Tootfinder

Geometric Uncertainty for Detecting and Correcting Hallucinations in LLMs
Edward Phillips, Sean Wu, Soheila Molaei, Danielle Belgrave, Anshul Thakur, David Clifton
https://arxiv.org/abs/2509.13813

Geometric Uncertainty for Detecting and Correcting Hallucinations in LLMs
Large language models demonstrate impressive results across diverse tasks but are still known to hallucinate, generating linguistically plausible but incorrect answers to questions. Uncertainty quantification has been proposed as a strategy for hallucination detection, but no existing black-box approach provides estimates for both global and local uncertainty. The former attributes uncertainty to a batch of responses, while the latter attributes uncertainty to individual responses. Current loca…

Krugman outlines a constellation of possible causes around a core theme: extreme uncertainty.
“What does a business do in the face of this kind of uncertainty? It tries to avoid making commitments that it may soon regret.”
Krugman tries to tie it all to Trump’s recent actions, which is dubious: the post-college job market has been crap since 2022 or 2023; Krugman’s own graph suggests it started worsening circa 2018. The larger theme of uncertainty, however, fits that timeframe.

Experimental investigation of uncertainty relations for non-Hermitian operators
Xinzhi Zhao, Xinglei Yu, Wenting Zhou, Chengjie Zhang, Jin-Shi Xu, Chuan-Feng Li, Guang-Can Guo
https://arxiv.org/abs/2508.12214

Experimental investigation of uncertainty relations for non-Hermitian operators
Uncertainty relations for Hermitian operators have been confirmed through many experiments. However, previous experiments have only tested the special case of non-Hermitian operators, i.e., uncertainty relations for unitary operators. In this study, we explore uncertainty relations for general non-Hermitian operators, which include Hermitian and unitary operators as special cases. We perform experiments with both real and complex non-Hermitian operators for qubit states, and confirm the validit…

A Conformal Prediction Framework for Uncertainty Quantification in Physics-Informed Neural Networks
Yifan Yu, Cheuk Hin Ho, Yangshuai Wang
https://arxiv.org/abs/2509.13717 https…

A Conformal Prediction Framework for Uncertainty Quantification in Physics-Informed Neural Networks
Physics-Informed Neural Networks (PINNs) have emerged as a powerful framework for solving PDEs, yet existing uncertainty quantification (UQ) approaches for PINNs generally lack rigorous statistical guarantees. In this work, we bridge this gap by introducing a distribution-free conformal prediction (CP) framework for UQ in PINNs. This framework calibrates prediction intervals by constructing nonconformity scores on a calibration set, thereby yielding distribution-free uncertainty estimates with …

The Art of Saying "Maybe": A Conformal Lens for Uncertainty Benchmarking in VLMs
Asif Azad, Mohammad Sadat Hossain, MD Sadik Hossain Shanto, M Saifur Rahman, Md Rizwan Pervez
https://arxiv.org/abs/2509.13379

The Art of Saying "Maybe": A Conformal Lens for Uncertainty Benchmarking in VLMs
Vision-Language Models (VLMs) have achieved remarkable progress in complex visual understanding across scientific and reasoning tasks. While performance benchmarking has advanced our understanding of these capabilities, the critical dimension of uncertainty quantification has received insufficient attention. Therefore, unlike prior conformal prediction studies that focused on limited settings, we conduct a comprehensive uncertainty benchmarking study, evaluating 16 state-of-the-art VLMs (open a…

The Goldilocks zone of governing technology: Leveraging uncertainty for responsible quantum practices
Miriam Meckel, Philipp Hacker, Lea Steinacker, Aurelija Lukoseviciene, Surjo R. Soekadar, Jacob Slosser, Gina-Maria Poehlmann
https://arxiv.org/abs/2507.12957

The Goldilocks zone of governing technology: Leveraging uncertainty for responsible quantum practices
Emerging technologies challenge conventional governance approaches, especially when uncertainty is not a temporary obstacle but a foundational feature as in quantum computing. This paper reframes uncertainty from a governance liability to a generative force, using the paradigms of quantum mechanics to propose adaptive, probabilistic frameworks for responsible innovation. We identify three interdependent layers of uncertainty--physical, technical, and societal--central to the evolution of quantu…

🧠 Excited to share our latest research presented at #CNS2025 in beautiful Firenze, Italy!
"Population decoding of visual motion direction in V1 marmoset monkey: effects of uncertainty"
Our work explores how populations of neurons in the primary visual cortex (V1) of marmoset monkeys encode visual motion direction, with a particular focus on understanding how uncertainty influences…

Population decoding of visual motion direction in V1 marmoset monkey : effects of uncertainty | Next-generation neural computations

Cowboys' uncertainty on offense leads to mediocre win total projection https://www.si.com/nfl/cowboys/news/dallas-cowboys-uncertainty-offense-mediocre-win-total-projection

Mitigating the Impact of Location Uncertainty on Radio Map-Based Predictive Rate Selection via Noisy-Input Gaussian Process
Koya Sato
https://arxiv.org/abs/2509.14710 https://…

Mitigating the Impact of Location Uncertainty on Radio Map-Based Predictive Rate Selection via Noisy-Input Gaussian Process
This paper proposes a predictive rate-selection framework based on Gaussian process (GP)-based radio map construction that is robust to location uncertainty. Radio maps are a promising tool for improving communication efficiency in 6G networks. Although they enable the design of location-based maximum transmission rates by exploiting statistical channel information, existing discussions often assume perfect (i.e., noiseless) location information during channel sensing. Since such information mu…

Day-Ahead Transmission Grid Topology Optimization Considering Renewable Energy Sources' Uncertainty
Giacomo Bastianel, Dirk Van Hertem, Hakan Ergun, Line Roald
https://arxiv.org/abs/2509.13994

Day-Ahead Transmission Grid Topology Optimization Considering Renewable Energy Sources' Uncertainty
The increasing renewable penetration introduces significant uncertainty in power system operations. At the same time, the existing transmission grid is often already congested, and urgently needed reinforcements are frequently delayed due to several constraints. To address these challenges, adjusting the grid topology based on congestion patterns is considered a non-costly remedy to guarantee efficient power transmission. Based on this idea, this paper proposes a grid topology optimization mode…

Acquihire deals like Google's Windsurf deal are a wake-up call for AI startup employees left behind, creating new uncertainty over leadership and compensation (Shirin Ghaffary/Bloomberg)

US officials provide contradictory statements on security guarantees, fueling uncertainty ahead of Trump-Zelensky meeting: https://benborges.xyz/2025/08/17/us-officials-provide-contradictory-statements.html

Physics constrained learning of stochastic characteristics
Pardha Sai Krishna Ala, Ameya Salvi, Venkat Krovi, Matthias Schmid
https://arxiv.org/abs/2507.12661

Physics constrained learning of stochastic characteristics
Accurate state estimation requires careful consideration of uncertainty surrounding the process and measurement models; these characteristics are usually not well-known and need an experienced designer to select the covariance matrices. An error in the selection of covariance matrices could impact the accuracy of the estimation algorithm and may sometimes cause the filter to diverge. Identifying noise characteristics has long been a challenging problem due to uncertainty surrounding noise sourc…

Calibrated and uncertain? Evaluating uncertainty estimates in binary classification models
Aurora Grefsrud, Nello Blaser, Trygve Buanes
https://arxiv.org/abs/2508.11460 https://…

Calibrated and uncertain? Evaluating uncertainty estimates in binary classification models
Rigorous statistical methods, including parameter estimation with accompanying uncertainties, underpin the validity of scientific discovery, especially in the natural sciences. With increasingly complex data models such as deep learning techniques, uncertainty quantification has become exceedingly difficult and a plethora of techniques have been proposed. In this case study, we use the unifying framework of approximate Bayesian inference combined with empirical tests on carefully created synthe…

