Tootfinder

'Medieval raids' — fear, uncertainty in Ukraine's Sumy Oblast border region: https://benborges.xyz/2025/12/24/medieval-raids-fear-uncertainty-in.html

As recently as Thursday, Vladimir Putin told a Kremlin demographic conference that increasing births was “crucial” for Russia.
Putin has launched initiatives to encourage people to have more children -- from free school meals for large families to awarding Soviet-style “hero-mother” medals to women with 10 or more children.
“Many of our grandmothers and great-grandmothers had seven, eight, and even more children,” Putin said in 2023.
“Let’s preserve and revive these wonderful…

Russia's population is getting smaller and older. Putin sees that as a national security threat
Russia has grappled with a shrinking and aging population for decades, with President Vladimir Putin viewing it as a threat to national security. Yet despite efforts like cash incentives and family-friendly policies, the number of deaths now outpaces births. Economic uncertainty, restrictions on immigration and the war in Ukraine are making the crisis worse. Desperate to reverse the trend, Russian authorities are also rolling out a wave of restrictive laws, such as banning "child-free propagand…

Grindr ends talks on a $3.46B take-private deal by shareholders Ray Zage and James Lu, who own 60% of Grindr, citing uncertainty over the deal's financing (Kritika Lamba/Reuters)
https://www.reuters.com/business/grindr-special-commi…

the future is not a countdown — #climateanxiety

Chronos vs Kairos: Rethinking Time, Change, and Leadership in a World of Uncertainty
We’ve been taught to see time as running out — every crisis a countdown. This essay reimagines time through Chronos and Kairos, exploring impermanence, systems thinking, and ethical leadership. The future isn’t a deadline — it’s a field of possibility we can participate in.

Cowboys trade rumor from Ian Rapoport reveals deadline uncertainty https://www.sportingnews.com/us/nfl/dallas-cowboys/news/cowboys-trade-rumor-ian-rapoport-reveals-deadline-uncertainty/1048fcbbe5ce1933bb…

A sheriff, a billionaire, a tinge of scandal. California governor's race packs drama, uncertainty (Michael R. Blood/Associated Press)
https://apnews.com/article/california-governor-newsom-porter-scandal-tom-steyer-39abafa3e201d767b13f3db71c2fd0ed
http://www.memeorandum.com/251123/p20#a251123p20

The size of #3I/ATLAS from non-gravitational acceleration: https://arxiv.org/abs/2512.18341 -> "we find diameters between 820 meters and 1050 meters [...] reliable estimates of the mass loss rate at other stages of the comet's trajectory will substantially reduce the systematic uncertainty in this estimate."

Dynamic reversal of IT-PFC information flow orchestrates visual categorization under perceptual uncertainty https://www.biorxiv.org/content/10.64898/2025.12.17.695044v1 Quite a mouthful to say that "the brain actually reverses its information flow when things get blurr…

Teaching Language Models to Faithfully Express their Uncertainty
Bryan Eikema, Evgenia Ilia, Jos\'e G. C. de Souza, Chrysoula Zerva, Wilker Aziz
https://arxiv.org/abs/2510.12587

Teaching Language Models to Faithfully Express their Uncertainty
Large language models (LLMs) often miscommunicate their uncertainty: repeated queries can produce divergent answers, yet generated responses are typically unhedged or hedged in ways that do not reflect this variability. This conveys unfaithful information about the uncertain state of the LLMs' knowledge, creating a faithfulness gap that affects even strong LLMs. We introduce Faithful Uncertainty Tuning (FUT): a fine-tuning approach that teaches instruction-tuned LLMs to express uncertainty fait…

Pete Carroll faces job uncertainty as Raiders endure 2-12 season https://www.foxsports.com/articles/nfl/pete-carroll-faces-job-uncertainty-as-raiders-endure-212-season

HybridFlow: Quantification of Aleatoric and Epistemic Uncertainty with a Single Hybrid Model
Peter Van Katwyk, Karianne J. Bergen
https://arxiv.org/abs/2510.05054 https://

HybridFlow: Quantification of Aleatoric and Epistemic Uncertainty with a Single Hybrid Model
Uncertainty quantification is critical for ensuring robustness in high-stakes machine learning applications. We introduce HybridFlow, a modular hybrid architecture that unifies the modeling of aleatoric and epistemic uncertainty by combining a Conditional Masked Autoregressive normalizing flow for estimating aleatoric uncertainty with a flexible probabilistic predictor for epistemic uncertainty. The framework supports integration with any probabilistic model class, allowing users to easily adap…

Wow. I've dealt with various toxic personalities in software development, but a good portion of the time those toxic personalities were at least extremely knowledgeable in their (often, very limited) domain.
AI, however, seems to be enabling toxic personalities *who are completely clueless*. Impressive!
https://github…

Uncertainty Matters in Dynamic Gaussian Splatting for Monocular 4D Reconstruction
Fengzhi Guo, Chih-Chuan Hsu, Sihao Ding, Cheng Zhang
https://arxiv.org/abs/2510.12768 https://

Uncertainty Matters in Dynamic Gaussian Splatting for Monocular 4D Reconstruction
Reconstructing dynamic 3D scenes from monocular input is fundamentally under-constrained, with ambiguities arising from occlusion and extreme novel views. While dynamic Gaussian Splatting offers an efficient representation, vanilla models optimize all Gaussian primitives uniformly, ignoring whether they are well or poorly observed. This limitation leads to motion drifts under occlusion and degraded synthesis when extrapolating to unseen views. We argue that uncertainty matters: Gaussians with r…

Any sufficiently advanced disaster preparedness is indistinguishable from revolutionary dual power. This essay is a bit of a transition between the theory I've written earlier, and more concrete plans.
Even though I only touched on my life on the commune, it was hard not to write more. These are such weird spaces, with so much invisible opportunity. But they're also just so unique and special. For all the stress and uncertainty of making sure you stayed on Lorean's (the head priestess), there were also those long summer nights with the whole community (except the old lady) gathered around a fire, talking and drinking. There was almost a child-like play to the whole time.
There were so Fridays I'd come home with a couple of gallons of beer from the real world, folks would bring things from the garden, someone would grill a steak, everyone who didn't cook would clean up, and we'd just hang out and have fun. So many evenings I'd go over to Miles place with a guitar, or with his guitar, and we'd pass it around over a few beers, talking about philosophy, Star Wars, or some book or other. It's hard not to write about the strange magic of that space.
My partner and I bonded over similar experiences, mine on a weird little religious commune in California and theirs as a temporary worker at Omega Institute. Both had exploitation, people on weird power trips, frustrating dynamics, but also a strange magic and freedom. Both were sort of fantasy worlds, but places that let us see through this one, let us imagine something that something else is possible behind the veil.
There are many such veils.
Perhaps it's fitting that this is more meandering, as a good wander can help the transition between lots of hard thinking and lots of hard working.
https://anarchoccultism.org/building-zion/evaluating-options
Editing feedback (especially typos, spelling, grammar) is always welcome, as are questions and even wider structural advice. I've been adding the handles of folks who provide feedback to the intro in a "thank you" section. If you do help and wouldn't like to be added, please let me know.

