LLM Reliability

LLM: Large Language Model

It asks not only whether a model gets an answer right, but whether it knows when it may be wrong, follows constraints, uses evidence faithfully, and behaves consistently across repeated or multi-turn use. Researchers test these properties with adversarial and perturbation benchmarks, repeated-run and cross-language evaluations, domain-specific tasks, and audits of LLM judges and tool-using agents. Common approaches include estimating uncertainty from logits, hidden representations, or response agreement; selective abstention and conformal calibration; retrieval and symbolic verification; and training methods that reward grounded answers, correction, or hesitation. The central tension is that benchmark accuracy and verbal confidence often fail to predict reliability on individual cases, long tasks, or shifted inputs, while stronger checks and abstention trade coverage, latency, and cost for safer decisions.

Latest papers

802 papers116 papers in the last four weeks, up 18% on the four weeks before. 0.7% of all new papers.

The Oracle's Fingerprint: Correlated AI Forecasting Errors and the Limits of Bias Transmission
Apr 7, 2026Theodor SpiroCorrelated Errors in Language Models LLM Reliability
A Multi-Stage Validation Framework for Trustworthy Large-scale Clinical Information Extraction using Large Language Models
Apr 7, 2026Maria Mahbub, Gregory M. Dams, Josh Arnold +9Clinical Information Extraction Clinical Language Model Evaluation
Oak Ridge National Laboratory, Oak Ridge, TN, USA · Program Evaluation and Resource Center, Office of Mental Health and Office of Suicide Prevention, Department of Veterans Affairs, Menlo Park, CA, USA · Vanderbilt University Medical Center, Nashville, TN, USA +4
The Model Agreed, But Didn't Learn: Diagnosing Surface Compliance in Large Language Models
Apr 7, 2026Xiaojie Gu, Ziying Huang, Weicong Hong +3Knowledge Editing LLM Reliability
Independent Researcher · Cornell Tech · The Ohio State University +1
Swiss-Bench 003: Evaluating LLM Reliability and Adversarial Security for Swiss Regulatory Contexts
Apr 7, 2026Fatih UenalLLM Evaluation LLM Reliability
Department of Computer Science, University of Colorado Boulder
Identifying Influential N-grams in Confidence Calibration via Regression Analysis
Apr 7, 2026Shintaro Ozaki, Wataru Hashimoto, Hidetaka Kamigaito +2Language Model Calibration Confidence Estimation in Language Models
Nara Institute of Science and Technology (NAIST) · The University of Tokyo
LLM Psychosis: A Theoretical and Diagnostic Framework for Reality-Boundary Failures in Large Language Models
Apr 7, 2026Ashutosh RajLLM Reliability Language Model Safety Evaluation
Founder & CEO, NeuraCare AI | IIT Ropar TBIF Incubatee, India
Confidence Should Be Calibrated More Than One Turn Deep
Apr 7, 2026Zhaohan Zhang, Chengzhengxu Li, Xiaoming Liu +3Confidence Estimation in Language Models Language Model Calibration
Queen Mary University of London · Xi’an Jiaotong University
LLM-as-Judge for Semantic Judging of Powerline Segmentation in UAV Inspection
Apr 7, 2026Akram Hossain, Rabab Abdelfattah, Xiaofeng Wang +1LLM Reliability Vision-Language Model-as-a-Judge
School of Computing Sciences and Computer Engineering, The University of Southern Mississippi, Hattiesburg, MS 39406 USA. · Electrical Engineering Department, University of South Carolina, Columbia, SC, USA. · Computer Science Department, Faculty of Computers & Artificial Intelligence, Fayoum University, Fayoum, Egypt.
Justified or Just Convincing? Error Verifiability as a Dimension of LLM Quality
Apr 6, 2026Xiaoyuan Zhu, Kimberly Le Truong, Riccardo Fogliato +8LLM Evaluation LLM Answer Verification
University of Southern California · Carnegie Mellon University · Microsoft Core AI +1
Benchmarking Multi-turn Medical Diagnosis: Hold, Lure, and Self-Correction
Apr 6, 2026Jinrui Fang, Runhan Chen, Xu Yang +9Clinical Language Model Evaluation Medical Diagnosis
The University of Texas at Austin · New York University · The University of Texas Southwestern Medical Center +2
NeuroSymbolic AI for Legal AI-TRISM: Trustworthy, Reliable, Interpretable, Safe Models
Apr 5, 2026Deepa Tilwani, Yash Saxena, Ankur Padia +2LLM Reliability Neuro-Symbolic AI
Department of Computer Science, AI Institute, University of South Carolina, Columbia, South Carolina, USA · Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County, Baltimore, Maryland, USA · Department of Computer Science and Engineering, The Ohio State University, Columbus, Ohio, USA
A Reproducible Semantic Benchmark for Multivendor DSM-to-CLI Translation
Apr 5, 2026Jerônimo Menezes, Leonardo Bitzki, Diego Kreutz +3LLM Evaluation LLM Reliability
Federal University of Rio Grande do Sul (UFRGS) · AI Horizon Labs and PPGES – Federal University of Pampa (UNIPAMPA)