MEGAN: Mixture of Experts for Robust Uncertainty Estimation in Endoscopy Videos
Damola Agbelese, Krishna Chaitanya, Pushpak Pati, Chaitanya Parmar, Pooya Mobadersany, Shreyas Fadnavis, Lindsey Surace, Shadi Yarandi, Louis R. Ghanem, Molly Lucas, Tommaso Mansi, Oana Gabriela Cula, Pablo F. Damasceno, Kristopher Standish
https://arxiv.org/ab…

MEGAN: Mixture of Experts for Robust Uncertainty Estimation in Endoscopy Videos
Reliable uncertainty quantification (UQ) is essential in medical AI. Evidential Deep Learning (EDL) offers a computationally efficient way to quantify model uncertainty alongside predictions, unlike traditional methods such as Monte Carlo (MC) Dropout and Deep Ensembles (DE). However, all these methods often rely on a single expert's annotations as ground truth for model training, overlooking the inter-rater variability in healthcare. To address this issue, we propose MEGAN, a Multi-Expert Gati…

An Uncertainty-Weighted Decision Transformer for Navigation in Dense, Complex Driving Scenarios
Zhihao Zhang, Chengyang Peng, Minghao Zhu, Ekim Yurtsever, Keith A. Redmill
https://arxiv.org/abs/2509.13132

An Uncertainty-Weighted Decision Transformer for Navigation in Dense, Complex Driving Scenarios
Autonomous driving in dense, dynamic environments requires decision-making systems that can exploit both spatial structure and long-horizon temporal dependencies while remaining robust to uncertainty. This work presents a novel framework that integrates multi-channel bird's-eye-view occupancy grids with transformer-based sequence modeling for tactical driving in complex roundabout scenarios. To address the imbalance between frequent low-risk states and rare safety-critical decisions, we propose…

Systematics from NICER Pulse Profiles Drive Uncertainty in Multi-Messenger Inference of the Neutron Star Equation of State
Bhaskar Biswas, Prasanta Char
https://arxiv.org/abs/2507.12540

Systematics from NICER Pulse Profiles Drive Uncertainty in Multi-Messenger Inference of the Neutron Star Equation of State
We present new constraints on the neutron star equation of state (EOS) and mass distribution using a unified Bayesian inference framework that incorporates latest NICER measurements, including PSR J0614$-$3329, alongside gravitational wave data, radio pulsar masses, and nuclear theory. By systematically comparing four inference scenarios--varying in the inclusion of PSR J0614$-$3329 and in the pulse profile model used for PSR J0030+0451--we quantify the impact of observational and modeling choi…

A Framework for Waterfall Pricing Using Simulation-Based Uncertainty Modeling
Nicola Jean, Giacomo Le Pera, Lorenzo Giada, Claudio Nordio
https://arxiv.org/abs/2507.13324 https:…

A Framework for Waterfall Pricing Using Simulation-Based Uncertainty Modeling
We present a novel framework for pricing waterfall structures by simulating the uncertainty of the cashflow generated by the underlying assets in terms of value, time, and confidence levels. Our approach incorporates various probability distributions calibrated on the market price of the tranches at inception. The framework is fully implemented in PyTorch, leveraging its computational efficiency and automatic differentiation capabilities through Adjoint Algorithmic Differentiation (AAD). This e…

Surrogate-based Bayesian calibration methods for climate models: a comparison of traditional and non-traditional approaches
Maike F. Holthuijzen, Atlanta Chakraborty, Elizabeth Krath, Tommie Catanach
https://arxiv.org/abs/2508.13071

Surrogate-based Bayesian calibration methods for climate models: a comparison of traditional and non-traditional approaches
Parameter calibration is crucial for reducing uncertainty and improving simulation accuracy in physics-based models, yet computational constraints pose significant challenges. Bayesian calibration methods offer a principled framework for combining prior knowledge with data while rigorously quantifying uncertainty. In this work, we compare four emulator-based Bayesian calibration methods: Calibrate-Emulate-Sample (CES), History Matching (HM), Bayesian Optimal Experimental Design (BOED), and a no…

Network representations reveal structured uncertainty in music
Lluc Bono Rossell\'o, Robert Jankowski, Hugues Bersini, Mari\'an Bogu\~n\'a, M. \'Angeles Serrano
https://arxiv.org/abs/2509.14053

Network representations reveal structured uncertainty in music
Music, as a structured yet perceptually rich experience, can be modeled as a network to uncover how humans encode and process auditory information. While network-based representations of music are increasingly common, the impact of feature selection on structural properties and cognitive alignment remains underexplored. In this study, we evaluated eight network models, each constructed from symbolic representations of piano compositions using distinct combinations of pitch, octave, duration, an…

🌍 Satellite data reveal African grasslands' carbon uptake rises with rainfall, unlike forests and savannas
https://phys.org/news/2025-07-satellite-reveal-african-grasslands-carbon.html

Satellite data reveal African grasslands' carbon uptake rises with rainfall, unlike forests and savannas
Africa is a source of uncertainty in carbon cycle calculations. By some estimates, the continent's landscapes emit 2.1 billion tons more carbon dioxide than they take up each year—about equal to 1.5 times the annual emissions from coal-fired power plants. But other estimates are almost the complete opposite, suggesting that the continent's copious plant matter takes up 2.0 billion more tons of carbon dioxide per year than it releases.

Uncertainty and entropies of classical channels
Takla Nateeboon
https://arxiv.org/abs/2507.12310 https://arxiv.org/pdf/2507.12310

Uncertainty and entropies of classical channels
In this thesis, I studied a mathematical development to define and quantify the uncertainty inherent in classical channels. This thesis starts with the introduction and background on how to formally think about uncertainty in the domain of classical states. The concept of probability vector majorization and its variants, relative majorization and conditional majorization, are reviewed. This thesis introduces three conceptually distinct approaches to formalize the notion of uncertainty inherent …

The role of angular slit number and angular slit width on the OAM spectrum
Jayson P. Cabanilla, Nathaniel Hermosa
https://arxiv.org/abs/2509.13829 https://…

The role of angular slit number and angular slit width on the OAM spectrum
The uncertainty principle sets the limit for simultaneous measurements of position and momentum, and its angular analogue is realized through angular diffraction. When a beam is spatially confined by angular slits, the uncertainty in its orbital angular momentum (OAM) increases, leading to the generation of OAM sidebands. Both the angular slit number and the angular slit width shapes the spatial confinement of the beam. In this study, we investigate this dependence of the OAM sidebands by obstr…

Commanders' Robinson to sit amid uncertainty https://www.espn.com/nfl/story/_/id/46009690/commanders-rb-brian-robinson-jr-sit-amid-uncertain-future

Enhancing Dual Network Based Semi-Supervised Medical Image Segmentation with Uncertainty-Guided Pseudo-Labeling
Yunyao Lu, Yihang Wu, Ahmad Chaddad, Tareef Daqqaq, Reem Kateb
https://arxiv.org/abs/2509.13084

Enhancing Dual Network Based Semi-Supervised Medical Image Segmentation with Uncertainty-Guided Pseudo-Labeling
Despite the remarkable performance of supervised medical image segmentation models, relying on a large amount of labeled data is impractical in real-world situations. Semi-supervised learning approaches aim to alleviate this challenge using unlabeled data through pseudo-label generation. Yet, existing semi-supervised segmentation methods still suffer from noisy pseudo-labels and insufficient supervision within the feature space. To solve these challenges, this paper proposes a novel semi-superv…

Signal and Noise: A Framework for Reducing Uncertainty in Language Model Evaluation
David Heineman, Valentin Hofmann, Ian Magnusson, Yuling Gu, Noah A. Smith, Hannaneh Hajishirzi, Kyle Lo, Jesse Dodge
https://arxiv.org/abs/2508.13144

