Hallucination in RAG

RAG: Retrieval-Augmented Generation

Latest papers

35 papers8 papers in the last four weeks, up 167% on the four weeks before. 0.1% of all new papers.

AR-RRG: Anatomy-Guided Retrieval-Driven Radiology Report Generation
Nov 1, 2026Xiang Zhang, Shijie Song, Wanqing Zhao +8Radiology Report Generation Multimodal RAG
the School of Computer Science, Northwest University, Shaanxi, China · the School of Electronic Information, Northwest University, Shaanxi, China · the School of Art, Northwest University, Shaanxi, China
RAG-TRACE: Tracing Measurement Evidence and Diagnostic Completeness in AI-Generated ECG Reports
Nov 1, 2026Elyas Irankhah, Shahed Salehzehi, Parisa HosseiniMedical Report Generation RAG Evaluation
Department of Emergency Medicine, Yale University. · Department of Biomedical Engineering, Hakim Sabzevari University. · Neuroscience Research Center, Mashhad University of Medical Sciences.
ShiftRAG: Bypassing the Textual Bottleneck via Decoupled Learning and Continuous Soft Tokens
Oct 31, 2026Youmin Ko, Jihong Jeong, Hyunjoon KimRetrieval-Augmented Generation Retrieval-Augmented Reasoning
Department of Artificial Intelligence, Hanyang University · Department of Data Science, Hanyang University
CalibraRL: Confidence-Calibrated Reinforcement Learning for Mitigating Hallucinations in RAG
Oct 26, 2026Qianchi Zhang, Hainan Zhang, Liang Pang +2Hallucination in RAG RL for RAG
School of Artificial Intelligence, Beihang University, Beijing, China · Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China · Beijing Academy of Blockchain and Edge Computing, Beijing, China
SOPO: Socratic Guided Policy Optimization for Span-Level Hallucination Detection
Oct 23, 2026Jiawei Dong, Zilong Bai, Pengtian zhu +1Hallucination Detection LLM Hallucination Detection
Patsnap Pte. Ltd., Suzhou, China
Reasoning-Search-Augmented LLMs: An Operational Taxonomy, Benchmark Analysis and Controlled Evidence Synthesis
Oct 21, 2026Biswas Poudel, Nilson Chapagain, Amit Kumar +1Retrieval-Augmented Reasoning Language Model Reasoning Evaluation
Texas A&M University, College Station, TX, USA · Texas A&M University–Corpus Christi, Corpus Christi, TX, USA
When Does Uncertainty Measure RAG Hallucination Risk?
Oct 15, 2026Rajeel Ahmad Ansari, Tautik AgrahariHallucination in RAG LLM Hallucination Detection
Independent Researcher, Delhi, India · Independent Researcher, Bengaluru, India
When Citations Mislead? A Claim-Level Benchmark for Legal Hallucination Detection
Oct 7, 2026M. Mikail Demir, M. Abdullah CanbazHallucination in RAG Legal Citation Verification
University at Albany, SUNY Albany, New York, USA
Grounding Probes: Generator-Independent Hallucination Detection from Observer Model Hidden States
Oct 3, 2026Michael Rathmayr, Ádám Kovács, Gábor RecskiHallucination in RAG LLM Grounding
TU Wien, Austria · KR Labs
RAGScope: A Leakage-Controlled, Cost-Aware Evidence-Gating Protocol for RAG Hallucination Triage
Sep 30, 2026Zeming Liu, Qibai Chen, Jingtao Zhang +1Hallucination in RAG Selective Prediction
Brown University, United States · Independent Researcher, United States · Georgia Institute of Technology, United States
When Rubrics Fail: Hallucinations Reveal Blind Spots in Medical AI Evaluation
Sep 11, 2026Griffin Farrow, Lily Sijia Li, Jack Johnson +4Hallucination in RAG LLM Hallucination Detection
University of Oxford
Beyond Majority Vote: Multi-Perspective Adjudication for Medical Hallucination Detection
Sep 3, 2026Joe Cecil, Marjorie FreedmanHallucination in RAG LLM-as-a-Judge
Information Sciences Institute, University of Southern California
Detecting and Repairing Hallucinations in Retrieval-Augmented Generation
Aug 29, 2026Sai Krishna Reddy Mulakkayala, Niki van Stein, Aske PlaatHallucination in RAG LLM Hallucination Mitigation
LIACS, Leiden University, Leiden, The Netherlands
Why RAGs Hallucinate: Penalty-Aware Evaluation of Retrieval-Augmented Generation Systems with Knowledge-Gap Canaries
Aug 26, 2026Alden Do Rosario, Hussein Younes, Felipe PiresHallucination in RAG Retrieval-Augmented Generation
Lost in Speech: Trilingual Spoken Hallucination Detection Across Audio and Transcripts
Aug 25, 2026Meruyert Aristombayeva, Jason S. Lucas, Chaewan Chun +1Audio-Language Model Evaluation Hallucination in RAG