Structural Rigidity and the 57-Token Predictive Window: A Physical Framework for Inference-Layer Governability in Large Language Models
Apr 4, 2026Gregory M. RuddellLanguage Model Safety Evaluation Inference-Time Language Model Alignment
SnailSafe.ai — Carson City, Nevada
Measuring LLM Trust Allocation Across Conflicting Software Artifacts
Apr 3, 2026Noshin Ulfat, Ahsanul Ameen Sabit, Soneya Binta HossainLLM Reliability Software Engineering
University of Texas at Dallas Texas, USA · University of Texas at Dallas Richardson, Texas, USA
BAS: A Decision-Theoretic Approach to Evaluating Large Language Model Confidence
Apr 3, 2026Sean Wu, Fredrik K. Gustafsson, Edward Phillips +3LLM Evaluation Confidence Estimation in Language Models
Department of Engineering Science, University of Oxford · Oxford Suzhou Centre for Advanced Research
BibTeX Citation Errors in Scientific Publishing Agents: Evaluation and Mitigation
Apr 3, 2026Delip Rao, Chris Callison-BurchCitation Verification LLM Reliability
University of Pennsylvania
Measuring What Cannot Be Surveyed: LLMs as Instruments for Latent Cognitive Variables in Labor Economics
Apr 2, 2026Cristian Espinal MayaAI and Labor LLM Reliability
Department of Economics, Universidad EAFIT, Medellín, Colombia.
Bayesian Elicitation with LLMs: Model Size Helps, Extra "Reasoning" Doesn't Always
Apr 2, 2026Luka Hobor, Mario Brcic, Mihael Kovac +1Probability Elicitation from Language Models LLM Uncertainty Estimation
University of Zagreb Faculty of Electrical Engineering and Computing, Zagreb, Croatia
Hidden Reliability Risks in Large Language Models: Systematic Identification of Precision-Induced Output Disagreements
Apr 2, 2026Yifei Wang, Tianlin Li, Xiaohan Zhang +4LLM Reliability LLM Evaluation
Shanghai Jiao Tong University, China · Beihang University, China · Nanyang Technological University, Singapore +1
What Is Actually Being Annotated? Inter-Prompt Reliability as a Measurement Problem in LLM-Based Social Science Labeling
Apr 2, 2026Jingyuan LiuLLM-Assisted Annotation LLM Reliability
Boston University
Generative AI-Based Virtual Assistant using Retrieval-Augmented Generation: An evaluation study for bachelor projects
Apr 1, 2026Dumitru Verşebeniuc, Martijn Elands, Sara Falahatkar +4Educational QA Question Answering
Department of Advanced Computing Sciences, Maastricht University, Maastricht 6200 MD, The Netherlands
Exploring Silent Data Corruption as a Reliability Challenge in LLM Training
Apr 1, 2026Anton Altenbernd, Philipp Wiesner, Odej KaoLLM Reliability LLM Training
Technische Universität Berlin, Berlin, Germany
Towards Reliable Truth-Aligned Uncertainty Estimation in Large Language Models
Apr 1, 2026Ponhvoan Srey, Quang Minh Nguyen, Xiaobao Wu +1LLM Uncertainty Estimation Uncertainty Calibration
Nanyang Technological University, Singapore · KAIST, South Korea · Shanghai Jiao Tong University
Decision-Centric Design for LLM Systems
Apr 1, 2026Wei SunLLM Decision-Making Language Model-Based Control
IBM Research
Can Large Language Models Self-Correct in Medical Question Answering? An Exploratory Study
Mar 31, 2026Zaifu Zhan, Mengyuan Cui, Rui ZhangLLM Self-Correction LLM Reliability
Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis, USA · Division of Computational Health Sciences, Department of Surgery, University of Minnesota, Minneapolis, USA · Department of Software Engineering, University of St. Thomas, Saint Paul,USA
Learning to Generate Formally Verifiable Step-by-Step Logic Reasoning via Structured Formal Intermediaries
Mar 31, 2026Luoxin Chen, Yichi Zhou, Huishuai ZhangLLM Reasoning LLM Reliability
Wangxuan Institue of Computer Technology, Peking University. · ByteDance.
Security in LLM-as-a-Judge: A Comprehensive SoK
Mar 31, 2026Aiman Al Masoud, Antony Anju, Marco Arazzi +6LLM-as-a-Judge LLM Security
Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy · Department of Computer Applications, Cochin University of Science and Technology, Kerala, India
Audio Hallucination Attacks: Probing the Reliability of Large Audio Language Models
Mar 31, 2026Ashish Seth, Sonal Kumar, Ramaneswaran Selvakumar +5Audio-Language Model Evaluation LLM Reliability
University of Maryland, College Park · Adobe Research
Beyond pass@1: A Reliability Science Framework for Long-Horizon LLM Agents
Mar 31, 2026Aaditya Khanal, Yangyang Tao, Junxiu ZhouLLM Reliability LLM Agent Reliability
School of Computing and Analytics, Northern Kentucky University
The Model Says Walk: Measuring whether LLMs Condition on Hidden Constraints
Mar 30, 2026Yubo Li, Lu Zhang, Tianchong Jiang +2Constraint-Guided Reasoning in Language Models Implicit Reasoning in Language Models
Carnegie Mellon University · Independent Researcher