“Many researchers worry about the long-term effects of throttling the pipeline of trained scientists.
‘If this keeps up, it would be really devastating for the field, bc this is where the next generation of experts comes from,’
says Emily Levesque,
an astronomer at the Univ of WA in Seattle.”
https://

Author Bill Sullivan (@authorbillsullivan.com)
US PhD admissions shrink as fears over Trump’s cuts take hold Some doctoral programmes are admitting no students at all amid uncertainty about federal science funding. https://www.nature.com/articles/d41586-025-03417-6

Here is a sketch of #security issues in a typical #PC-alike #boot flow. 💾
The red squiggly bits are paths of uncertainty. 🤨
Coming from the

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

Revisiting Hallucination Detection with Effective Rank-based Uncertainty
Rui Wang, Zeming Wei, Guanzhang Yue, Meng Sun
https://arxiv.org/abs/2510.08389 https://

Revisiting Hallucination Detection with Effective Rank-based Uncertainty
Detecting hallucinations in large language models (LLMs) remains a fundamental challenge for their trustworthy deployment. Going beyond basic uncertainty-driven hallucination detection frameworks, we propose a simple yet powerful method that quantifies uncertainty by measuring the effective rank of hidden states derived from multiple model outputs and different layers. Grounded in the spectral analysis of representations, our approach provides interpretable insights into the model's internal re…

Shutdown fight fuels anger and uncertainty far from Washington, D.C. (Natasha Korecki/NBC News)
https://www.nbcnews.com/politics/congress/shutdown-fight-fuels-anger-uncertainty-mayors-rcna237996
http://www.memeorandum.com/251017/p26#a251017p26

Smooth Uncertainty Sets: Dependence of Uncertain Parameters via a Simple Polyhedral Set
Noam Goldberg, Michael Poss, Shimrit Shtern
https://arxiv.org/abs/2510.08843 https://

Smooth Uncertainty Sets: Dependence of Uncertain Parameters via a Simple Polyhedral Set
We propose a novel polyhedral uncertainty set for robust optimization, termed the smooth uncertainty set, which captures dependencies of uncertain parameters by constraining their pairwise differences. The bounds on these differences may be dictated by the underlying physics of the problem and may be expressed by domain experts. When correlations are available, the bounds can be set to ensure that the associated probabilistic constraints are satisfied for any given probability. We explore spe…

On Thompson Sampling and Bilateral Uncertainty in Additive Bayesian Optimization
Nathan Wycoff
https://arxiv.org/abs/2510.11792 https://arxiv.org/pdf/2510.…

On Thompson Sampling and Bilateral Uncertainty in Additive Bayesian Optimization
In Bayesian Optimization (BO), additive assumptions can mitigate the twin difficulties of modeling and searching a complex function in high dimension. However, common acquisition functions, like the Additive Lower Confidence Bound, ignore pairwise covariances between dimensions, which we'll call \textit{bilateral uncertainty} (BU), imposing a second layer of approximations. While theoretical results indicate that asymptotically not much is lost in doing so, little is known about the practical e…

Are you more scared of: https://mastodon.social/@euobserver/115564641753988876

Progressive Uncertainty-Guided Evidential U-KAN for Trustworthy Medical Image Segmentation
Zhen Yang, Yansong Ma, Lei Chen
https://arxiv.org/abs/2510.08949 https://

Progressive Uncertainty-Guided Evidential U-KAN for Trustworthy Medical Image Segmentation
Trustworthy medical image segmentation aims at deliver accurate and reliable results for clinical decision-making. Most existing methods adopt the evidence deep learning (EDL) paradigm due to its computational efficiency and theoretical robustness. However, the EDL-based methods often neglect leveraging uncertainty maps rich in attention cues to refine ambiguous boundary segmentation. To address this, we propose a progressive evidence uncertainty guided attention (PEUA) mechanism to guide the m…

A Formal gatekeeper Framework for Safe Dual Control with Active Exploration
Kaleb Ben Naveed, Devansh R. Agrawal, Dimitra Panagou
https://arxiv.org/abs/2510.06351 https://

A Formal gatekeeper Framework for Safe Dual Control with Active Exploration
Planning safe trajectories under model uncertainty is a fundamental challenge. Robust planning ensures safety by considering worst-case realizations, yet ignores uncertainty reduction and leads to overly conservative behavior. Actively reducing uncertainty on-the-fly during a nominal mission defines the dual control problem. Most approaches address this by adding a weighted exploration term to the cost, tuned to trade off the nominal objective and uncertainty reduction, but without formal consi…

Uncertainty Quantification for Retrieval-Augmented Reasoning
Heydar Soudani, Hamed Zamani, Faegheh Hasibi
https://arxiv.org/abs/2510.11483 https://arxiv.or…

Uncertainty Quantification for Retrieval-Augmented Reasoning
Retrieval-augmented reasoning (RAR) is a recent evolution of retrieval-augmented generation (RAG) that employs multiple reasoning steps for retrieval and generation. While effective for some complex queries, RAR remains vulnerable to errors and misleading outputs. Uncertainty quantification (UQ) offers methods to estimate the confidence of systems' outputs. These methods, however, often handle simple queries with no retrieval or single-step retrieval, without properly handling RAR setup. Accura…

PitchBook: VC investment in Asia slowed to $48.9B in the first nine months of 2025, just over half of 2024's total, amid uncertainty due to US tariff policies (Pak Yiu/Nikkei Asia)
https://asia.nikkei.com/business/finance/asian-ven…

Asian venture capital activity slows amid US tariff uncertainty
Investors increasingly cautious as deals shift toward supply chains and manufacturing

Uncertainty-Aware, Risk-Adaptive Access Control for Agentic Systems using an LLM-Judged TBAC Model
Charles Fleming, Ashish Kundu, Ramana Kompella
https://arxiv.org/abs/2510.11414

Uncertainty-Aware, Risk-Adaptive Access Control for Agentic Systems using an LLM-Judged TBAC Model
The proliferation of autonomous AI agents within enterprise environments introduces a critical security challenge: managing access control for emergent, novel tasks for which no predefined policies exist. This paper introduces an advanced security framework that extends the Task-Based Access Control (TBAC) model by using a Large Language Model (LLM) as an autonomous, risk-aware judge. This model makes access control decisions not only based on an agent's intent but also by explicitly considerin…

QB Jackson still out as Ravens return from bye https://www.espn.com/nfl/story/_/id/46663947/lamar-jackson-hamstring-ravens-return-bye