Signal and Noise: A Framework for Reducing Uncertainty in Language Model Evaluation
Developing large language models is expensive and involves making decisions with small experiments, typically by evaluating on large, multi-task evaluation suites. In this work, we analyze specific properties which make a benchmark more reliable for such decisions, and interventions to design higher-quality evaluation benchmarks. We introduce two key metrics that show differences in current benchmarks: signal, a benchmark's ability to separate better models from worse models, and noise, a bench…

Series A, Episode 05 - The Web
CALLY: You're very practical.
[Decimas are exploring the area immediately outside the laboratory. Inside, Geela is making adjustments to the console.]
https://blake.torpidity.net/m/105/394 B7B5

When is it (im)possible to respect all individuals' preferences under uncertainty?
Kensei Nakamura
https://arxiv.org/abs/2508.12542 https://arxiv.org/p…

When is it (im)possible to respect all individuals' preferences under uncertainty?
When aggregating Subjective Expected Utility preferences, the Pareto principle leads to an impossibility result unless the individuals have a common belief. This paper examines the source of this impossibility in more detail by considering the aggregation of a general class of incomplete preferences that can represent gradual ambiguity perceptions. Our result shows that the planner cannot avoid ignoring some individuals unless there is a probability distribution that all individuals agree is mo…

Bayesian Parameter Inference and Uncertainty-Informed Sensitivity Analysis in a 0D Cardiovascular Model for Intraoperative Hypotension
Jan-Niklas Thiel, Marko Zlicar, Ulrich Steinseifer, Borut Kirn, Michael Neidlin
https://arxiv.org/abs/2509.14354

Bayesian Parameter Inference and Uncertainty-Informed Sensitivity Analysis in a 0D Cardiovascular Model for Intraoperative Hypotension
Computational cardiovascular models are promising tools for clinical decision support, particularly in complex conditions, such as intraoperative hypotension (IOH). IOH arises from different mechanisms, making treatment selection non-trivial. Patient-specific predictions require calibration, typically performed using deterministic approaches prone to parameter non-identifiability and lacking uncertainty quantification, hindering clinical translation. Consequently, Bayesian approaches are needed…

Forecasting Climate Policy Uncertainty: Evidence from the United States
Donia Besher, Anirban Sengupta, Tanujit Chakraborty
https://arxiv.org/abs/2507.12276 https://

Forecasting Climate Policy Uncertainty: Evidence from the United States
Forecasting Climate Policy Uncertainty (CPU) is essential as policymakers strive to balance economic growth with environmental goals. High levels of CPU can slow down investments in green technologies, make regulatory planning more difficult, and increase public resistance to climate reforms, especially during times of economic stress. This study addresses the challenge of forecasting the US CPU index by building the Bayesian Structural Time Series (BSTS) model with a large set of covariates, i…

Quantum nature of gravity in self-bound quantum droplets
Asma Tahar Taiba, Abdelaali Boudjemaa
https://arxiv.org/abs/2508.12838 https://arxiv.org/pdf/2508.…

Quantum nature of gravity in self-bound quantum droplets
We explore the possibility of testing the quantum nature of the gravitational field with an ultracold self-bound quantum droplet of one-dimensional Bose-Bose mixtures. To this end, we solve variationally and numerically the underlying generalized Gross-Pitaevskii equation which includes the effects of quadratic and cubic nonlinearities. We derive the associated generalized uncertainty principle and its corresponding minimal length. The obtained modified uncertainty relation enables us to search…

Improvements on uncertainty quantification with variational autoencoders
Andrea Tonini, Tan Bui-Thanh, Francesco Regazzoni, Luca Dede', Alfio Quarteroni
https://arxiv.org/abs/2509.11174

Improvements on uncertainty quantification with variational autoencoders
Inverse problems aim to determine model parameters of a mathematical problem from given observational data. Neural networks can provide an efficient tool to solve these problems. In the context of Bayesian inverse problems, Uncertainty Quantification Variational AutoEncoders (UQ-VAE), a class of neural networks, approximate the posterior distribution mean and covariance of model parameters. This allows for both the estimation of the parameters and their uncertainty in relation to the observatio…

The Time-Energy Principle in Algebraic Geometry
Renaud Gauthier
https://arxiv.org/abs/2507.13134 https://arxiv.org/pdf/2507.13134

The Time-Energy Principle in Algebraic Geometry
We consider the time-energy uncertainty principle from Quantum Mechanics and provide its Algebro-Geometric interpretation within the context of stacks.

AI That Helps Us Help Each Other: A Proactive System for Scaffolding Mentor-Novice Collaboration in Entrepreneurship Coaching
Evey Jiaxin Huang, Matthew Easterday, Elizabeth Gerber
https://arxiv.org/abs/2508.11052

AI That Helps Us Help Each Other: A Proactive System for Scaffolding Mentor-Novice Collaboration in Entrepreneurship Coaching
Entrepreneurship requires navigating open-ended, ill-defined problems: identifying risks, challenging assumptions, and making strategic decisions under deep uncertainty. Novice founders often struggle with these metacognitive demands, while mentors face limited time and visibility to provide tailored support. We present a human-AI coaching system that combines a domain-specific cognitive model of entrepreneurial risk with a large language model (LLM) to proactively scaffold both novice and ment…

Donald Trump observed a few tents on public land and some garbage under an overpass.
Perturbed by the imagery, he issued a sweeping demand via his social media site, Truth Social:
“The Homeless have to move out, IMMEDIATELY.”
“We will give you places to stay, but FAR from the Capital,” Trump continued in his Sunday post. “Be prepared!”
The command came as he made plans to deploy 800 National Guard members to DC and temporarily federalize the city’s police department, …

DC’s homeless have nowhere to go. Trump might send them to jail.
The president's crackdown is causing fear and uncertainty among the city's unhoused and their advocates.

Impact of Geometric Uncertainty on the Computation of Abdominal Aortic Aneurysm Wall Strain
Saeideh Sekhavat, Mostafa Jamshidian, Adam Wittek, Karol Miller
https://arxiv.org/abs/2509.12550

Impact of Geometric Uncertainty on the Computation of Abdominal Aortic Aneurysm Wall Strain
Abdominal aortic aneurysm (AAA) is a life-threatening condition characterized by permanent enlargement of the aorta, often detected incidentally during imaging for unrelated conditions. Current management relies primarily on aneurysm diameter and growth rate, which may not reliably predict patient-specific rupture risk. Computation of AAA wall stress and strain has the potential to improve individualized risk assessment, but these analyses depend on image-derived geometry, which is subject to s…

{ggdist}: Visualizations of distributions and uncertainty #rstats #ggplot

Replaced article(s) found for cs.DC. https://arxiv.org/list/cs.DC/new
[1/1]:
- AAPA: An Archetype-Aware Predictive Autoscaler with Uncertainty Quantification for Serverless Wor...
Zhang, Vippagunta, Nandagopal, Raman, Xu, Pfeiffer, Chatterjee, Tan, Guo, Jiang

Imagine that a cease-fire would be implemented along current frontlines and it would hold for several years, Ukraine would become a EU member, economic integration with the EU would follow which would be beneficial to the union as a whole. Putin can not allow that to happen, especially because he can never be so succesful with his own failed state Russia (economically at least).
Putin prefers uncertainty, instability for Ukraine.

