Uncertainty Calibration

Follow this topic Ask about this topic

Momentum

15 papers in the last four weeks, up 7% on the four weeks before. 0.1% of all new papers.

Jul 13Week of Sep 28

Latest papers 184

All topics

SAGE: Answer-Conditioned Uncertainty Targets for Verbal Uncertainty Alignment
Jun 9, 2026Kaiwen Shi, Zheyuan Zhang, Yanfang YeConfidence Estimation in Language Models Preference Optimization
University of Notre Dame
Can we trust our models? Epistemic calibration in second-order classification
Jun 9, 2026Arthur HoarauUncertainty Quantification Uncertainty Calibration
Université de Lorraine, CentraleSupélec · Loria, CNRS, Metz, France
SPACR: Single-Pass Adaptive Training of Uncertainty-Aware Conformal Regressors
Jun 9, 2026Soundouss Messoudi, Sylvain Rousseau, Sébastien DesterckeConformal Prediction Adaptive Conformal Inference
Heudiasyc - UMR CNRS 7253, Université de Technologie de Compiègne
Operator learning for the 2D incompressible Navier-Stokes equations: a conformal prediction approach in the data-scarce regime
Jun 7, 2026Weinan Wang, Bowen Gang, Hao DengUncertainty Quantification Conformal Prediction
Department of Mathematics, University of Oklahoma, Norman, OK, USA · Department of Statistics and Data Science, School of Management, Fudan University, Shanghai, China
The Confidence Trap: Calibration Attacks for Graph Neural Networks
Jun 7, 2026Cuong Dang, Jiahao Zhang, Hieu Ta Quang +3Graph Neural Networks Adversarial Attacks
Department of Electrical and Computer Engineering Virginia Polytechnic Institute and State University Blacksburg, VA, USA · The Pennsylvania State University University Park, PA, USA · Center for AI Research VinUniversity Hanoi, Vietnam +1
Trust-Aware Predictive Emissions Monitoring for Gas Turbine Fleets with Limited Labelled Data
Jun 4, 2026Rebecca Potts, Aiden Durrant, Rick Hackney +1Trust in AI Uncertainty Calibration
School of Natural and Computing Sciences University of Aberdeen, Aberdeen, UK · School of Computing Sciences University of East Anglia, UK · Siemens Energy Industrial Turbomachinery Ltd. Lincoln, UK +1
SC3: The Multi-Solvent Solubility Challenge and Benchmark
Jun 3, 2026Vansh Ramani, Har Ashish Arora, Dhairya Kuchhal +4Benchmark Construction Benchmark Validity
Indian Institute of Technology Delhi, India · Kurnakov Institute of General and Inorganic Chemistry RAS, Russia
CANMOT: Class-Aware Noise Modeling for Multi-Object Tracking in Autonomous Driving
Jun 2, 2026Timo Osterburg, Stefan Schütte, Torsten BertramKalman Filtering Autonomous Driving
Institute of Control Theory and Systems Engineering, TU Dortmund University, Germany.
FUSE: Quantifying Uncertainty in Vision-Language Models by Bayesian Fusing Epistemic and Aleatoric Uncertainty
Jun 1, 2026Harry Zhang, Luca CarloneUncertainty Estimation for VLA Models Vision-Language Models
Massachusetts Institute of Technology
ProbRes: Volatility Learning for Probabilistic Time-Series Forecasting
Jun 1, 2026Tingting Wang, Yunyi Zhang, Benyou WangFinancial Time Series Uncertainty Calibration
School of Data Science, The Chinese University of Hong Kong, Shenzhen
Uncertainty-Calibrated Diffusion for Reliable 3D Molecular Graph Generation
Jun 1, 2026Fang Wan, Jingxiang Qu, Yi LiuDiffusion Sampling Molecular Graph Generation
State University of New York at Stony Brook
Online Irregular Multivariate Time Series Forecasting via Uncertainty-Driven Dual-Expert Calibration
May 27, 2026Haonan Wen, Hanyang Chen, Songhe FengIrregular Time-Series Modeling Multivariate Time Series Forecasting
Key Laboratory of Big Data & Artificial Intelligence in Transportation (Beijing Jiaotong University), Ministry of Education School of Computer Science and Technology, Beijing Jiaotong University Beijing, China · School of Computer Science and Technology, Beijing Jiaotong University Beijing, China · Tangshan Research Institute, Beijing Jiaotong University Tangshan, China
High Performance, Low Reliability: Uncertainty Benchmarking for Tabular Foundation Models
May 27, 2026José Lucas De Melo Costa, Fabrice Popineau, Arpad Rimmel +1Tabular Foundation Models Uncertainty Quantification
CentraleSup´elec, Universit´e Paris-Saclay Gif-sur-Yvette - France
Chance-Constrained MPPI under State and Dynamic Object Prediction Uncertainty and the Evaluation of Collision Risk Calibration
May 27, 2026Benjamin Serfling, Konrad Doll, Kati Radkhah-LensSafe Motion Planning Motion Planning
University of Applied Sciences Aschaffenburg, Aschaffenburg, Germany