Satbayev University, Almaty, Kazakhstan · University of Colorado Boulder, Boulder, CO, USA · The Pennsylvania State University, University Park, PA, USA
Do Large Language Models Play Six Degrees of Separation? Measuring Topological Compression in Long-Context Manifolds
Aug 18, 2026Md. Faiyaz Abdullah SayeediLong-Context Reasoning in Language Models Hallucination in RAG
BRAC University, Bangladesh
SIRIN: A Unified Toolkit for Detecting Contextual Hallucinations in Retrieval-Augmented and Memory-Grounded LLM Systems
Jul 20, 2026Julia Belikova, Rauf Parchiev, Mikhail Filimonov +3Retrieval-Augmented Generation LLM Answer Verification
Sber AI Lab
To Answer or to Abstain: Mitigating Search-Agent Hallucinations via Abstention-Aware Reinforcement Learning
Jul 12, 2026Fengji Zhang, Tianyu Fan, Yuxiang Zheng +4Question Answering RL for LLM Agents
City University of Hong Kong · Alibaba Group
Detecting Hallucinations in Retrieval-Augmented Generation through Grounding-Aware Sensitivity by Perturbation (GASP)
Jul 5, 2026Mohamed Aly BoukeRetrieval-Augmented Generation LLM Grounding
Centre for Intelligent Cloud Computing, CoE for Advanced Cloud, Faculty of Information Science and Technology, Multimedia University, Jalan Ayer Keroh Lama, Bukit Beruang, 75450, Melaka, Malaysia
Beyond Document Grounding: Span-Level Hallucination Detection over Code, Tool Output, and Documents
Jul 1, 2026Ádám Kovács, Bowei He, Xue Liu +3LLM Grounding Hallucination Detection
KR Labs · MBZUAI · McGill University +1
CORTEX: Token-Level Hallucination Detection in RAG via Comparative Internal Representations
Jun 30, 2026Kazuaki Furumai, Shuichiro Haruta, Kazunori Matsumoto +1Retrieval-Augmented Generation LLM Hallucination Detection
KDDI Research, Inc.
Evidence Graph Consistency in Retrieval-Augmented Generation: A Model-Dependent Analysis of Hallucination Detection
Jun 4, 2026Jianru ShenRetrieval-Augmented Generation Hallucination in Language Models
University of Montana Missoula, MT, USA
Reducing Hallucinations in Complex Question Answering using Simple Graph-based Retrieval-Augmented Generation (long version)
Jun 4, 2026Christopher J. Wedge, Joshua Stutter, Danny Dixon +1Multi-Hop QA Tool-Augmented Language Model Agents
National Innovation Centre for Data, Newcastle University
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 RAG
KAIST AI · Financial Tech Lab, KakaoBank Corp.
From Clouds to Hallucinations: Atmospheric Retrieval Hijacking in Remote Sensing Vision-Language RAG
May 8, 2026Jiaju Han, Chao Li, Chengyin Hu +8Multimodal RAG Retrieval-Augmented Generation
China University of Petroleum, Beijing at Karamay, Karamay, Xinjiang 834000, China
CuraView: A Multi-Agent Framework for Medical Hallucination Detection with GraphRAG-Enhanced Knowledge Verification
May 5, 2026Severin Ye, Xiao Kong, Xiaopeng He +2Hallucination in RAG Healthcare
School of Computer Science and Engineering Kyungpook National University Daegu, Republic of Korea · School of Airspace Science and Engineering Shandong University Weihai, China · Department of English Language and Literature, Kyungpook National University, Daegu, Republic of Korea
RAGognizer: Hallucination-Aware Fine-Tuning via Detection Head Integration
Apr 17, 2026Fabian Ridder, Laurin Lessel, Malte SchillingHallucination in Language Models RAG Robustness
Computer Science Department, University of Münster, Münster, Germany
Facet-Level Tracing of Evidence Uncertainty and Hallucination in RAG
Apr 10, 2026Passant Elchafei, Monorama Swain, Shahed Masoudian +1Hallucination in RAG RAG Evaluation
Johannes Kepler University Linz, Institute of Computational Perception, Linz, Austria · Linz Institute of Technology, Artificial Intelligence Lab, Austria
Guaranteeing Knowledge Integration with Joint Decoding for Retrieval-Augmented Generation
Apr 9, 2026Zhengyi Zhao, Shubo Zhang, Zezhong Wang +7Hallucination in RAG Retrieval-Augmented Generation
The Chinese University of Hong Kong · Ministry of Education Key Laboratory of High Confidence Software Technologies, CUHK · University of International Relations +2
Retromorphic Testing with Hierarchical Verification for Hallucination Detection in RAG
Mar 29, 2026Boxi Yu, Yuzhong Zhang, Liting Lin +2Hallucination in RAG LLM Hallucination Detection
Lero, the Research Ireland Centre for Software, University of Limerick, Ireland · The Chinese University of Hong Kong, Shenzhen, China · University of Ottawa, Canada +1