Loss-aware distributionally robust optimization via trainable optimal transport ambiguity sets
Jonas Ohnemus, Marta Fochesato, Riccardo Zuliani, John Lygeros
https://arxiv.org/abs/2509.12689

Loss-aware distributionally robust optimization via trainable optimal transport ambiguity sets
Optimal-Transport Distributionally Robust Optimization (OT-DRO) robustifies data-driven decision-making under uncertainty by capturing the sampling-induced statistical error via optimal transport ambiguity sets. The standard OT-DRO pipeline consists of a two-step procedure, where the ambiguity set is first designed and subsequently embedded into the downstream OT-DRO problem. However, this separation between uncertainty quantification and optimization might result in excessive conservatism. We …

Robust Beamforming Design for Secure Near-Field ISAC Systems
Ziqiang CHen, Feng Wang, Guojun Han, Xin Wang, Vincent K. N. Lau
https://arxiv.org/abs/2507.12881

Robust Beamforming Design for Secure Near-Field ISAC Systems
This letter investigates the robust beamforming design for a near-field secure integrated sensing and communication (ISAC) system with multiple communication users (CUs) and targets, as well as multiple eavesdroppers. Taking into account the channel uncertainty constraints, we maximize the minimum sensing beampattern gain for targets, subject to the minimum signal-to-interference-plus-noise ratio (SINR) constraint for each CU and the maximum SINR constraint for each eavesdropper, as well as the…

Safety filtering of robotic manipulation under environment uncertainty: a computational approach
Anna Johansson, Daniel Lindmark, Viktor Wiberg, Martin Servin
https://arxiv.org/abs/2509.12674

Safety filtering of robotic manipulation under environment uncertainty: a computational approach
Robotic manipulation in dynamic and unstructured environments requires safety mechanisms that exploit what is known and what is uncertain about the world. Existing safety filters often assume full observability, limiting their applicability in real-world tasks. We propose a physics-based safety filtering scheme that leverages high-fidelity simulation to assess control policies under uncertainty in world parameters. The method combines dense rollout with nominal parameters and parallelizable spa…

Online Bayesian Risk-Averse Reinforcement Learning
Yuhao Wang, Enlu Zhou
https://arxiv.org/abs/2509.14077 https://arxiv.org/pdf/2509.14077

Online Bayesian Risk-Averse Reinforcement Learning
In this paper, we study the Bayesian risk-averse formulation in reinforcement learning (RL). To address the epistemic uncertainty due to a lack of data, we adopt the Bayesian Risk Markov Decision Process (BRMDP) to account for the parameter uncertainty of the unknown underlying model. We derive the asymptotic normality that characterizes the difference between the Bayesian risk value function and the original value function under the true unknown distribution. The results indicate that the Baye…

Inertia Tames Fluctuations in Autonomous Stationary Heat Engines
Enrique P. Cital, Viktor Holubec
https://arxiv.org/abs/2508.12936 https://arxiv.org/pdf/25…

Inertia Tames Fluctuations in Autonomous Stationary Heat Engines
Thermodynamic uncertainty relations (TURs) provide fundamental constraints on the interplay between power fluctuations, entropy production, and efficiency in overdamped stationary autonomous heat engines. However, their validity in underdamped regimes remains limited and less explored. Here, we analytically and numerically study a physically realizable autonomous heat engine composed of two underdamped continuous degrees of freedom coupled to a two-level system. We show that this nonlinear setu…

Cross-World Assumption and Refining Prediction Intervals for Individual Treatment Effects
Juraj Bodik, Yaxuan Huang, Bin Yu
https://arxiv.org/abs/2507.12581

Cross-World Assumption and Refining Prediction Intervals for Individual Treatment Effects
While average treatment effects (ATE) and conditional average treatment effects (CATE) provide valuable population- and subgroup-level summaries, they fail to capture uncertainty at the individual level. For high-stakes decision-making, individual treatment effect (ITE) estimates must be accompanied by valid prediction intervals that reflect heterogeneity and unit-specific uncertainty. However, the fundamental unidentifiability of ITEs limits the ability to derive precise and reliable individua…

Cooling of quark stars from perturbative QCD
\'Ursula Fonseca, Eduardo S. Fraga
https://arxiv.org/abs/2508.11056 https://arxiv.org/pdf/2508.11056

Cooling of quark stars from perturbative QCD
We investigate the thermal evolution of quark stars with and without a hadronic crust using an equation of state derived from perturbative QCD that incorporates the running of the strong coupling and the strange quark mass. Our analysis reveals that bare quark stars cool too rapidly to match the luminosity data, including those of the coldest observed isolated neutron stars, even when the uncertainty from the renormalization scale is taken into account. In contrast, configurations featuring a h…

Learning, fast and slow: a two-fold algorithm for data-based model adaptation
Laura Boca de Giuli, Alessio La Bella, Riccardo Scattolini
https://arxiv.org/abs/2507.12187

Learning, fast and slow: a two-fold algorithm for data-based model adaptation
This article addresses the challenge of adapting data-based models over time. We propose a novel two-fold modelling architecture designed to correct plant-model mismatch caused by two types of uncertainty. Out-of-domain uncertainty arises when the system operates under conditions not represented in the initial training dataset, while in-domain uncertainty results from real-world variability and flaws in the model structure or training process. To handle out-of-domain uncertainty, a slow learnin…

Unified Framework for Geodesic Dynamics with Conservative, Dissipative, and GUP Effects
Gaurav Bhandari, S. D. Pathak, Harjit Ghotra
https://arxiv.org/abs/2509.14663 https://

Unified Framework for Geodesic Dynamics with Conservative, Dissipative, and GUP Effects
We derive generalized geodesic equations in curved spacetime that include conservative forces, dissipative effects, and quantum-gravity-motivated minimal-length corrections. Conservative interactions are incorporated through external vector potentials, while dissipative dynamics arise from an exponential rescaling of the particle Lagrangian. Quantum-gravity effects are introduced via Generalized Uncertainty Principle (GUP) deformed Poisson brackets in the Hamiltonian framework. We show that fre…

Calibrating a Finite-strain Phase-field Model of Fracture for Bonded Granular Materials with Uncertainty Quantification
Abigail C. Schmid, Erik Jensen, Fabio Di Gioacchino, Pooyan B. Javadzadeh, Nate E. Peterson, C. Gus Becker, Hongbing Lu, Fatemeh Pourahmadian, Amy J. Clarke, Alireza Doostan, Richard A. Regueiro
https://arxiv.org/abs/2509…

Calibrating a Finite-strain Phase-field Model of Fracture for Bonded Granular Materials with Uncertainty Quantification
To study the mechanical behavior of mock high explosives, an experimental and simulation program was developed to calibrate, with quantified uncertainty, a material model of the bonded granular material Idoxuridine and nitroplasticized Estane-5703. This paper reports on the efficacy of such a framework as a generalizable methodology for calibrating material models against experimental data with uncertainty quantification. Additionally, this paper studies the effect of two manufacturing temperat…

Crosslisted article(s) found for cs.NE. https://arxiv.org/list/cs.NE/new
[1/1]:
- Proximity-Based Evidence Retrieval for Uncertainty-Aware Neural Networks
Hassan Gharoun, Mohammad Sadegh Khorshidi, Kasra Ranjbarigderi, Fang Chen, Amir H. Gandomi

Crosslisted article(s) found for cs.NI. https://arxiv.org/list/cs.NI/new
[1/1]:
- Mitigating the Impact of Location Uncertainty on Radio Map-Based Predictive Rate Selection via No...
Koya Sato

Performance Portable Gradient Computations Using Source Transformation
Kim Liegeois, Brian Kelley, Eric Phipps, Sivasankaran Rajamanickam, Vassil Vassilev
https://arxiv.org/abs/2507.13204

Performance Portable Gradient Computations Using Source Transformation
Derivative computation is a key component of optimization, sensitivity analysis, uncertainty quantification, and nonlinear solvers. Automatic differentiation (AD) is a powerful technique for evaluating such derivatives, and in recent years, has been integrated into programming environments such as Jax, PyTorch, and TensorFlow to support derivative computations needed for training of machine learning models, resulting in widespread use of these technologies. The C++ language has become the de fa…

