Uncertainty Quantification

Also known as UQ

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44 papers in the last four weeks, up 110% on the four weeks before. 0.4% of all new papers.

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Quantifying Aleatoric Uncertainty of In-Context Learning for Robust Measure of LLM Prediction Confidence
Apr 28, 2026Jinseok Chung, Minkyoung Song, Hyunji Jung +1Uncertainty Quantification In-Context Learning
POSTECH
Biased Dreams: Limitations to Epistemic Uncertainty Quantification in Latent Space Models
Apr 28, 2026Julia Berger, Bernd Frauenknecht, Sebastian Trimpe +1Uncertainty Quantification Latent Dynamics Modeling
1RWTH Aachen University
Conditional Flow Matching for Probabilistic Downscaling of Maximum 3-day Snowfall in Alaska
Apr 28, 2026Douglas Brinkerhoff, Elizabeth FischerUncertainty Quantification Climate Science
Department of Computer Science, University of Montana · International Arctic Research Center, University of Alaska
Learning with Embedded Linear Equality Constraints via Variational Bayesian Inference
Apr 27, 2026Matthew Marsh, Benoît Chachuat, Antonio del Rio ChanonaBayesian Neural Networks Uncertainty Quantification
Sargent Centre for Process Systems Engineering, Imperial College London
BaLoRA: Bayesian Low-Rank Adaptation of Large Scale Models
Apr 27, 2026Dario Coscia, Sindy Löwe, Max WellingBayesian Neural Networks Uncertainty Quantification
mathLab, SISSA · CuspAI
Credal Concept Bottleneck Models for Epistemic-Aleatoric Uncertainty Decomposition
Apr 27, 2026Tanmoy Mukherjee, Thomas Bailleux, Pierre Marquis +1Uncertainty Quantification Concept Bottleneck Models
Univ. Artois, CNRS, CRIL, France · Institut Universitaire de France
Uncertainty Propagation in LLM-Based Systems
Apr 26, 2026Boming Xia, Liming Zhu, Erdun Gao +3Uncertainty Quantification LLM Uncertainty Estimation
1Responsible AI Research (RAIR) Centre · 2Adelaide University · 3CSIRO +1
Uncertainty Quantification for LLM Function-Calling
Apr 24, 2026Zihuiwen Ye, Lukas Aichberger, Michael Kirchhof +5Uncertainty Quantification LLM Tool Use
University of Oxford · Apple
Revisiting Neural Activation Coverage for Uncertainty Estimation
Apr 24, 2026Benedikt Franke, Nils Förster, Frank Köster +3Uncertainty Quantification
DLR Institute for AI Safety and Security, Ulm - Germany · Ruhr University Bochum, Bochum, Germany
Algorithm and Hardware Co-Design for Efficient Complex-Valued Uncertainty Estimation
Apr 21, 2026Zehuan Zhang, Mark Chen, He Li +1Bayesian Neural Networks Uncertainty Quantification
Department of Computing, Imperial College London · 2Southeast University
Analytical Extraction of Conditional Sobol' Indices via Basis Decomposition of Polynomial Chaos Expansions
Apr 21, 2026Shijie Zhong, Jiangfeng FuUncertainty Quantification Sensitivity Analysis
School of Power and Energy Northwestern Polytechnical University Xi’an, Shanxi 710129
Mind the Unseen Mass: Unmasking LLM Hallucinations via Soft-Hybrid Alphabet Estimation
Apr 21, 2026Hongxing Pan, Yingying Guo, Wenqing Kuang +1Uncertainty Quantification LLM Hallucination Detection
School of Data Science The Chinese University of Hong Kong, Shenzhen
Online Conformal Prediction with Adversarial Semi-bandit Feedback via Regret Minimization
Apr 20, 2026Junyoung Yang, Kyungmin Kim, Sangdon ParkUncertainty Quantification Conformal Prediction
GSAI POSTECH · GSAI & CSE POSTECH
Trustworthy deep domain adaptation for wearable photoplethysmography signal analysis with decision-theoretic uncertainty quantification
Apr 19, 2026Ciaran BenchUncertainty Quantification Domain Adaptation
Department of Data Science and AI National Physical Laboratory Teddington, UK
Uncertainty Quantification in PINNs for Turbulent Flows: Bayesian Inference and Repulsive Ensembles
Apr 18, 2026Khemraj Shukla, Zongren Zou, Theo Kaeufer +2Uncertainty Quantification Deep Ensembles
Division of Applied Mathematics, Brown University, Providence, RI, USA · Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, CA, USA · Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
Reference-state System Reliability method for scalable uncertainty quantification of coherent systems
Apr 18, 2026Ji-Eun Byun, Hyeuk Ryu, Junho SongUncertainty Quantification
Department of Civil and Environmental Engineering, Imperial College London, United Kingdom · Geoscience Australia, Australia · Department of Civil, Urban, and Environmental Engineering, Seoul National University, South Korea
ReconVLA: An Uncertainty-Guided and Failure-Aware Vision-Language-Action Framework for Robotic Control