Calibrated Inference for the Conditional Average Treatment Effect in the Few-Placebo Regime via Gaussian Processes
May 26, 2026Eichi UeharaCausal Effect Estimation Conditional Average Treatment Effect Estimation
AFLO
Proper Calibeating
May 26, 2026Dean P. Foster, Sergiu HartProper Scoring Rules Uncertainty Calibration
Department of Statistics, Wharton, University of Pennsylvania, Philadelphia, and Amazon, New York. · Institute of Mathematics, Department of Economics, and Federmann Center for the Study of Rationality, The Hebrew University of Jerusalem.
Distribution-Aware Conformal Prediction: A Framework for generating efficient prediction intervals for time series
May 26, 2026Daniel Schweizer, Peter Kuhn, Jayant Sharma +3Conformal Prediction Uncertainty Calibration
Fraunhofer Institute for Highspeed Dynamics, Ernst-Mach-Institut, EMI Freiburg, Germany
MiRD: Reliable Set-Valued Prediction for Open-Ended Question Answering via Miscoverage Risk Decomposition
May 25, 2026Anqi Hu, Zhiyuan Wang, Zijun Jia +1LLM Hallucination Mitigation Set Prediction
University of Electronic Science and Technology of China · Beihang University
The Behavioral Credibility Trilemma: When Calibrated Autonomy Becomes Impossible
May 25, 2026Lauri Lovén, Nam Do, Hassan Mehmood +2AI Agent Reliability AI Agent Governance
Future Computing Group University of Oulu Oulu, Finland · Department of Computer Science University of Helsinki Helsinki, Finland
Proper Scoring Rules for Agentic Uncertainty Quantification
May 23, 2026Suresh Raghu, Satwik Pandey, Shashwat PandeyUncertainty Quantification Agent Evaluation
Independent Researcher
Calibrating Probabilistic Object Detectors with Annotator Disagreement
May 23, 2026Zhi Qin Tan, Owen Addison, Yunpeng LiAnnotator Disagreement Object Detection
Faculty of Dentistry, Oral & Craniofacial Sciences, King’s College London, London, United Kingdom
Overcoming "Physics Shock" in Earth Observation A Heteroscedastic Uncertainty Framework for PINN-based Flood Inference
May 22, 2026Tewodros Syum Gebre, Jagrati Talreja, Matilda Anokye +1Remote Sensing Image Segmentation Uncertainty Quantification
Built Environment Department, College of Science and Technology, North Carolina A&T State University, Greensboro, NC 27405 USA · United Nations University Institute for Water, Environment and Health, Richmond Hill, ON, Canada
Dirichlet-Based Monte Carlo Dropout for Uncertainty Estimation in Neural Networks
May 22, 2026Rouaa Hoblos, Noura Dridi, Noureddine Zerhouni +1Evidential Deep Learning Uncertainty Quantification
FEMTO-ST · Universit´e Marie et Louis Pasteur, SUPMICROTECH, CNRS, Institut FEMTO-ST, F-25000 Besan¸con, France
Enhancing Deep Neural Network Reliability with Refinement and Calibration
May 22, 2026Ramya Hebbalaguppe, Ajay Shastry, Soumya Suvra Ghosal +1Probability Calibration Uncertainty Calibration
SIT, Indian Institute of Technology Delhi, New Delhi, India
UfM*: Uncertainty from Motion* for DNN Depth Estimation Using Gaussians
May 21, 2026Soumya Sudhakar, Sertac Karaman, Vivienne SzeUncertainty Quantification Depth Estimation
Massachusetts Institute of Technology, Cambridge, MA 02139, USA
MARGIN: Runtime Confidence Calibration for Multi-Agent Foundation Model Coordination
May 21, 2026Joss ArmstrongAI Agent Reliability Multi-Agent Coordination
Ericsson, Athlone, Ireland
Decomposing Ensemble Spread in Lorenz '96 With Learned Stochastic Parameterizations
May 21, 2026Birgit Kühbacher, Daan Crommelin, Niki KilbertusUncertainty Quantification Chaotic Dynamical Systems
Technical University of Munich, Munich, Germany · Helmholtz Munich, Munich, Germany · Munich Center for Machine Learning (MCML), Munich, Germany +2
Invascal: Inverse-Vacuity Self-Calibration for Uncertainty-Aware LiDAR Range-View Semantic Segmentation
May 20, 2026Kerim Turacan, Hannes Reichert, Andrei Bolandut +1Evidential Deep Learning 3D Semantic Segmentation
Faculty of Engineering and Computer Science, University of Applied Sciences Aschaffenburg, Aschaffenburg, Germany
Anomaly-Informed Confidence Calibration for Vision-Based Safety Prediction
May 20, 2026Zhenjiang Mao, Jiawen Wu, Gabriel Wagner +2Post-Hoc Calibration Autonomous Racing
Trustworthy Engineered Autonomy (TEA) Lab, Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL 32611, USA
Expectation Consistency Loss: Rethink Confidence Calibration under Covariate Shift
May 20, 2026Jinzong Dong, Zhaohui Jiang, Bo YangProbability Calibration Unsupervised Domain Adaptation
School of Automation, Central South University, Changsha, China.