Testing halo models for constraining astrophysical feedback with multi-probe modeling: I. 3D Power spectra and mass fractions
Pranjal R. S., Shivam Pandey, Dhayaa Anbajagane, Elisabeth Krause, Klaus Dolag
https://arxiv.org/abs/2507.13317

Testing halo models for constraining astrophysical feedback with multi-probe modeling: I. 3D Power spectra and mass fractions
Upcoming Stage-IV surveys will deliver measurements of distribution of matter with unprecedented precision, demanding highly accurate theoretical models for cosmological parameter inference. A major source of modeling uncertainty lies in astrophysical processes associated with galaxy formation and evolution, which remain poorly understood. Probes such as the thermal and kinematic Sunyaev-Zel'dovich effects, X-rays, and dispersion measure from fast radio bursts offer a promising avenue for mappi…

Memory as Structured Trajectories: Persistent Homology and Contextual Sheaves
Xin Li
https://arxiv.org/abs/2508.11646 https://arxiv.org/pdf/2508.11646

Memory as Structured Trajectories: Persistent Homology and Contextual Sheaves
We propose a topological framework for memory and inference grounded in the structure of spike-timing dynamics, persistent homology, and the Context-Content Uncertainty Principle (CCUP). Starting from the observation that polychronous neural groups (PNGs) encode reproducible, time-locked spike sequences shaped by axonal delays and synaptic plasticity, we construct spatiotemporal complexes whose temporally consistent transitions define chain complexes over which robust activation cycles emerge. …

Large Wing Model
Howon Lee, Pranay Seshadri, Juergen Rauleder
https://arxiv.org/abs/2508.11827 https://arxiv.org/pdf/2508.11827

Large Wing Model
Developing a generalized aerodynamics prediction machine learning model for finite wings with different airfoil sections is challenging due to the vast parameter space and a relative scarcity of available data. This paper presents the Large Wing Model (LWM), a probabilistic machine learning model designed to predict pressure coefficient ($C_p$) distributions using a small, strictly experimental data set. From its uncertainty-aware $C_p$ predictions, the sectional and total wing lift coefficient…

T3MAL: Test-Time Fast Adaptation for Robust Multi-Scale Information Diffusion Prediction
Wenting Zhu, Chaozhuo Li, Qingpo Yang, Xi Zhang, Philip S. Yu
https://arxiv.org/abs/2507.12880

T3MAL: Test-Time Fast Adaptation for Robust Multi-Scale Information Diffusion Prediction
Information diffusion prediction (IDP) is a pivotal task for understanding how information propagates among users. Most existing methods commonly adhere to a conventional training-test paradigm, where models are pretrained on training data and then directly applied to test samples. However, the success of this paradigm hinges on the assumption that the data are independently and identically distributed, which often fails in practical social networks due to the inherent uncertainty and variabili…

Taming Uncertainty via Automation: Observing, Analyzing, and Optimizing Agentic AI Systems
Dany Moshkovich, Sergey Zeltyn
https://arxiv.org/abs/2507.11277 …

Taming Uncertainty via Automation: Observing, Analyzing, and Optimizing Agentic AI Systems
Large Language Models (LLMs) are increasingly deployed within agentic systems-collections of interacting, LLM-powered agents that execute complex, adaptive workflows using memory, tools, and dynamic planning. While enabling powerful new capabilities, these systems also introduce unique forms of uncertainty stemming from probabilistic reasoning, evolving memory states, and fluid execution paths. Traditional software observability and operations practices fall short in addressing these challenges…

Giants' Russell Wilson seized opportunity to 'answer the call' with 450-yard effort amid QB uncertainty

https://www.cbssports.com/nfl/news/giants-…

Bayesian Modeling and Estimation of Linear Time-Variant Systems using Neural Networks and Gaussian Processes
Yaniv Shulman
https://arxiv.org/abs/2507.12878

Bayesian Modeling and Estimation of Linear Time-Variant Systems using Neural Networks and Gaussian Processes
The identification of Linear Time-Variant (LTV) systems from input-output data is a fundamental yet challenging ill-posed inverse problem. This work introduces a unified Bayesian framework that models the system's impulse response, $h(t, τ)$, as a stochastic process. We decompose the response into a posterior mean and a random fluctuation term, a formulation that provides a principled approach for quantifying uncertainty and naturally defines a new, useful system class we term Linear Time-Inva…

Uncertainty Quantification on State-Based Conflict Detection and Resolution Algorithms
Muhammad Fazlur Rahman, Joost Ellerbroek, Jacco Hoekstra
https://arxiv.org/abs/2509.10899 …

Uncertainty Quantification on State-Based Conflict Detection and Resolution Algorithms
This study investigates how navigation uncertainty affects conflict detection and resolution (CD&R) for uncrewed aircraft in U-space. Position and velocity errors are modelled as zero-mean Gaussian noise consistent with ADS-L accuracy, and propagated through conflict metrics using Monte Carlo and analytical approximations. Under uncertainty, state-based detection becomes probabilistic. The probability of detection depends on both the level of uncertainty and the encounter geometry, and falls be…

Uncertainty in Authorship: Why Perfect AI Detection Is Mathematically Impossible
Aadil Gani Ganie
https://arxiv.org/abs/2509.11915 https://arxiv.org/pdf/25…

Uncertainty in Authorship: Why Perfect AI Detection Is Mathematically Impossible
As large language models (LLMs) become more advanced, it is increasingly difficult to distinguish between human-written and AI-generated text. This paper draws a conceptual parallel between quantum uncertainty and the limits of authorship detection in natural language. We argue that there is a fundamental trade-off: the more confidently one tries to identify whether a text was written by a human or an AI, the more one risks disrupting the text's natural flow and authenticity. This mirrors the t…

UbiQTree: Uncertainty Quantification in XAI with Tree Ensembles
Akshat Dubey, Aleksandar An\v{z}el, Bahar \.Ilgen, Georges Hattab
https://arxiv.org/abs/2508.09639 https://

UbiQTree: Uncertainty Quantification in XAI with Tree Ensembles
Explainable Artificial Intelligence (XAI) techniques, such as SHapley Additive exPlanations (SHAP), have become essential tools for interpreting complex ensemble tree-based models, especially in high-stakes domains such as healthcare analytics. However, SHAP values are usually treated as point estimates, which disregards the inherent and ubiquitous uncertainty in predictive models and data. This uncertainty has two primary sources: aleatoric and epistemic. The aleatoric uncertainty, which refle…

UDON: Uncertainty-weighted Distributed Optimization for Multi-Robot Neural Implicit Mapping under Extreme Communication Constraints
Hongrui Zhao, Xunlan Zhou, Boris Ivanovic, Negar Mehr
https://arxiv.org/abs/2509.12702

UDON: Uncertainty-weighted Distributed Optimization for Multi-Robot Neural Implicit Mapping under Extreme Communication Constraints
Multi-robot mapping with neural implicit representations enables the compact reconstruction of complex environments. However, it demands robustness against communication challenges like packet loss and limited bandwidth. While prior works have introduced various mechanisms to mitigate communication disruptions, performance degradation still occurs under extremely low communication success rates. This paper presents UDON, a real-time multi-agent neural implicit mapping framework that introduces …

Accelerating seismic inversion and uncertainty quantification with efficient high-rank Hessian approximations
Mathew Hu, Nick Alger, Rami Nammour, Omar Ghattas
https://arxiv.org/abs/2507.10804

Accelerating seismic inversion and uncertainty quantification with efficient high-rank Hessian approximations
Efficient high-rank approximations of the Hessian can accelerate seismic full waveform inversion (FWI) and uncertainty quantification (UQ). In FWI, approximations of the inverse of the Hessian may be used as preconditioners for Newton-type or quasi-Newton algorithms, reducing computational costs and improving recovery in deeper subsurface regions. In Bayesian UQ, Hessian approximations enable the construction of Markov chain Monte Carlo (MCMC) proposals that capture the directional scalings of …

"Let’s be honest,
an idea of 30 percent tariff rate is effectively prohibitive to the mutual trade,”
Maros Sefcovic, the European Union’s trade commissioner, told reporters in Brussels on Monday.
“The current uncertainty caused by unjustified tariffs cannot persist indefinitely,” he added.