Uncertainty as Feature Gaps: Epistemic Uncertainty Quantification of LLMs in Contextual Question-Answering
Yavuz Bakman, Sungmin Kang, Zhiqi Huang, Duygu Nur Yaldiz, Catarina G. Bel\'em, Chenyang Zhu, Anoop Kumar, Alfy Samuel, Salman Avestimehr, Daben Liu, Sai Praneeth Karimireddy
https://arxiv.org/abs/2510.02671

Uncertainty as Feature Gaps: Epistemic Uncertainty Quantification of LLMs in Contextual Question-Answering
Uncertainty Quantification (UQ) research has primarily focused on closed-book factual question answering (QA), while contextual QA remains unexplored, despite its importance in real-world applications. In this work, we focus on UQ for the contextual QA task and propose a theoretically grounded approach to quantify epistemic uncertainty. We begin by introducing a task-agnostic, token-level uncertainty measure defined as the cross-entropy between the predictive distribution of the given model and…

Stochastic Finite Volume Approximation with Clustering in the Parameter Space for Forward Uncertainty Quantification of PDEs with Random Parameters
Zhao Zhang, Na Ou
https://arxiv.org/abs/2510.12109

Stochastic Finite Volume Approximation with Clustering in the Parameter Space for Forward Uncertainty Quantification of PDEs with Random Parameters
The uncertainty quantification (UQ) for partial differential equations (PDEs) with random parameters is important for science and engineering. Forward UQ quantifies the impact of random parameters on the solution or the quantity-of-interest (QoI). In the current study, we propose a new extension of the stochastic finite volume (SFV) method by clustering samples in the parameter space. Compared to classic SFV based on structured grid in the parameter space, the new scheme based on clustering ext…

MPC strategies for density profile control with pellet fueling in nuclear fusion tokamaks under uncertainty
Christopher A. Orrico, Hari Prasad Varadarajan, Matthijs van Berkel, Lennard Ceelen, Thomas O. S. J. Bosman, W. P. M. H. Heemels, Dinesh Krishnamoorthy
https://arxiv.org/abs/2510.04784

MPC strategies for density profile control with pellet fueling in nuclear fusion tokamaks under uncertainty
Control of the density profile based on pellet fueling for the ITER nuclear fusion tokamak involves a multi-rate nonlinear system with safety-critical constraints, input delays, and discrete actuators with parametric uncertainty. To address this challenging problem, we propose a multi-stage MPC (msMPC) approach to handle uncertainty in the presence of mixed-integer inputs. While the scenario tree of msMPC accounts for uncertainty, it also adds complexity to an already computationally intensive …

Uncertainty in Machine Learning
Hans Weytjens, Wouter Verbeke
https://arxiv.org/abs/2510.06007 https://arxiv.org/pdf/2510.06007

Uncertainty in Machine Learning
This book chapter introduces the principles and practical applications of uncertainty quantification in machine learning. It explains how to identify and distinguish between different types of uncertainty and presents methods for quantifying uncertainty in predictive models, including linear regression, random forests, and neural networks. The chapter also covers conformal prediction as a framework for generating predictions with predefined confidence intervals. Finally, it explores how uncerta…

Nonlinear Heisenberg Limit via Uncertainty Principle in Quantum Metrology
Binke Xia, Jingzheng Huang, Yuxiang Yang, Guihua Zeng
https://arxiv.org/abs/2510.09216 https://

Nonlinear Heisenberg Limit via Uncertainty Principle in Quantum Metrology
The Heisenberg limit is acknowledged as the ultimate precision limit in quantum metrology, traditionally implying that root mean square errors of parameter estimation decrease linearly with the time T of evolution and the number N of quantum gates or probes. However, this conventional perspective fails to interpret recent studies of "super-Heisenberg" scaling, where precision improves faster than linearly with T and N. In this work, we revisit the Heisenberg scaling by leveraging the position-m…

Unique continuation and Hardy's uncertainty principle for hyperbolic Schr\"odinger equations
Torunn Jensen
https://arxiv.org/abs/2510.09202 https://

Unique continuation and Hardy's uncertainty principle for hyperbolic Schrödinger equations
We prove unique continuation properties related to the Hardy uncertainty principle for solutions of the hyperbolic nonlinear Schrödinger equation and the hyperbolic Schrödinger equation with potential. Under suitable conditions on the nonlinearity, or the potential, we show that if $u$ is a solution with Gaussian decay at two different times, then $u\equiv 0$. These results extend to the hyperbolic setting the work of Escauriaza, Kenig, Ponce, and Vega (JEMS, 10, 2008) for the classical Schr�…

Null-Shaping for Interference Mitigation in LEO Satellites Under Location Uncertainty
Fernando Moya Caceres, Akram Al-Hourani, Saman Atapattu, Kandeepan Sithamparanathan
https://arxiv.org/abs/2510.00816

Null-Shaping for Interference Mitigation in LEO Satellites Under Location Uncertainty
Radio frequency interference (RFI) poses a growing challenge to satellite communications, particularly in uplink channels of Low Earth Orbit (LEO) systems, due to increasing spectrum congestion and uncertainty in the location of terrestrial interferers. This paper addresses the impact of RFI source position uncertainty on beamforming-based interference mitigation. First, we analytically characterize how geographic uncertainty in RFI location translates into angular deviation as observed from th…

🐐 Bundled climate solutions for better production: Scientists listen to West African farmers
https://phys.org/news/2025-09-bundled-climate-solutions-production-scientists.html

Bundled climate solutions for better production: Scientists listen to West African farmers
The rain delays, sorghum withers; when a storm finally breaks, complaints rise again. To break this cycle of uncertainty, scientists from the Alliance of Bioversity International and CIAT, INERA, and the University of Ghana conducted the first large-scale survey on agricultural climate services in Burkina Faso, Ghana, Mali, and Senegal.

Muonium HFS Uncertainty Revisited
Michael I. Eides
https://arxiv.org/abs/2510.07281 https://arxiv.org/pdf/2510.07281

Muonium HFS Uncertainty Revisited
Uncertainty of the quantum electrodynamics theoretical prediction for the hyperfine splitting in the ground state of muonium is considered. It is compared with the respective discussion in the two most recent CODATA adjustments of the fundamental physical constants.

The Trump administration on Monday said it would
pause leases for five #wind #farms under construction off the east coast
🔥essentially gutting the country’s nascent offshore wind industry
in a sharp escalation of Trump’s crusade against the renewable energy source.
The decision injected uncertainty into…

Trump Halts 5 Wind Farms Off the East Coast
The Interior Department said the projects posed national security risks, without providing details. The decision imperils billions of dollars of investments.