Nov 1, 2026J/K move · Enter open · S save

AR-RRG: Anatomy-Guided Retrieval-Driven Radiology Report Generation

Xiang Zhang, Shijie Song, Wanqing Zhao, Wei Zhou+7

the School of Computer Science, Northwest University, Shaanxi, China · the School of Electronic Information, Northwest University, Shaanxi, China · the School of Art, Northwest University, Shaanxi, China

Radiology Report Generation Multimodal RAG Hallucination in RAG

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Radiology report generation (RRG) streamlines clinical workflows and alleviates the burden of radiologists' documentation. Standard vision-language models capture global visual cues and lack fine-grained alignment between anatomical regions and diagnostic text. Most retrieval-augmented methods retrieve complete historical reports, introducing redundant noise and severe vision-text mismatches that trigger clinically risky hallucinations and missed lesions. In this paper, we introduce AR-RRG, an anatomy-guided retrieval-driven method for the generation of radiology reports. AR-RRG consists of two core modules: the anatomy-aware self-training lesion detection network (ASLDNet) and the retrieval-driven radiology report generation (RRRG). ASLDNet leverages anatomy-aware spatial verification (ASV) and class-adaptive dynamic thresholding (CDT) via self-training, delivering accurate lesion localization to support subsequent generation. As the generative branch of AR-RRG, RRRG eliminates clinical hallucinations through dual-granularity retrieval (DR) and complements missing lesions with a neighborhood consensus (NC) mechanism. A parallel continuous visual perception (PCVP) stream converts radiographs into visual tokens through fixed medical encoding and resampling. Multi-scale text and visual features are fused, followed by LoRA-efficient large language model tuning to generate clinically consistent, visually grounded radiology reports. Extensive evaluations on MS-CXR, MIMIC-CXR, and IU X-Ray demonstrate that AR-RRG consistently outperforms state-of-the-art methods across mainstream metrics. Our code will be made publicly available.