Companies in Europe Are Frozen as Tariff Uncertainty Drags On
After European Union leaders said they would keep negotiating instead of immediately retaliating against President Trump’s latest threat, businesses remain unable to develop long-term plans.

A Priest, a Rabbi, and an Atheist Walk Into an Error Bar: Religious Meditations on Uncertainty Visualization
Michael Correll, Lane Harrison
https://arxiv.org/abs/2509.08213 http…

A Priest, a Rabbi, and an Atheist Walk Into an Error Bar: Religious Meditations on Uncertainty Visualization
In this provocation, we suggest that much (although not all) current uncertainty visualization simplifies the myriad forms of uncertainty into error bars around an estimate. This apparent simplification into error bars comes only as a result of a vast metaphysics around uncertainty and probability underlying modern statistics. We use examples from religion to present alternative views of uncertainty (metaphysical or otherwise) with the goal of enriching our conception of what kind of uncertaint…

A Probabilistic Approach to Uncertainty Quantification Leveraging 3D Geometry
Rushil Desai, Frederik Warburg, Trevor Darrell, Marissa Ramirez de Chanlatte
https://arxiv.org/abs/2507.06269

A Probabilistic Approach to Uncertainty Quantification Leveraging 3D Geometry
Quantifying uncertainty in neural implicit 3D representations, particularly those utilizing Signed Distance Functions (SDFs), remains a substantial challenge due to computational inefficiencies, scalability issues, and geometric inconsistencies. Existing methods typically neglect direct geometric integration, leading to poorly calibrated uncertainty maps. We introduce BayesSDF, a novel probabilistic framework for uncertainty quantification in neural implicit SDF models, motivated by scientific …

Active Fault Identification and Robust Control for Unknown Bounded Faults via Volume-Based Costs
Annalena Daniels, Johannes Teutsch, Fabian Kleindienst, Marion Leibold, Dirk Wollherr
https://arxiv.org/abs/2508.12010

Active Fault Identification and Robust Control for Unknown Bounded Faults via Volume-Based Costs
This paper proposes a novel framework for active fault diagnosis and parameter estimation in linear systems operating in closed-loop, subject to unknown but bounded faults. The approach integrates set-membership identification with a cost function designed to accelerate fault identification. Informative excitation is achieved by minimizing the size of the parameter uncertainty set, which is approximated using ellipsoidal outer bounds. Combining this formulation with a scheduling parameter enabl…

Conformal Prediction Meets Long-tail Classification
Shuqi Liu, Jianguo Huang, Luke Ong
https://arxiv.org/abs/2508.11345 https://arxiv.org/pdf/2508.11345

Conformal Prediction Meets Long-tail Classification
Conformal Prediction (CP) is a popular method for uncertainty quantification that converts a pretrained model's point prediction into a prediction set, with the set size reflecting the model's confidence. Although existing CP methods are guaranteed to achieve marginal coverage, they often exhibit imbalanced coverage across classes under long-tail label distributions, tending to over cover the head classes at the expense of under covering the remaining tail classes. This under coverage is partic…

Mitigating Strategy Preference Bias in Emotional Support Conversation via Uncertainty Estimations
Yougen Zhou, Qin Chen, Ningning Zhou, Jie Zhou, Xingjiao Wu, Liang He
https://arxiv.org/abs/2509.12661 …

Mitigating Strategy Preference Bias in Emotional Support Conversation via Uncertainty Estimations
Emotional support conversation (ESC) aims to alleviate distress through empathetic dialogue, yet large language models (LLMs) face persistent challenges in delivering effective ESC due to low accuracy in strategy planning. Moreover, there is a considerable preference bias towards specific strategies. Prior methods using fine-tuned strategy planners have shown potential in reducing such bias, while the underlying causes of the preference bias in LLMs have not well been studied. To address these …

Replaced article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[2/3]:
- Uncertainty-Aware Adaptation of Large Language Models for Protein-Protein Interaction Analysis
Jantre, Wang, Park, Chopra, Jeon, Qian, Urban, Yoon

Uncertainty Aware Mapping for Vision-Based Underwater Robots
Abhimanyu Bhowmik, Mohit Singh, Madhushree Sannigrahi, Martin Ludvigsen, Kostas Alexis
https://arxiv.org/abs/2507.10991

Uncertainty Aware Mapping for Vision-Based Underwater Robots
Vision-based underwater robots can be useful in inspecting and exploring confined spaces where traditional sensors and preplanned paths cannot be followed. Sensor noise and situational change can cause significant uncertainty in environmental representation. Thus, this paper explores how to represent mapping inconsistency in vision-based sensing and incorporate depth estimation confidence into the mapping framework. The scene depth and the confidence are estimated using the RAFT-Stereo model an…

Unified Conformalized Multiple Testing with Full Data Efficiency
Yuyang Huo, Xiaoyang Wu, Changliang Zou, Haojie Ren
https://arxiv.org/abs/2508.12085 https://

Unified Conformalized Multiple Testing with Full Data Efficiency
Conformalized multiple testing offers a model-free way to control predictive uncertainty in decision-making. Existing methods typically use only part of the available data to build score functions tailored to specific settings. We propose a unified framework that puts data utilization at the center: it uses all available data-null, alternative, and unlabeled-to construct scores and calibrate p-values through a full permutation strategy. This unified use of all available data significantly impro…

Privacy-Preserving Uncertainty Disclosure for Facilitating Enhanced Energy Storage Dispatch
Ning Qi, Xiaolong Jin, Kai Hou, Zeyu Liu, Hongjie Jia, Wei Wei
https://arxiv.org/abs/2509.11022

Privacy-Preserving Uncertainty Disclosure for Facilitating Enhanced Energy Storage Dispatch
This paper proposes a novel privacy-preserving uncertainty disclosure framework, enabling system operators to release marginal value function bounds to reduce the conservativeness of interval forecast and mitigate excessive withholding, thereby enhancing storage dispatch and social welfare. We propose a risk-averse analytical storage arbitrage model based on stochastic dynamic programming and explicitly account for uncertainty intervals in value function training. We derive real-time marginal v…

On the Derivation of Equations of Motion from Symmetries in Quantum-Mechanical Systems via Heisenberg's Uncertainty
Enrique Casanova, Jos\'e Rojas, Melvin Arias
https://arxiv.org/abs/2508.10661

On the Derivation of Equations of Motion from Symmetries in Quantum-Mechanical Systems via Heisenberg's Uncertainty
We propose the construction of equations of motion based on symmetries in quantum-mechanical systems, using Heisenberg's uncertainty principle as a minimal foundation. From canonical operators, two spaces of conjugate operators are constructed, along with a third space derived from the former, which includes the ``Symmetry-Dilation'' operator. When this operator commutes with the main equation of motion, it defines the set of observables compatible with a complete basis of operators (symmetry g…

CLEAR: Calibrated Learning for Epistemic and Aleatoric Risk
Ilia Azizi, Juraj Bodik, Jakob Heiss, Bin Yu
https://arxiv.org/abs/2507.08150 https://

CLEAR: Calibrated Learning for Epistemic and Aleatoric Risk
Accurate uncertainty quantification is critical for reliable predictive modeling, especially in regression tasks. Existing methods typically address either aleatoric uncertainty from measurement noise or epistemic uncertainty from limited data, but not necessarily both in a balanced way. We propose CLEAR, a calibration method with two distinct parameters, $γ_1$ and $γ_2$, to combine the two uncertainty components for improved conditional coverage. CLEAR is compatible with any pair of aleatori…