Design of chemical recycling processes for PUR foam under uncertainty
Patrick Lotz, Luca Bosetti, Andr\'e Bardow, Sergio Lucia, Sebastian Engell
https://arxiv.org/abs/2510.08301

Design of chemical recycling processes for PUR foam under uncertainty
Optimization problems in chemical process design involve a significant number of discrete and continuous decisions. When taking into account uncertainties, the search space is very difficult to explore, even for experienced engineers. Moreover, it should be taken into account that while some decisions are fixed at the design stage, other parameters can be adapted to the realization of the uncertainty during the operation of the plant. This leads to a two-stage optimization problem which is diff…

The vOICe vision BCI can in principle also get around the frequency-time uncertainty principle (Heisenberg uncertainty principle) in sound waves, here by exploiting a priori knowledge of the limited set of frequencies used https://www.seeingwithsound.com/freqtime.htm

EU: All bark, no bite, and everyone knows it. https://mastodon.social/@euobserver/115564641753988876

Uncertainty Quantification for Multi-level Models Using the Survey-Weighted Pseudo-Posterior
Matthew R. Williams, F. Hunter McGuire, Terrance D. Savitsky
https://arxiv.org/abs/2510.09401

Uncertainty Quantification for Multi-level Models Using the Survey-Weighted Pseudo-Posterior
Parameter estimation and inference from complex survey samples typically focuses on global model parameters whose estimators have asymptotic properties, such as from fixed effects regression models. We present a motivating example of Bayesian inference for a multi-level or mixed effects model in which both the local parameters (e.g. group level random effects) and the global parameters may need to be adjusted for the complex sampling design. We evaluate the limitations of the survey-weighted ps…

Uncertainty assessment in satellite-based greenhouse gas emissions estimates using emulated atmospheric transport
Jeffrey N. Clark, Elena Fillola, Nawid Keshtmand, Raul Santos-Rodriguez, Matthew Rigby
https://arxiv.org/abs/2510.05751

Uncertainty assessment in satellite-based greenhouse gas emissions estimates using emulated atmospheric transport
Monitoring greenhouse gas emissions and evaluating national inventories require efficient, scalable, and reliable inference methods. Top-down approaches, combined with recent advances in satellite observations, provide new opportunities to evaluate emissions at continental and global scales. However, transport models used in these methods remain a key source of uncertainty: they are computationally expensive to run at scale, and their uncertainty is difficult to characterise. Artificial intelli…

Optical clocks with accuracy validated at the 19th digit
K. J. Arnold, M. D. K. Lee, Zhao Qi, Qichen Qin, Zhang Zhao, N. Jayjong, M. D. Barrett
https://arxiv.org/abs/2512.07346 https://arxiv.org/pdf/2512.07346 https://arxiv.org/html/2512.07346
arXiv:2512.07346v1 Announce Type: new
Abstract: We report a comprehensive evaluation of all known sources of systematic uncertainty for two independent $^{176}$Lu$^ $ single-ion optical references, finding total systematic uncertainty of $1.1\times10^{-19}$ and $1.4\times10^{-19}$ for the two individual systems and $9.6\times10^{-20}$ for the difference. Through direct comparison via correlation spectroscopy, we demonstrate a relative frequency agreement of $-2.4\pm(5.7)_\mathrm{stat}\pm(1.0)_\mathrm{sys}\times10^{-19}$, where `stat' and `sys' indicate the statistical and systematic uncertainty, respectively. The comparison uncertainty is statistically limited after approximately 200 hours of averaging with a measurement instability of $4.8\times10^{-16}(\tau/\mathrm{s})^{-1/2}$.
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The Shape of Surprise: Structured Uncertainty and Co-Creativity in AI Music Tools
Eric Browne
https://arxiv.org/abs/2509.25028 https://arxiv.org/pdf/2509.2…

The Shape of Surprise: Structured Uncertainty and Co-Creativity in AI Music Tools
Randomness plays a pivotal yet paradoxical role in computational music creativity: it can spark novelty, but unchecked chance risks incoherence. This paper presents a thematic review of contemporary AI music systems, examining how designers incorporate randomness and uncertainty into creative practice. I draw on the concept of structured uncertainty to analyse how stochastic processes are constrained within musical and interactive frameworks. Through a comparative analysis of six systems - Musi…

When Robustness Meets Conservativeness: Conformalized Uncertainty Calibration for Balanced Decision Making
Wenbin Zhou, Shixiang Zhu
https://arxiv.org/abs/2510.07750 https://

When Robustness Meets Conservativeness: Conformalized Uncertainty Calibration for Balanced Decision Making
Robust optimization safeguards decisions against uncertainty by optimizing against worst-case scenarios, yet their effectiveness hinges on a prespecified robustness level that is often chosen ad hoc, leading to either insufficient protection or overly conservative and costly solutions. Recent approaches using conformal prediction construct data-driven uncertainty sets with finite-sample coverage guarantees, but they still fix coverage targets a priori and offer little guidance for selecting rob…

Weekend Reads
* Is this an AI bubble?
https://www.oaktreecapital.com/insights/memo/is-it-a-bubble
* Internet Protocol Journal

Is It a Bubble?
In his latest memo, Howard Marks explores if there is a bubble in AI, identifying uncertainty, parallels to past bubbles, AI's vast potential, and emphasizing the importance of prudence.

Characterizing 5G User Throughput via Uncertainty Modeling and Crowdsourced Measurements
Javier Albert-Smet, Zoraida Frias, Luis Mendo, Sergio Melones, Eduardo Yraola
https://arxiv.org/abs/2510.09239

Characterizing 5G User Throughput via Uncertainty Modeling and Crowdsourced Measurements
Characterizing application-layer user throughput in next-generation networks is increasingly challenging as the higher capacity of the 5G Radio Access Network (RAN) shifts connectivity bottlenecks towards deeper parts of the network. Traditional methods, such as drive tests and operator equipment counters, are costly, limited, or fail to capture end-to-end (E2E) Quality of Service (QoS) and its variability. In this work, we leverage large-scale crowdsourced measurements-including E2E, radio, co…

Multi-agent learning under uncertainty: Recurrence vs. concentration
Kyriakos Lotidis, Panayotis Mertikopoulos, Nicholas Bambos, Jose Blanchet
https://arxiv.org/abs/2512.08132 https://arxiv.org/pdf/2512.08132 https://arxiv.org/html/2512.08132
arXiv:2512.08132v1 Announce Type: new
Abstract: In this paper, we examine the convergence landscape of multi-agent learning under uncertainty. Specifically, we analyze two stochastic models of regularized learning in continuous games -- one in continuous and one in discrete time with the aim of characterizing the long-run behavior of the induced sequence of play. In stark contrast to deterministic, full-information models of learning (or models with a vanishing learning rate), we show that the resulting dynamics do not converge in general. In lieu of this, we ask instead which actions are played more often in the long run, and by how much. We show that, in strongly monotone games, the dynamics of regularized learning may wander away from equilibrium infinitely often, but they always return to its vicinity in finite time (which we estimate), and their long-run distribution is sharply concentrated around a neighborhood thereof. We quantify the degree of this concentration, and we show that these favorable properties may all break down if the underlying game is not strongly monotone -- underscoring in this way the limits of regularized learning in the presence of persistent randomness and uncertainty.
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'Ghost job' postings are adding another layer of uncertainty to the stalling jobs picture (Jeff Cox/CNBC)
https://www.cnbc.com/2025/11/11/ghost-job-postings-add-another-layer-of-uncertainty-to-stalled-jobs-picture.html
http://www.memeorandum.com/251111/p79#a251111p79