Apr 17, 2026Lingling Chen, Zongyao Lyu, William J. BeksiUncertainty Estimation for VLA Models Robot Failure Detection
Department of Computer Science and Engineering, The University of Texas at Arlington, Arlington, TX, USA
SegWithU: Uncertainty as Perturbation Energy for Single-Forward-Pass Risk-Aware Medical Image Segmentation
Apr 16, 2026Tianhao Fu, Austin Wang, Charles Chen +2Image Segmentation Uncertainty Quantification
University of Toronto, Toronto, ON, Canada · Vector Institute, Toronto, ON, Canada · Project Neura, Toronto, ON, Canada +4
IUQ: Interrogative Uncertainty Quantification for Long-Form Large Language Model Generation
Apr 16, 2026Haozhi Fan, Jinhao Duan, Kaidi XuUncertainty Quantification Language Model Generation Evaluation
University of Pennsylvania · 2UNC Chapel Hill · 3City University of Hong Kong
xFODE+: Explainable Type-2 Fuzzy Additive ODEs for Uncertainty Quantification
Apr 16, 2026Ertugrul Kececi, Tufan KumbasarUncertainty Quantification Fuzzy Logic
AI and Intelligent Systems Laboratory, Istanbul Technical University, 34469, Istanbul, Türkiye
A Robust Evaluation of Probe Robustness: Lessons for Reliable OOD Uncertainty Quantification
Apr 13, 2026Joe Stacey, Hadas Orgad, Kentaro Inui +2LLM Evaluation Uncertainty Quantification
University of Sheffield · Kempner Institute at Harvard University · MBZUAI +2
Geometrically-Constrained Radar-Inertial Odometry via Continuous Point-Pose Uncertainty Modeling
Apr 3, 2026Wooseong Yang, Dongjae Lee, Minwoo Jung +1Simultaneous Localization and Mapping Inertial Odometry
Dept. of Mechanical Engineering, SNU, Seoul, S. Korea
Safe learning-based control via function-based uncertainty quantification
Apr 1, 2026Abdullah Tokmak, Toni Karvonen, Thomas B. Schön +1Uncertainty Quantification Safety-Critical Control
Cyber-physical Systems Group, Aalto University, Espoo, Finland · School of Engineering Sciences, Lappeenranta–Lahti University of Technology LUT, Lappeenranta, Finland · Department of Information Technology, Uppsala University, Uppsala, Sweden
An Isotropic Approach to Efficient Uncertainty Quantification with Gradient Norms
Mar 31, 2026Nils Grünefeld, Jes Frellsen, Christian HardmeierUncertainty Quantification LLM Uncertainty Estimation
1IT University of Copenhagen · 3Pioneer Centre for Artificial Intelligence · 2Technical University of Denmark
Quantification of Credal Uncertainty: A Distance-Based Approach
Mar 28, 2026Xabier Gonzalez-Garcia, Siu Lun Chau, Julian Rodemann +6Uncertainty Quantification Aleatoric Uncertainty
Dept. of Statistics, CS & Mathematics, Public Univ. of Navarre · EPIC Lab, Nanyang Technological Univ. Singapore · Rational Intelligence Lab, CISPA Helmholtz Center for Information Security +7
A plug-and-play approach with fast uncertainty quantification for weak lensing mass mapping
Mar 23, 2026Hubert Leterme, Andreas Tersenov, Jalal Fadili +1Uncertainty Quantification
Université Caen Normandie, ENSICAEN, CNRS, Normandie Univ, GREYC UMR 6072, F-14000 Caen, France · Université Paris-Saclay, Université Paris Cité, CEA, CNRS, AIM, 91191, Gif-sur-Yvette, France · Institutes of Computer Science and Astrophysics, Foundation for Research and Technology Hellas (FORTH), Heraklion, Greece
A Variational Latent-Space Framework for Uncertainty-Aware Spectral Image Emulation
Mar 23, 2026Chedly Ben Azizi, Claire Guilloteau, Gilles Roussel +1Neural Surrogate Modeling Variational Autoencoders
Univ. Littoral Côte d’Opale, LISIC – UR 4491, 62219, Longuenesse, France
Achieving $\widetilde{O}(1/ε)$ Sample Complexity for Bilinear Systems Identification under Bounded Noises
Mar 21, 2026Hongyu Yi, Chenbei Lu, Jing YuParameter Estimation Uncertainty Quantification
Department of Electrical and Computer Engineering, University of Washington, Seattle, WA, USA. · Cornell University AI for Science Institute, Cornell University, Ithaca, NY, USA.
Rethinking Uncertainty Quantification and Entanglement in Image Segmentation
Mar 19, 2026Jakob Lønborg Christensen, Vedrana Andersen Dahl, Morten Rieger Hannemose +2Uncertainty Quantification Deep Ensembles
Technical University of Denmark, DTU Compute · University of Lucerne, Faculty of Health Sciences and Medicine, Switzerland
Structured SIR: Efficient and Expressive Importance-Weighted Inference for High-Dimensional Image Registration
Mar 18, 2026Ivor J. A. Simpson, Neill D. F. CampbellUncertainty Quantification Importance Sampling
Department of Informatics University of Sussex, UK · Department of Computer Science University College London, UK · Department of Computer Science University of Bath, UK