Comparison of three random field sampling methods for high-resolution Bayesian inversion with application to a plane stress problem
Pieter Vanmechelen, Geert Lombaert, Giovanni Samaey
https://arxiv.org/abs/2508.12876

Comparison of three random field sampling methods for high-resolution Bayesian inversion with application to a plane stress problem
Bayesian inversion aims to provide uncertainty quantification of posterior estimates conditional on observations. When the inferred parameter is a continuous spatial function, one common strategy is to model it as a random field. Several random field sampling strategies exist. In this article, we investigate three different approaches in terms of numerical efficiency and influence on the posterior distribution. These approaches are based on (i) Karhunen-Loeve and (ii) wavelet expansions, and (i…

NAMOUnc: Navigation Among Movable Obstacles with Decision Making on Uncertainty Interval
Kai Zhang, Eric Lucet, Julien Alexandre Dit Sandretto, Shoubin Chen, David Filait
https://arxiv.org/abs/2509.12723

NAMOUnc: Navigation Among Movable Obstacles with Decision Making on Uncertainty Interval
Navigation among movable obstacles (NAMO) is a critical task in robotics, often challenged by real-world uncertainties such as observation noise, model approximations, action failures, and partial observability. Existing solutions frequently assume ideal conditions, leading to suboptimal or risky decisions. This paper introduces NAMOUnc, a novel framework designed to address these uncertainties by integrating them into the decision-making process. We first estimate them and compare the correspo…

Who to Trust? Aggregating Client Knowledge in Logit-Based Federated Learning
Viktor Kovalchuk, Nikita Kotelevskii, Maxim Panov, Samuel Horv\'ath, Martin Tak\'a\v{c}
https://arxiv.org/abs/2509.15147

Who to Trust? Aggregating Client Knowledge in Logit-Based Federated Learning
Federated learning (FL) usually shares model weights or gradients, which is costly for large models. Logit-based FL reduces this cost by sharing only logits computed on a public proxy dataset. However, aggregating information from heterogeneous clients is still challenging. This paper studies this problem, introduces and compares three logit aggregation methods: simple averaging, uncertainty-weighted averaging, and a learned meta-aggregator. Evaluated on MNIST and CIFAR-10, these methods reduce…

Replaced article(s) found for cs.CL. https://arxiv.org/list/cs.CL/new
[2/2]:
- Uncertainty-Aware Adaptation of Large Language Models for Protein-Protein Interaction Analysis
Jantre, Wang, Park, Chopra, Jeon, Qian, Urban, Yoon

PC-Sampler: Position-Aware Calibration of Decoding Bias in Masked Diffusion Models
Pengcheng Huang, Shuhao Liu, Zhenghao Liu, Yukun Yan, Shuo Wang, Zulong Chen, Tong Xiao
https://arxiv.org/abs/2508.13021

PC-Sampler: Position-Aware Calibration of Decoding Bias in Masked Diffusion Models
Recent advances in masked diffusion models (MDMs) have established them as powerful non-autoregressive alternatives for sequence generation. Nevertheless, our preliminary experiments reveal that the generation quality of MDMs is still highly sensitive to the choice of decoding strategy. In particular, widely adopted uncertainty-based samplers suffer from two key limitations: a lack of global trajectory control and a pronounced bias toward trivial tokens in the early stages of decoding. These sh…

Differentially Private Conformal Prediction via Quantile Binary Search
Ogonnaya M. Romanus, Roberto Molinari
https://arxiv.org/abs/2507.12497 https://

Differentially Private Conformal Prediction via Quantile Binary Search
Most Differentially Private (DP) approaches focus on limiting privacy leakage from learners based on the data that they are trained on, there are fewer approaches that consider leakage when procedures involve a calibration dataset which is common in uncertainty quantification methods such as Conformal Prediction (CP). Since there is a limited amount of approaches in this direction, in this work we deliver a general DP approach for CP that we call Private Conformity via Quantile Search (P-COQS).…

Joint space-time wind field data extrapolation and uncertainty quantification using nonparametric Bayesian dictionary learning
George D. Pasparakis, Ioannis A. Kougioumtzoglou, Michael D. Shields
https://arxiv.org/abs/2507.11385

Joint space-time wind field data extrapolation and uncertainty quantification using nonparametric Bayesian dictionary learning
A methodology is developed, based on nonparametric Bayesian dictionary learning, for joint space-time wind field data extrapolation and estimation of related statistics by relying on limited/incomplete measurements. Specifically, utilizing sparse/incomplete measured data, a time-dependent optimization problem is formulated for determining the expansion coefficients of an associated low-dimensional representation of the stochastic wind field. Compared to an alternative, standard, compressive sam…

Soft Regrasping Tool Inspired by Jamming Gripper
Takuya Kiyokawa, Zhengtao Hu, Weiwei Wan, Kensuke Harada
https://arxiv.org/abs/2509.13815 https://arxiv.or…

Soft Regrasping Tool Inspired by Jamming Gripper
Regrasping on fixtures is a promising approach to reduce pose uncertainty in robotic assembly, but conventional rigid fixtures lack adaptability and require dedicated designs for each part. To overcome this limitation, we propose a soft jig inspired by the jamming transition phenomenon, which can be continuously deformed to accommodate diverse object geometries. By pressing a triangular-pyramid-shaped tool into the membrane and evacuating the enclosed air, a stable cavity is formed as a placeme…

The Bayesian SIAC filter
Jan Glaubitz, Tongtong Li, Jennifer Ryan, Roman Stuhlmacher
https://arxiv.org/abs/2509.14771 https://arxiv.org/pdf/2509.14771

The Bayesian SIAC filter
We propose the Bayesian smoothness-increasing accuracy-conserving (SIAC) filter -- a hierarchical Bayesian extension of the existing deterministic SIAC filter. The SIAC filter is a powerful numerical tool for removing high-frequency noise from data or numerical solutions without degrading accuracy. However, current SIAC methodology is limited to (i) nodal data (direct, typically noisy function values) and (ii) deterministic point estimates that do not account for uncertainty propagation from in…

Uncertainty-Aware Tabular Prediction: Evaluating VBLL-Enhanced TabPFN in Safety-Critical Medical Data
Madhushan Ramalingam
https://arxiv.org/abs/2509.10048 https://

Uncertainty-Aware Tabular Prediction: Evaluating VBLL-Enhanced TabPFN in Safety-Critical Medical Data
Predictive models are being increasingly used across a wide range of domains, including safety-critical applications such as medical diagnosis and criminal justice. Reliable uncertainty estimation is a crucial task in such settings. Tabular Prior-data Fitted Network (TabPFN) is a recently proposed machine learning foundation model for tabular dataset, which uses a generative transformer architecture. Variational Bayesian Last Layers (VBLL) is a state-of-the-art lightweight variational formulati…

A Risk-Aware Adaptive Robust MPC with Learned Uncertainty Quantification
Mingcong Li
https://arxiv.org/abs/2507.11420 https://arxiv.o…

A Risk-Aware Adaptive Robust MPC with Learned Uncertainty Quantification
Solving chance-constrained optimal control problems for systems subject to non-stationary uncertainties is a significant challenge.Conventional robust model predictive control (MPC) often yields excessive conservatism by relying on static worst-case assumptions, while standard stochastic MPC methods struggle when underlying uncertainty distributions are unknown a priori.This article presents a Risk-Aware Adaptive Robust MPC (RAAR-MPC) framework,a hierarchical architecture that systematically or…

Extending the Entropic Potential of Events for Uncertainty Quantification and Decision-Making in Artificial Intelligence
Mark Zilberman
https://arxiv.org/abs/2508.10241 https://…