Latent Uncertainty Representations for Video-based Driver Action and Intention Recognition
Koen Vellenga, H. Joe Steinhauer, Jonas Andersson, Anders Sj\"ogren
https://arxiv.org/abs/2510.05006

Latent Uncertainty Representations for Video-based Driver Action and Intention Recognition
Deep neural networks (DNNs) are increasingly applied to safety-critical tasks in resource-constrained environments, such as video-based driver action and intention recognition. While last layer probabilistic deep learning (LL-PDL) methods can detect out-of-distribution (OOD) instances, their performance varies. As an alternative to last layer approaches, we propose extending pre-trained DNNs with transformation layers to produce multiple latent representations to estimate the uncertainty. We ev…

Calibrated Uncertainty Sampling for Active Learning
Ha Manh Bui, Iliana Maifeld-Carucci, Anqi Liu
https://arxiv.org/abs/2510.03162 https://arxiv.org/pdf/25…

Calibrated Uncertainty Sampling for Active Learning
We study the problem of actively learning a classifier with a low calibration error. One of the most popular Acquisition Functions (AFs) in pool-based Active Learning (AL) is querying by the model's uncertainty. However, we recognize that an uncalibrated uncertainty model on the unlabeled pool may significantly affect the AF effectiveness, leading to sub-optimal generalization and high calibration error on unseen data. Deep Neural Networks (DNNs) make it even worse as the model uncertainty from…

Trevon Diggs on return to the field, uncertain of future with Cowboys https://www.dallascowboys.com/news/trevon-diggs-on-return-to-the-field-uncertain-of-future-with-cowboys

Towards Reliable LLM-based Robot Planning via Combined Uncertainty Estimation
Shiyuan Yin, Chenjia Bai, Zihao Zhang, Junwei Jin, Xinxin Zhang, Chi Zhang, Xuelong Li
https://arxiv.org/abs/2510.08044

Towards Reliable LLM-based Robot Planning via Combined Uncertainty Estimation
Large language models (LLMs) demonstrate advanced reasoning abilities, enabling robots to understand natural language instructions and generate high-level plans with appropriate grounding. However, LLM hallucinations present a significant challenge, often leading to overconfident yet potentially misaligned or unsafe plans. While researchers have explored uncertainty estimation to improve the reliability of LLM-based planning, existing studies have not sufficiently differentiated between epistem…

Multi-level informed optimization via decomposed Kriging for large design problems under uncertainty
Enrico Ampellio, Blazhe Gjorgiev, Giovanni Sansavini
https://arxiv.org/abs/2510.07904

Multi-level informed optimization via decomposed Kriging for large design problems under uncertainty
Engineering design involves demanding models encompassing many decision variables and uncontrollable parameters. In addition, unavoidable aleatoric and epistemic uncertainties can be very impactful and add further complexity. The state-of-the-art adopts two steps, uncertainty quantification and design optimization, to optimize systems under uncertainty by means of robust or stochastic metrics. However, conventional scenario-based, surrogate-assisted, and mathematical programming methods are not…

Riccati-ZORO: An efficient algorithm for heuristic online optimization of internal feedback laws in robust and stochastic model predictive control
Florian Messerer, Yunfan Gao, Jonathan Frey, Moritz Diehl
https://arxiv.org/abs/2511.10473 https://arxiv.org/pdf/2511.10473 https://arxiv.org/html/2511.10473
arXiv:2511.10473v1 Announce Type: new
Abstract: We present Riccati-ZORO, an algorithm for tube-based optimal control problems (OCP). Tube OCPs predict a tube of trajectories in order to capture predictive uncertainty. The tube induces a constraint tightening via additional backoff terms. This backoff can significantly affect the performance, and thus implicitly defines a cost of uncertainty. Optimizing the feedback law used to predict the tube can significantly reduce the backoffs, but its online computation is challenging.
Riccati-ZORO jointly optimizes the nominal trajectory and uncertainty tube based on a heuristic uncertainty cost design. The algorithm alternates between two subproblems: (i) a nominal OCP with fixed backoffs, (ii) an unconstrained tube OCP, which optimizes the feedback gains for a fixed nominal trajectory. For the tube optimization, we propose a cost function informed by the proximity of the nominal trajectory to constraints, prioritizing reduction of the corresponding backoffs. These ideas are developed in detail for ellipsoidal tubes under linear state feedback. In this case, the decomposition into the two subproblems yields a substantial reduction of the computational complexity with respect to the state dimension from $\mathcal{O}(n_x^6)$ to $\mathcal{O}(n_x^3)$, i.e., the complexity of a nominal OCP.
We investigate the algorithm in numerical experiments, and provide two open-source implementations: a prototyping version in CasADi and a high-performance implementation integrated into the acados OCP solver.
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Towards Label-Free Biological Reasoning Synthetic Dataset Creation via Uncertainty Filtering
Josefa Lia Stoisser, Lawrence Phillips, Aditya Misra, Tom A. Lamb, Philip Torr, Marc Boubnovski Martell, Julien Fauqueur, Kaspar M\"artens
https://arxiv.org/abs/2510.05871

Towards Label-Free Biological Reasoning Synthetic Dataset Creation via Uncertainty Filtering
Synthetic chain-of-thought (CoT) traces are widely used to train large reasoning models (LRMs), improving generalization by providing step-level supervision. Yet most approaches require ground-truth labels to seed or filter these traces - an expensive bottleneck in domains like biology where wet-lab data are scarce. We propose a label-free alternative: uncertainty-based filtering, which uses a model's own confidence - quantified through established uncertainty metrics like self-consistency and …

the future is not a countdown — #climate

Chronos vs Kairos: Rethinking Time, Change, and Leadership in a World of Uncertainty
We’ve been taught to see time as running out — every crisis a countdown. This essay reimagines time through Chronos and Kairos, exploring impermanence, systems thinking, and ethical leadership. The future isn’t a deadline — it’s a field of possibility we can participate in.

