Uncertainty Propagation

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2 papers in the last four weeks, down 60% on the four weeks before. 0.0% of all new papers.

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Propagating Elevation-Map Uncertainty Through the Contact Maximum in Closed Form
Oct 5, 2026Aleš Kučera, Karel ZimmermannUncertainty Quantification Safe Motion Planning
Department of Cybernetics, Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, Czech Republic
Human-in-the-Loop Geospatial Annotation for Rapid Dataset Construction in Field-Deployed UAV Systems
Sep 23, 2026Morgan Masters, Adam Korycki, Nikolaas Bender +4Human-in-the-Loop Annotation Uncertainty Propagation
Department of Electrical and Computer Engineering, University of California Santa Cruz
Marginal Calibration Does Not Compose: Hidden Dependence in Modular Robot Navigation
Sep 20, 2026Rista BaralModular Robotics Robot State Estimation
University of Delaware
Uncertainty-Aware Trajectory Forecasting from Imperfect Tracking
Aug 31, 2026Stephane Da Silva Martins, Victor Petrovic, Emanuel Aldea +1Probabilistic Forecasting Uncertainty Propagation
SATIE – CNRS UMR 8029, Université Paris-Saclay, France · ENS Paris-Saclay, Université Paris-Saclay, France
PropUQ-MAS: Propagation-Aware Uncertainty Quantification for LLM Multi-Agent Systems
Aug 22, 2026Yaokun Liu, Yifan Liu, Daniel Yue Zhang +3Multi-Agent LLM Systems LLM Uncertainty Estimation
University of Illinois Urbana-Champaign · Scale AI
Sensor-Informed Per-Point Covariance for Structured-Light 3D Imaging
Aug 11, 2026Sehoon Tak, Jae-Sang HyunFringe Projection Profilometry Uncertainty Quantification
Department of Mechanical Engineering, Yonsei University, Seoul 03722, South Korea
Structure-Preserving Uncertainty Propagation in First-Order Proof Search
Aug 10, 2026Tanel TammetProbabilistic Logic Logical Reasoning
Tallinn University of Technology
Two-Step MV-DeepONet: Probabilistic Operator Learning for Uncertainty Propagation Driven by Random Input Fields
Aug 10, 2026Yupei Nie, Lei Wang, Jiasen LiuUncertainty Quantification PDE Surrogate Modeling
School of Mathematics and Physics, North China Electric Power University, Beijing 102206, P.R. China · School of Energy Power and Mechanical Engineering, North China Electric Power University, Beijing 102206, P.R. China
CUPA-T2*: Covariance-Aware Uncertainty Propagation and Alignment for T2* Mapping in Accelerated MRI
Aug 9, 2026Gideon N. L. Rouwendaal, Natascha Niessen, Hannah Eichhorn +3Uncertainty Quantification Heteroscedastic Regression
Institute of Machine Learning in Biomedical Imaging, Helmholtz Munich, Germany · School of Computation, Information & Technology, Technical University of Munich, Germany · GE HealthCare, Munich, Germany +6
PPDL: LLM-Based Flows as Probabilistic Programs
Aug 5, 2026Louis Mandel, Guillaume Baudart, Mandana Vaziri +1LLM Inference LLM Reliability
IBM, New York, USA · Université Paris Cité, Inria, CNRS, IRIF, France
Runtime Uncertainty Monitoring for LLM-Based Multi-Agent Systems Using Bayesian Networks
Jul 28, 2026Bart Custers, Koorosh AslansefatMulti-Agent LLM Systems Uncertainty Quantification
University of Hull, Hull HU6 7RX, UK
Interval and fuzzy physics-augmented neural networks (iPANN and fPANN) for uncertainty quantification and propagation in constitutive modeling
Jul 22, 2026Somesh Pratap Singh, Govinda Anantha Padmanabha, Jingye Tan +4Uncertainty Quantification Hyperelasticity
Sibley School of Mechanical and Aerospace Engineering, Cornell University, Ithaca, 14853, NY, USA · Ecole Polytechnique Federale de Lausanne (EPFL), 1015, Lausanne, Switzerland · Department of Aerospace and Mechanical Engineering, University of Southern California, Los Angeles, 90007, CA, USA +2
Bayesian Uncertainty Propagation for Agentic RAG Pipelines: A Proof-of-Concept Study on Multi-Hop Question Answering
Jul 1, 2026Louis Donaldson, Connor Walker, Koorosh Aslansefat +1Uncertainty Quantification Agentic RAG
University of Hull, Hull HU6 7RX, UK
YUKTI: From Natural-Language Situations to Robust, Verifiable Decisions An Uncertainty-Typed Proposition IR, Assumption-Robust Pareto Frontiers, and a Regret Certificate
Jun 22, 2026Suyash MishraLarge Language Model-Guided Optimization Robust Optimization
AI Researcher
Continuous-Time Probabilistic Correctors for Uncertainty-Aware Physics-Based Spacecraft Trajectory Forecasting
Jun 19, 2026Muhammad Bilal Shahid, Zhanhong Jiang, Soumik Sarkar +1Neural Controlled Differential Equations Uncertainty Quantification
Iowa State University, Ames, Iowa, 50011, USA
Calibrated Sampling-Free Uncertainty Estimation in Bayesian Deep Learning
Jun 15, 2026Tobias Jan Wieczorek, Leon de Andrade, Thomas Möllenhoff +1Bayesian Neural Networks Uncertainty Quantification
TU Darmstadt & hessian.AI, Darmstadt, Germany · RIKEN Center for Advanced Intelligence Project, Tokyo, Japan
Confidence Laundering in Agent Systems: Why Uncertainty Needs a Latent Carrier
Jun 9, 2026Kaiwen Shi, Zheyuan Zhang, Han Bao +2Agent Reliability Uncertainty Quantification
University of Notre Dame
A practical probabilistic framework for deformable image registration uncertainty in radiotherapy dose propagation
Jun 8, 2026Stefan Heldmann, Sven Kuckertz, Nasim Givehchi +4Uncertainty Quantification Oncology
Fraunhofer Institute for Digital Medicine MEVIS, Lübeck, Germany · Varian, a Siemens Healthineers company, Dättwil, Switzerland · Icon Group, South Brisbane, QLD, Australia
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
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
Improving Spatio-Temporal Residual Error Propagation by Mitigating Over-Squashing
May 18, 2026Seyed Mohamad Moghadas, Esther Rodrigo Bonet, Bruno Cornelis +1Graph Rewiring Over-Squashing
ETRO Department, Vrije Universiteit Brussel, B-1050 Brussels, Belgium · imec Kapeldreef 75, B-3001 Leuven, Belgium
A numerical study into neural network surrogate model performance for uncertainty propagation
May 15, 2026Noah Wade, Kirubel TeferraNeural Surrogate Modeling Uncertainty Quantification
ASEE Postdoctoral Associate, U.S. Naval Research Laboratory, Washington, DC, 20375, USA · U.S. Naval Research Laboratory, Washington, 20375, DC, USA
Learning Context-conditioned Gaussian Overbounds for Convolution-Based Uncertainty Propagation
May 15, 2026Ruirui Liu, Xuejie Hou, Yiping Jiang +1Uncertainty Quantification Uncertainty Propagation
Department of Aeronautical and Aviation Engineering, The Hong Kong Polytechnic University, Hong Kong, China
Heuristic Pathologies and Further Variance Reduction via Uncertainty Propagation in the AIVAT Family of Techniques
May 14, 2026Juho Kim, Tuomas SandholmCounterfactual Evaluation Uncertainty Quantification
Computer Science Department Carnegie Mellon University
Divergence is Uncertainty: A Closed-Form Posterior Covariance for Flow Matching
May 1, 2026Jiarui Xing, Song Wang, Jian WangFlow Matching Uncertainty Quantification
Yale University · Harvard Medical School
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
Statistical Taylor Expansion: A New and Path-Independent Method for Uncertainty Analysis
Oct 2, 2024Chengpu WangUncertainty Quantification Uncertainty Propagation
Grossman Street, Melville, NY 11747, USA
Analytical Standard Errors for Exploratory Factor Solutions
Nov 12, 2018Xingwei Hu, Caihong Hu, Cheng-Kuang WuUncertainty Quantification Dimensionality Reduction
International Monetary Fund, Washington, DC, 20431, USA · Zhangjiajie College, Zhangjiajie, Hunan, 427000, China · Zhejiang Industry and Trade Vocational College, Wenzhou, Zhejiang, 325700, China
Kalman Filtering Based Flight Management System Modeling for AAM Aircraft
Date pendingBalram Kandoria, Aryaman Singh SamyalKalman Filtering Uncertainty Quantification
SkyGrid, Austin, TX, 78727

