Aleatoric Uncertainty

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Aleatoric uncertainty describes variability that remains even when a model has learned the available patterns, such as measurement noise, ambiguous labels, or inherently stochastic outcomes. Researchers estimate it with predictive distributions, heteroscedastic models, and probabilistic or Bayesian methods, often separating it from epistemic uncertainty caused by limited knowledge. Evaluation commonly examines calibration, predictive intervals, proper scoring rules, and whether uncertainty tracks independent signals such as annotator disagreement or changing noise levels. A central challenge is that estimates depend on how uncertainty is defined and modeled, and separating irreducible variability from model error is not always identifiable or reliable.

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44 papers4 papers in the last four weeks, down 20% on the four weeks before. 0.0% of all new papers.

Complementing Self-Consistency with Cross-Model Disagreement for Uncertainty Quantification
Apr 18, 2026Kimia Hamidieh, Veronika Thost, Walter Gerych +2Selective Prediction Confidence Estimation in Language Models
1MIT · MIT-IBM Watson AI Lab · 3WPI +1
Retrieval-Augmented Generation Must Move Beyond Factual Grounding to Represent Diverse Opinions
Apr 13, 2026Aditya Agrawal, Alwarappan Nakkiran, Aman Singh Thakur +2Retrieval-Augmented Generation Aleatoric Uncertainty
Enhancing the Reliability of Medical AI through Expert-guided Uncertainty Modeling
Apr 2, 2026Aleksei Khalin, Ekaterina Zaychenkova, Aleksandr Yugay +4Aleatoric Uncertainty Uncertainty Quantification
Kharkevich Institute for Information Transmission Problems of Russian Academy of Sciences, Moscow, Russia · Moscow Insitute of Physics and Technology, Dolgoprudny, Russia · Skolkovo Institute of Science and Technology, Moscow, Russia +2
Variational LSTM with Augmented Inputs: Nonlinear Response History Metamodeling with Aleatoric and Epistemic Uncertainty
Apr 2, 2026Manisha Sapkota, Min Li, Bowei LiSurrogate Modeling Uncertainty Propagation
Department of Civil, Environmental, and Construction Engineering, Texas Tech University, 2500 Broadway St, Lubbock 79409, TX, USA · Department of Civil and Environmental Engineering, Rensselaer Polytechnic Institute, 110 8th Street, Troy 12180, NY, USA · National Wind Institute, Texas Tech University, 1009 Canton Ave, Lubbock 79409, TX, USA
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
Beyond the Mean: Distribution-Aware Loss Functions for Bimodal Regression
Mar 20, 2026Abolfazl Mohammadi-Seif, Carlos Soares, Rita P. Ribeiro +1Aleatoric Uncertainty Uncertainty Quantification
Department of Engineering, Universitat Pompeu Fabra, Roc Boronat St., Barcelona, 08018, Barcelona, Spain. · Department of Engineering, University of Porto, Praça de Gomes Teixeira, Porto, 4099-002, Portugal. · Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal. +1
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
CUPID: A Plug-in Framework for Joint Aleatoric and Epistemic Uncertainty Estimation with a Single Model
Mar 11, 2026Xinran Xu, Xiuyi FanUncertainty Quantification Epistemic Uncertainty
Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore · College of Computing and Data Science, Nanyang Technological University, Singapore · Centre for Medical Technologies & Innovations, National Health Group, Singapore
TRIAGE: Type-Routed Interventions via Aleatoric-Epistemic Gated Estimation in Robotic Manipulation and Adaptive Perception -- Don't Treat All Uncertainty the Same
Mar 9, 2026Divake Kumar, Sina Tayebati, Devashri Naik +4Robotic Control Uncertainty Decomposition
University of Illinois at Chicago · Intel Labs
Epistemic Gain, Aleatoric Cost: Uncertainty Decomposition in Multi-Agent Debate for Math Reasoning
Mar 1, 2026Dan Qiao, Binbin Chen, Fengyu Cai +7Multi-Agent Debate Epistemic Uncertainty
School of Data Science, The Chinese University of Hong Kong, Shenzhen, China · ByteDance, Beijing, China · Technical University of Darmstadt, Darmstadt, Germany +2
Disentangling Aleatoric and Epistemic Uncertainty in Physics-Informed Neural Networks. Application to Insulation Material Degradation Prognostics
Jan 7, 2026Ibai Ramirez, Jokin Alcibar, Joel Pino +2Bayesian Neural Networks Uncertainty Quantification
Department of Electronics and Computer Science Mondragon University Arrasate-Mondragon, 20500, Spain · CARTIF Technology Centre Valladolid, 47151, Spain · Department of Physical Chemistry University of the Basque Country (EHU)[cs.LG] Basque Centre for Applied Mathematics (BCAM) Leioa, 20018, Spain +1
Uncertainty Quantification for Regression: A Unified Framework based on kernel scores
Oct 29, 2025Christopher Bülte, Yusuf Sale, Gitta Kutyniok +1Uncertainty Quantification Aleatoric Uncertainty
LMU Munich · 2Munich Center for Machine Learning (MCML) · 3DLR-German Aerospace Center +2
CLEAR: Calibrated Learning for Epistemic and Aleatoric Risk
Jul 10, 2025Ilia Azizi, Juraj Bodik, Jakob Heiss +1Uncertainty Quantification Aleatoric Uncertainty
Department of Operations, HEC, University of Lausanne, Switzerland · BegooAI, Switzerland · Department of Statistics, University of California, Berkeley, USA +1
An Axiomatic Assessment of Entropy- and Variance-based Uncertainty Quantification in Regression
Apr 25, 2025Christopher Bülte, Yusuf Sale, Timo Löhr +3Uncertainty Quantification Aleatoric Uncertainty
1LMU Munich · 2Munich Center for Machine Learning (MCML) · 3DLR-German Aerospace Center +2

Apr 18, 2026J/K move · Enter open · S save

Complementing Self-Consistency with Cross-Model Disagreement for Uncertainty Quantification

Kimia Hamidieh, Veronika Thost, Walter Gerych, Mikhail Yurochkin+1

1MIT · MIT-IBM Watson AI Lab · 3WPI+1

Selective Prediction Confidence Estimation in Language Models LLM Reliability LLM Uncertainty Estimation Aleatoric Uncertainty Uncertainty Calibration+2

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Large language models (LLMs) often produce confident yet incorrect responses, and uncertainty quantification is one potential solution to more robust usage. Recent works routinely rely on self-consistency to estimate aleatoric uncertainty (AU), yet this proxy collapses when models are overconfident and produce the same incorrect answer across samples. We analyze this regime and show that cross-model semantic disagreement is higher on incorrect answers precisely when AU is low. Motivated by this, we introduce an epistemic uncertainty (EU) term that operates in the black-box access setting: EU uses only generated text from a small, scale-matched ensemble and is computed as the gap between inter-model and intra-model sequence-semantic similarity. We then define total uncertainty (TU) as the sum of AU and EU. In a comprehensive study across five 7-9B instruction-tuned models and ten long-form tasks, TU improves ranking calibration and selective abstention relative to AU, and EU reliably flags confident failures where AU is low. We further characterize when EU is most useful via agreement and complementarity diagnostics.