The role of entropy production and thermodynamic uncertainty relations in the thermalization of open quantum systems
\'Alvaro Tejero
https://arxiv.org/abs/2510.05072 https:/…

The role of entropy production and thermodynamic uncertainty relations in the thermalization of open quantum systems
The asymmetry between heating and cooling in open quantum systems is a hallmark of nonequilibrium dynamics, yet its thermodynamic origin has remained unclear. Here, we investigate the thermalization of a quantum system weakly coupled to a thermal bath, focusing on the entropy production rate and the quantum thermokinetic uncertainty relation (TKUR). We derive an analytical expression for the entropy production rate, showing that heating begins with a higher entropy production, which drives fast…

Multidimensional Uncertainty Quantification via Optimal Transport
Nikita Kotelevskii, Maiya Goloburda, Vladimir Kondratyev, Alexander Fishkov, Mohsen Guizani, Eric Moulines, Maxim Panov
https://arxiv.org/abs/2509.22380

Multidimensional Uncertainty Quantification via Optimal Transport
Most uncertainty quantification (UQ) approaches provide a single scalar value as a measure of model reliability. However, different uncertainty measures could provide complementary information on the prediction confidence. Even measures targeting the same type of uncertainty (e.g., ensemble-based and density-based measures of epistemic uncertainty) may capture different failure modes. We take a multidimensional view on UQ by stacking complementary UQ measures into a vector. Such vectors are a…

Uncertainty Propagation in Finite Impulse Response Filters: Evaluating the Gaussian Assumption
Jennie Couchman, Phillip Stanley-Marbell
https://arxiv.org/abs/2510.11384 https://…

Uncertainty Propagation in Finite Impulse Response Filters: Evaluating the Gaussian Assumption
A common assumption in signal processing is that underlying data numerically conforms to a Gaussian distribution. It is commonly utilized in signal processing to describe unknown additive noise in a system and is often justified by citing the central limit theorem for sums of random variables, although the central limit theorem applies only to sums of independent identically distributed random variables. However, many linear operations in signal processing take the form of weighted sums, which …

Enhancing Safety in Diabetic Retinopathy Detection: Uncertainty-Aware Deep Learning Models with Rejection Capabilities
Madhushan Ramalingam, Yaish Riaz, Priyanthi Rajamanoharan, Piyumi Dasanayaka
https://arxiv.org/abs/2510.00029

Enhancing Safety in Diabetic Retinopathy Detection: Uncertainty-Aware Deep Learning Models with Rejection Capabilities
Diabetic retinopathy (DR) is a major cause of visual impairment, and effective treatment options depend heavily on timely and accurate diagnosis. Deep learning models have demonstrated great success identifying DR from retinal images. However, relying only on predictions made by models, without any indication of model confidence, creates uncertainty and poses significant risk in clinical settings. This paper investigates an alternative in uncertainty-aware deep learning models, including a reje…

Flacco savors Bengals' victory after uncertainty https://www.espn.com/nfl/story/_/id/46622866/joe-flacco-savors-bengals-victory-benching-trade

Trevon Diggs on return to the field, uncertain of future with Cowboys https://www.dallascowboys.com/news/trevon-diggs-on-return-to-the-field-uncertain-of-future-with-cowboys

Addressing Pitfalls in the Evaluation of Uncertainty Estimation Methods for Natural Language Generation
Mykyta Ielanskyi, Kajetan Schweighofer, Lukas Aichberger, Sepp Hochreiter
https://arxiv.org/abs/2510.02279

Addressing Pitfalls in the Evaluation of Uncertainty Estimation Methods for Natural Language Generation
Hallucinations are a common issue that undermine the reliability of large language models (LLMs). Recent studies have identified a specific subset of hallucinations, known as confabulations, which arise due to predictive uncertainty of LLMs. To detect confabulations, various methods for estimating predictive uncertainty in natural language generation (NLG) have been developed. These methods are typically evaluated by correlating uncertainty estimates with the correctness of generated text, with…

Phys2Real: Fusing VLM Priors with Interactive Online Adaptation for Uncertainty-Aware Sim-to-Real Manipulation
Maggie Wang, Stephen Tian, Aiden Swann, Ola Shorinwa, Jiajun Wu, Mac Schwager
https://arxiv.org/abs/2510.11689

Phys2Real: Fusing VLM Priors with Interactive Online Adaptation for Uncertainty-Aware Sim-to-Real Manipulation
Learning robotic manipulation policies directly in the real world can be expensive and time-consuming. While reinforcement learning (RL) policies trained in simulation present a scalable alternative, effective sim-to-real transfer remains challenging, particularly for tasks that require precise dynamics. To address this, we propose Phys2Real, a real-to-sim-to-real RL pipeline that combines vision-language model (VLM)-inferred physical parameter estimates with interactive adaptation through unce…

Uncertainty in the time of arrival of ultra high energetic neutrinos
Fedele Lizzi
https://arxiv.org/abs/2509.24516 https://arxiv.org/pdf/2509.24516

Uncertainty in the time of arrival of ultra high energetic neutrinos
In the light of the recent observation by the KM3NeT collaboration of an ultrahigh energetic neutrino, we analyse it in its rest frame, where shortly after creation of the neutrino a muon arrives and interacts. Since the neutrino is at rest we use (in this first analysis) a non relativistic Gaussian wave function, and consider its spread. Due to the finite size of the packet, the time of the interaction of the muon will have an uncertainty. This uncertainty is small in the neutrino rest frame, …

Multi-Physics-Enhanced Bayesian Inverse Analysis: Information Gain from Additional Fields
Lea J. Haeusel, Jonas Nitzler, Lea J. K\"oglmeier, Wolfgang A. Wall
https://arxiv.org/abs/2510.11095

Multi-Physics-Enhanced Bayesian Inverse Analysis: Information Gain from Additional Fields
Many real-world inverse problems suffer from limited data, often because they rely on measurements of a single physical field. Such data frequently fail to sufficiently reduce parameter uncertainty in Bayesian inverse analysis. Incorporating easily available data from additional physical fields can substantially decrease this uncertainty. We focus on Bayesian inverse analyses based on computational models, e.g., those using the finite element method. To incorporate data from additional physical…

On Sharp Heisenberg Uncertainty Principle and the stability
Xia Huang, Dong Ye
https://arxiv.org/abs/2510.00453 https://arxiv.org/pdf/2510.00453

On Sharp Heisenberg Uncertainty Principle and the stability
In this work, we summarize the linearization method to study the Heisenberg Uncertainty Principles, and explain that the same approach can be used to handle the stability problem. As examples of application, combining with spherical harmonic decomposition and the Hardy inequalities, we revise two families of inequalities. We give firstly an affirmative answer in dimension four to Cazacu-Flynn-Lam's conjecture [JFA, 2022] for the sharp Hydrogen Uncertainty Principle, and improve the recent estim…

EntropyLong: Effective Long-Context Training via Predictive Uncertainty
Junlong Jia, Ziyang Chen, Xing Wu, Chaochen Gao, Zijia Lin, Debing Zhang, Songlin Hu, Binghui Guo
https://arxiv.org/abs/2510.02330