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Quantifying Aleatoric Uncertainty of In-Context Learning for Robust Measure of LLM Prediction Confidence

Jinseok Chung, Minkyoung Song, Hyunji Jung, Namhoon Lee

POSTECH

Uncertainty Quantification In-Context Learning LLM Uncertainty Estimation Aleatoric Uncertainty Uncertainty Decomposition

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In-Context Learning (ICL) allows LLMs to adapt to new tasks from a few demonstrations, but its reliability remains a concern: predictions are highly sensitive to both prompt design and the model's ability to understand the context, obscuring whether failures arise from data properties or model limitations. Uncertainty decomposition-separating aleatoric from epistemic sources-is particularly crucial in this setting, yet existing methods, designed for standard generation tasks, fail to capture the unique dynamics of ICL. To address this, we introduce a concept of self-function vectors, built upon Bayesian views and the mechanistic interpretability of ICL. These vectors leverage internal model representations to model the latent concept learned during in-context prompting, thereby enabling a direct estimation of aleatoric uncertainty within a Bayesian framework and circumventing the reliance on brittle input or decoding manipulations. Given the lack of established benchmarks and suitable evaluation protocols, we also propose the first and rigorous evaluation protocol, in which data is manipulated in controlled ways so as to quantify aleatoric uncertainty precisely and separately from epistemic uncertainty. With this new evaluation framework, initially grounded in synthetic tasks for conceptual development and subsequently extended to real-world datasets, we show that our proposed methodology can measure uncertainty of LLM predictions made under ICL more reliably than existing alternative methods. Moreover, we show it can be used as a practical tool for trustworthy-related applications, such as hallucination detection. Our findings pave a new direction for connecting the quantitative view of uncertainty with the mechanistic understanding of model behavior.

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Latest papers 431