented Generation
Columbia University · Weill Cornell Medicine
A Survey of Secure Retrieval-Augmented Generation
Apr 9, 2026Yuming Xu, Mingtao Zhang, Zhuohan Ge +7RAG Security RAG Robustness
The Hong Kong Polytechnic University · The Hong Kong University of Science and Technology (Guangzhou)
Where Does Robustness Live? Neuron-Guided Adaptation for Retrieval-Augmented Language Models
Apr 2, 2026Jae O Lee, Jaemin Kim, Sumyeong Ahn +1Retrieval-Augmented Generation Fine-Tuning
1Hanyang University · 2KENTECH
Eyes-on-Me: Scalable RAG Poisoning through Transferable Attention-Steering Attractors
Oct 1, 2025Yen-Shan Chen, Sian-Yao Huang, Cheng-Lin Yang +1Language Model Steering RAG Poisoning Attacks
CyCraft AI Lab, Taiwan · National Taiwan University

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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