EntropyLong: Effective Long-Context Training via Predictive Uncertainty
Training long-context language models to capture long-range dependencies requires specialized data construction. Current approaches, such as generic text concatenation or heuristic-based variants, frequently fail to guarantee genuine long-range dependencies. We propose EntropyLong, a novel data construction method that leverages predictive uncertainty to verify dependency quality. Our approach identifies high-entropy positions in documents, retrieves semantically relevant contexts from large co…

Uncertainty-Aware Concept Bottleneck Models with Enhanced Interpretability
Haifei Zhang, Patrick Barry, Eduardo Brandao
https://arxiv.org/abs/2510.00773 https://

Uncertainty-Aware Concept Bottleneck Models with Enhanced Interpretability
In the context of image classification, Concept Bottleneck Models (CBMs) first embed images into a set of human-understandable concepts, followed by an intrinsically interpretable classifier that predicts labels based on these intermediate representations. While CBMs offer a semantically meaningful and interpretable classification pipeline, they often sacrifice predictive performance compared to end-to-end convolutional neural networks. Moreover, the propagation of uncertainty from concept pred…

Large Language Model-Based Uncertainty-Adjusted Label Extraction for Artificial Intelligence Model Development in Upper Extremity Radiography
Hanna Kreutzer, Anne-Sophie Caselitz, Thomas Dratsch, Daniel Pinto dos Santos, Christiane Kuhl, Daniel Truhn, Sven Nebelung
https://arxiv.org/abs/2510.05664 …

Large Language Model-Based Uncertainty-Adjusted Label Extraction for Artificial Intelligence Model Development in Upper Extremity Radiography
Objectives: To evaluate GPT-4o's ability to extract diagnostic labels (with uncertainty) from free-text radiology reports and to test how these labels affect multi-label image classification of musculoskeletal radiographs. Methods: This retrospective study included radiography series of the clavicle (n=1,170), elbow (n=3,755), and thumb (n=1,978). After anonymization, GPT-4o filled out structured templates by indicating imaging findings as present ("true"), absent ("false"), or "uncertain." To …

Grid Restoration Under Uncertainty Considering Coupled Transportation-Power Networks
Harshal D. Kaushik, Roshni Anna Jacob, Souma Chowdhury, Jie Zhang
https://arxiv.org/abs/2510.10399

Grid Restoration Under Uncertainty Considering Coupled Transportation-Power Networks
A stochastic mixed-integer programming model is developed to address the power distribution system repair and restoration following failures caused by extreme events such as natural disasters. This model addresses the complex challenge of efficiently assigning and dispatching repair crews to minimize both downtime and the extent of outages, following the crew sequence constraints. By incorporating a realistic transportation network, our model accounts for uncertainties in repair times, repair d…

Localized Uncertainty Quantification in Random Forests via Proximities
Jake S. Rhodes, Scott D. Brown, J. Riley Wilkinson
https://arxiv.org/abs/2509.22928 https://

Localized Uncertainty Quantification in Random Forests via Proximities
In machine learning, uncertainty quantification helps assess the reliability of model predictions, which is important in high-stakes scenarios. Traditional approaches often emphasize predictive accuracy, but there is a growing focus on incorporating uncertainty measures. This paper addresses localized uncertainty quantification in random forests. While current methods often rely on quantile regression or Monte Carlo techniques, we propose a new approach using naturally occurring test sets and s…

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[4/5]:
- Sample, Don't Search: Rethinking Test-Time Alignment for Language Models
Gon\c{c}alo Faria, Noah A. Smith
https://arxiv.org/abs/2504.03790 https://mastoxiv.page/@arXiv_csCL_bot/114301112970577326
- A Survey on Archetypal Analysis
Aleix Alcacer, Irene Epifanio, Sebastian Mair, Morten M{\o}rup
https://arxiv.org/abs/2504.12392 https://mastoxiv.page/@arXiv_statME_bot/114357826909813483
- The Stochastic Occupation Kernel (SOCK) Method for Learning Stochastic Differential Equations
Michael L. Wells, Kamel Lahouel, Bruno Jedynak
https://arxiv.org/abs/2505.11622 https://mastoxiv.page/@arXiv_statML_bot/114539065460187982
- BOLT: Block-Orthonormal Lanczos for Trace estimation of matrix functions
Kingsley Yeon, Promit Ghosal, Mihai Anitescu
https://arxiv.org/abs/2505.12289 https://mastoxiv.page/@arXiv_mathNA_bot/114539035462135281
- Clustering and Pruning in Causal Data Fusion
Otto Tabell, Santtu Tikka, Juha Karvanen
https://arxiv.org/abs/2505.15215 https://mastoxiv.page/@arXiv_statML_bot/114550346291754635
- On the performance of multi-fidelity and reduced-dimensional neural emulators for inference of ph...
Chloe H. Choi, Andrea Zanoni, Daniele E. Schiavazzi, Alison L. Marsden
https://arxiv.org/abs/2506.11683 https://mastoxiv.page/@arXiv_statML_bot/114692410563481289
- Beyond Force Metrics: Pre-Training MLFFs for Stable MD Simulations
Maheshwari, Tang, Ock, Kolluru, Farimani, Kitchin
https://arxiv.org/abs/2506.14850 https://mastoxiv.page/@arXiv_physicschemph_bot/114709402590755731
- Quantifying Uncertainty in the Presence of Distribution Shifts
Yuli Slavutsky, David M. Blei
https://arxiv.org/abs/2506.18283 https://mastoxiv.page/@arXiv_statML_bot/114738165218533987
- ZKPROV: A Zero-Knowledge Approach to Dataset Provenance for Large Language Models
Mina Namazi, Alexander Nemecek, Erman Ayday
https://arxiv.org/abs/2506.20915 https://mastoxiv.page/@arXiv_csCR_bot/114754394485208892
- SpecCLIP: Aligning and Translating Spectroscopic Measurements for Stars
Zhao, Huang, Xue, Kong, Liu, Tang, Beers, Ting, Luo
https://arxiv.org/abs/2507.01939 https://mastoxiv.page/@arXiv_astrophIM_bot/114788369702591337
- Towards Facilitated Fairness Assessment of AI-based Skin Lesion Classifiers Through GenAI-based I...
Ko Watanabe, Stanislav Frolov, Aya Hassan, David Dembinsky, Adriano Lucieri, Andreas Dengel
https://arxiv.org/abs/2507.17860 https://mastoxiv.page/@arXiv_csCV_bot/114912976717523345
- PASS: Probabilistic Agentic Supernet Sampling for Interpretable and Adaptive Chest X-Ray Reasoning
Yushi Feng, Junye Du, Yingying Hong, Qifan Wang, Lequan Yu
https://arxiv.org/abs/2508.10501 https://mastoxiv.page/@arXiv_csAI_bot/115032101532614110
- Unified Acoustic Representations for Screening Neurological and Respiratory Pathologies from Voice
Ran Piao, Yuan Lu, Hareld Kemps, Tong Xia, Aaqib Saeed
https://arxiv.org/abs/2508.20717 https://mastoxiv.page/@arXiv_csSD_bot/115111255835875066
- Machine Learning-Driven Predictive Resource Management in Complex Science Workflows
Tasnuva Chowdhury, et al.
https://arxiv.org/abs/2509.11512 https://mastoxiv.page/@arXiv_csDC_bot/115213444524490263
- MatchFixAgent: Language-Agnostic Autonomous Repository-Level Code Translation Validation and Repair
Ali Reza Ibrahimzada, Brandon Paulsen, Reyhaneh Jabbarvand, Joey Dodds, Daniel Kroening
https://arxiv.org/abs/2509.16187 https://mastoxiv.page/@arXiv_csSE_bot/115247172280557686
- Automated Machine Learning Pipeline: Large Language Models-Assisted Automated Dataset Generation ...
Adam Lahouari, Jutta Rogal, Mark E. Tuckerman
https://arxiv.org/abs/2509.21647 https://mastoxiv.page/@arXiv_condmatmtrlsci_bot/115286737423175311
- Quantifying the Impact of Structured Output Format on Large Language Models through Causal Inference
Han Yuan, Yue Zhao, Li Zhang, Wuqiong Luo, Zheng Ma
https://arxiv.org/abs/2509.21791 https://mastoxiv.page/@arXiv_csCL_bot/115287166674809413
- The Generation Phases of Flow Matching: a Denoising Perspective
Anne Gagneux, S\'egol\`ene Martin, R\'emi Gribonval, Mathurin Massias
https://arxiv.org/abs/2510.24830 https://mastoxiv.page/@arXiv_csCV_bot/115462527449411627
- Data-driven uncertainty-aware seakeeping prediction of the Delft 372 catamaran using ensemble Han...
Giorgio Palma, Andrea Serani, Matteo Diez
https://arxiv.org/abs/2511.04461 https://mastoxiv.page/@arXiv_eessSY_bot/115507785247809767
- Generalized infinite dimensional Alpha-Procrustes based geometries
Salvish Goomanee, Andi Han, Pratik Jawanpuria, Bamdev Mishra
https://arxiv.org/abs/2511.09801 https://mastoxiv.page/@arXiv_statML_bot/115547135711272091
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Empowering Prosumers: Incentive Design for Local Electricity Markets Under Generalized Uncertainty and Grid Constraints
P{\aa}l Forr Austnes, Matthieu Jacobs, Lu Wang, Mario Paolone
https://arxiv.org/abs/2510.12318