Jun 9, 2026, cs.CLJ/K move · Enter open · S save

SAGE: Answer-Conditioned Uncertainty Targets for Verbal Uncertainty Alignment

Kaiwen Shi, Zheyuan Zhang, Yanfang Ye

University of Notre Dame

Confidence Estimation in Language Models Preference Optimization LLM Uncertainty Estimation Uncertainty Calibration

Open paper AskPDF arXiv

Large language models increasingly express uncertainty through natural-language statements, yet these expressions often fail to reflect the model's sampled behavior. We study verbal uncertainty alignment as a distributional calibration problem: the appropriate uncertainty target for a prompt should be estimated from repeated model outputs rather than from an isolated response. However, group rollouts alone are insufficient, since the resulting target must provide a useful training signal. Existing targets only partially satisfy this requirement. We propose SAGE, Semantic-Answer Guided Entropy, a group-level uncertainty target that constructs an answer-conditioned uncertainty geometry over sampled responses. SAGE preserves categorical, numeric, and symbolic answer distinctions while maintaining a smooth and scale-preserving calibration signal. We further apply this target through Group-Uncertainty Preference Optimization, or GUPO, an uncertainty-channel training framework that supervises verbal uncertainty expressions rather than the full response. Experiments across factual, mathematical, and multiple-choice reasoning tasks show improved uncertainty ranking, lower calibration error, and reduced overconfidence.

Momentum

Related topics

Latest papers 184