Extending the Entropic Potential of Events for Uncertainty Quantification and Decision-Making in Artificial Intelligence
This work demonstrates how the concept of the entropic potential of events -- a parameter quantifying the influence of discrete events on the expected future entropy of a system -- can enhance uncertainty quantification, decision-making, and interpretability in artificial intelligence (AI). Building on its original formulation in physics, the framework is adapted for AI by introducing an event-centric measure that captures how actions, observations, or other discrete occurrences impact uncertai…

E2-BKI: Evidential Ellipsoidal Bayesian Kernel Inference for Uncertainty-aware Gaussian Semantic Mapping
Junyoung Kim, Minsik Jeon, Jihong Min, Kiho Kwak, Junwon Seo
https://arxiv.org/abs/2509.11964

E2-BKI: Evidential Ellipsoidal Bayesian Kernel Inference for Uncertainty-aware Gaussian Semantic Mapping
Semantic mapping aims to construct a 3D semantic representation of the environment, providing essential knowledge for robots operating in complex outdoor settings. While Bayesian Kernel Inference (BKI) addresses discontinuities of map inference from sparse sensor data, existing semantic mapping methods suffer from various sources of uncertainties in challenging outdoor environments. To address these issues, we propose an uncertainty-aware semantic mapping framework that handles multiple sources…

A Systematic Particle Filter for Estimating Time-Varying Parameters in Advection-Diffusion Equations with Source Terms
Andrea Arnold
https://arxiv.org/abs/2508.12155 https://

A Systematic Particle Filter for Estimating Time-Varying Parameters in Advection-Diffusion Equations with Source Terms
Many real-world systems modeled using partial differential equations (PDEs) involve unknown parameters that must be estimated from limited, noisy system observations. While typically assumed to be constants, some of these unobserved parameters may vary with time. This work proposes a two-phase, offline-online numerical procedure for systematically estimating and quantifying uncertainty in time-varying parameters (TVPs) in time-dependent PDEs, specifically focusing on advection-diffusion models …

Uncertainty-Informed Scheduling of Decision Points for Intelligent Mobile Health Interventions
Asim H. Gazi, Bhanu T. Gullapalli, Daiqi Gao, Benjamin M. Marlin, Vivek Shetty, Susan A. Murphy
https://arxiv.org/abs/2507.10798

Uncertainty-Informed Scheduling of Decision Points for Intelligent Mobile Health Interventions
Timely decision making is critical to the effectiveness of mobile health (mHealth) interventions. At predefined timepoints called "decision points," intelligent mHealth systems such as just-in-time adaptive interventions (JITAIs) estimate an individual's biobehavioral context from sensor or survey data and determine whether and how to intervene. For interventions targeting habitual behavior (e.g., oral hygiene), effectiveness often hinges on delivering support shortly before the target behavior…

Uncertainty Estimation using Variance-Gated Distributions
H. Martin Gillis, Isaac Xu, Thomas Trappenberg
https://arxiv.org/abs/2509.08846 https://arxiv.org…

Uncertainty Estimation using Variance-Gated Distributions
Evaluation of per-sample uncertainty quantification from neural networks is essential for decision-making involving high-risk applications. A common approach is to use the predictive distribution from Bayesian or approximation models and decompose the corresponding predictive uncertainty into epistemic (model-related) and aleatoric (data-related) components. However, additive decomposition has recently been questioned. In this work, we propose an intuitive framework for uncertainty estimation a…

Managing Risks from Large Digital Loads Using Coordinated Grid-Forming Storage Network
Soumya Kundu, Kaustav Chatterjee, Ramij R. Hossain, Sai Pushpak Nandanoori, Veronica Adetola
https://arxiv.org/abs/2508.11080

Managing Risks from Large Digital Loads Using Coordinated Grid-Forming Storage Network
Anticipated rapid growth of large digital load, driven by artificial intelligence (AI) data centers, is poised to increase uncertainty and large fluctuations in consumption, threatening the stability, reliability, and security of the energy infrastructure. Conventional measures taken by grid planners and operators to ensure stable and reliable integration of new resources are either cost-prohibitive (e.g., transmission upgrades) or ill-equipped (e.g., generation control) to resolve the unique c…

SHaRe-RL: Structured, Interactive Reinforcement Learning for Contact-Rich Industrial Assembly Tasks
Jannick Strangh\"oner, Philipp Hartmann, Marco Braun, Sebastian Wrede, Klaus Neumann
https://arxiv.org/abs/2509.13949

SHaRe-RL: Structured, Interactive Reinforcement Learning for Contact-Rich Industrial Assembly Tasks
High-mix low-volume (HMLV) industrial assembly, common in small and medium-sized enterprises (SMEs), requires the same precision, safety, and reliability as high-volume automation while remaining flexible to product variation and environmental uncertainty. Current robotic systems struggle to meet these demands. Manual programming is brittle and costly to adapt, while learning-based methods suffer from poor sample efficiency and unsafe exploration in contact-rich tasks. To address this, we prese…

Adaptive Control with Set-Point Tracking and Linear-like Closed-loop Behavior
Mohamad T. Shahab
https://arxiv.org/abs/2508.12225 https://arxiv.org/pdf/2508…

Adaptive Control with Set-Point Tracking and Linear-like Closed-loop Behavior
In this paper, we consider the problem of set-point tracking for a discrete-time plant with unknown plant parameters belonging to a convex and compact uncertainty set. We carry out parameter estimation for an associated auxiliary plant, and a pole-placement-based control law is employed. We prove that this adaptive controller provides desirable linear-like closed-loop behavior which guarantees a bound consisting of: exponential decay with respect to the initial condition, a linear-like convolut…

GenFlowRL: Shaping Rewards with Generative Object-Centric Flow in Visual Reinforcement Learning
Kelin Yu, Sheng Zhang, Harshit Soora, Furong Huang, Heng Huang, Pratap Tokekar, Ruohan Gao
https://arxiv.org/abs/2508.11049

GenFlowRL: Shaping Rewards with Generative Object-Centric Flow in Visual Reinforcement Learning
Recent advances have shown that video generation models can enhance robot learning by deriving effective robot actions through inverse dynamics. However, these methods heavily depend on the quality of generated data and struggle with fine-grained manipulation due to the lack of environment feedback. While video-based reinforcement learning improves policy robustness, it remains constrained by the uncertainty of video generation and the challenges of collecting large-scale robot datasets for tra…

Online Robust Planning under Model Uncertainty: A Sample-Based Approach
Tamir Shazman, Idan Lev-Yehudi, Ron Benchetit, Vadim Indelman
https://arxiv.org/abs/2509.10162 https://…

Online Robust Planning under Model Uncertainty: A Sample-Based Approach
Online planning in Markov Decision Processes (MDPs) enables agents to make sequential decisions by simulating future trajectories from the current state, making it well-suited for large-scale or dynamic environments. Sample-based methods such as Sparse Sampling and Monte Carlo Tree Search (MCTS) are widely adopted for their ability to approximate optimal actions using a generative model. However, in practical settings, the generative model is often learned from limited data, introducing approxi…

A Robust Controller based on Gaussian Processes for Robotic Manipulators with Unknown Uncertainty
Giulio Giacomuzzo, Mohamed Abdelwahab, Marco Cal\`i, Alberto Dalla Libera, Ruggero Carli
https://arxiv.org/abs/2507.11170

A Robust Controller based on Gaussian Processes for Robotic Manipulators with Unknown Uncertainty
In this paper, we propose a novel learning-based robust feedback linearization strategy to ensure precise trajectory tracking for an important family of Lagrangian systems. We assume a nominal knowledge of the dynamics is given but no a-priori bounds on the model mismatch are available. In our approach, the key ingredient is the adoption of a regression framework based on Gaussian Processes (GPR) to estimate the model mismatch. This estimate is added to the outer loop of a classical feedback li…