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The Oracle's Fingerprint: Correlated AI Forecasting Errors and the Limits of Bias Transmission

Theodor Spiro

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When large language models (LLMs) are consulted as forecasting tools, the independence of individual errors -- the foundation of collective intelligence -- may collapse. We test three conditions necessary for this "epistemic monoculture" to emerge. In Study 1, we show that GPT-4o, Claude, and Gemini exhibit highly correlated forecasting errors on 568 resolved binary prediction questions (mean pairwise error correlation r = 0.77, p < 0.001; r = 0.78 excluding likely-leaked questions), despite being developed independently by different organizations. In Study 2, we test whether this correlated bias has propagated into human crowd forecasts, using a within-question design that tracks community prediction shifts across the ChatGPT launch boundary (November 2022). We find that community forecasts move in the direction predicted by LLMs (r = 0.20, p = 0.007), but this shift is fully explained by rational updating toward ground truth. In Study 3, we examine whether the category-level pattern of human forecasting errors increasingly resembles the LLM bias fingerprint. We find the opposite: pre-ChatGPT human biases already strongly resembled the LLM pattern (r = 0.87), while post-ChatGPT the resemblance weakened (r = -0.28). Together, these findings reveal an epistemic monoculture that is built but not yet activated: three nominally independent AI systems share the same failure modes, amplifying precisely the biases humans already hold.