Oct 5, 2026, cs.ROJ/K move · Enter open · S save

Propagating Elevation-Map Uncertainty Through the Contact Maximum in Closed Form

Aleš Kučera, Karel Zimmermann

Department of Cybernetics, Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, Czech Republic

Uncertainty Quantification Safe Motion Planning Uncertainty Propagation

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Risk-aware planners score paths on uncertain elevation maps using the path cost's mean and standard deviation. Modeling rigid contact, however, requires computing a maximum over several uncertain cells. First-order propagation loses accuracy here by differentiating at only a single cell, while Monte Carlo sampling requires a full path evaluation per draw. We compute the moments of that contact maximum in closed form using Clark's pairwise recursion. By tracking each contact's covariance against the shared map cells, we propagate the smooth remainder using exact Gaussian quadratic-form identities. A contest-depth calibration, fitted once on two design traverses, closes the aggregate standard-deviation shortfall that remains. On 317 held-out rover path segments, scored against a Monte Carlo reference from the same belief, every pre-registered criterion was met. The corrected Clark fold cuts the median error of the mean from linearization's 2.3% to 0.19% and attains the lowest error in the conditional value at risk (CVaR) at the 90% level of every method tested. A benchmark plan costs just 5.5 microseconds on a GPU. These accuracy gains concentrate at contested contacts. While they seldom change which path is chosen on this terrain, the fold still selects the reference-best path in 98% of decisions against linearization's 93 to 96%. On a second dataset the mean transfers, though the risk number does not.

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