Empowering Prosumers: Incentive Design for Local Electricity Markets Under Generalized Uncertainty and Grid Constraints
Since the 1990s, widespread introduction of central (wholesale) electricity markets has been seen across multiple continents, driven by the search for efficient operation of the power grid through competition. The increase of renewables has made significant impacts both on central electricity markets and distribution-level grids as renewable power generation is often connected to the latter. These stochastic renewable technologies have both advantages and disadvantages. On one hand they offer v…

TSMC reports a 16.9% rise in October sales, the slowest pace since February 2024, highlighting uncertainty over the AI boom's sustainability; TSMC is up 37% YTD (Debby Wu/Bloomberg)
https://www.bloomberg.com/news/articles/2025-11-…

A key segment of the job market isn't hiring. These businesses say why. (Washington Post)
https://www.washingtonpost.com/business/2025/11/09/small-business-owners-hiring-uncertainty/?pwapi_token=eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJyZWFzb24iOiJnaWZ0IiwibmJmIjoxNzYyNjY0NDAwLCJpc3MiOiJzdWJzY3JpcHRpb25zIiwiZXhwIjoxNzY0MDQ2Nzk5LCJpYXQiOjE3NjI2NjQ0MDAsImp0aSI6ImMzNmRkZThmLWVlNmQtNGFkYy04MjJhLWZhYjJlNDAyYmQ5NSIsInVybCI6Imh0dHBzOi8vd3d3Lndhc2hpbmd0b25wb3N0LmNvbS9idXNpbmVzcy8yMDI1LzExLzA5L3NtYWxsLWJ1c2luZXNzLW93bmVycy1oaXJpbmctdW5jZXJ0YWludHkvIn0.OlmJ2AKB_cXKQxBGRK1FwkH57p6cdVIJcprVEeB4SY8&itid=gfta
http://www.memeorandum.com/251109/p34#a251109p34

Precise and Efficient Collision Prediction under Uncertainty in Autonomous Driving
Marc Kaufeld, Johannes Betz
https://arxiv.org/abs/2510.05729 https://arx…

Precise and Efficient Collision Prediction under Uncertainty in Autonomous Driving
This research introduces two efficient methods to estimate the collision risk of planned trajectories in autonomous driving under uncertain driving conditions. Deterministic collision checks of planned trajectories are often inaccurate or overly conservative, as noisy perception, localization errors, and uncertain predictions of other traffic participants introduce significant uncertainty into the planning process. This paper presents two semi-analytic methods to compute the collision probabili…

New projection technique improves flood warning systems in face of climate uncertainty. #climatechange #climatesolutions #climate

Enhanced projection technique addresses flood warning amid climate uncertainty
The uncertainty surrounding flood risk is a major cause for concern and an obstacle to adaptation. According to researchers from Japan, their new statistical method increases the accuracy of flood risk projections across about 70% of Earth's landmass.

Leveraging Quantum Computing For Recourse-Based Energy Management Under PV Generation Uncertainty
Daniel M\"ussig, Mustafa Musab, Markus Wappler, J\"org L\"assig
https://arxiv.org/abs/2509.23133

Leveraging Quantum Computing For Recourse-Based Energy Management Under PV Generation Uncertainty
The integration of distributed energy resources, particularly photovoltaic (PV) systems and electric vehicles (EVs), introduces significant uncertainty and complexity into modern energy systems. This paper explores a novel approach to address these challenges by formulating a stochastic optimization problem that models the uncertain nature of PV power generation and the flexibility of bi-directional EV charging. The problem is structured as a two-stage stochastic program with recourse, enabling…

Hamilton-Jacobi Reachability for Viability Analysis of Constrained Waste-to-Energy Systems under Adversarial Uncertainty
Achraf Bouhmady, Othman Cherkaoui Dekkaki
https://arxiv.org/abs/2510.11396

Hamilton-Jacobi Reachability for Viability Analysis of Constrained Waste-to-Energy Systems under Adversarial Uncertainty
This paper investigates the problem of maintaining the safe operation of Waste-to-Energy (WtE) systems under operational constraints and uncertain waste inflows. We model this as a robust viability problem, formulated as a zero-sum differential game between a control policy and an adversarial disturbance. Within a Hamilton-Jacobi framework, the viability kernel is characterized as the zero sublevel set of a value function satisfying a constrained Hamilton-Jacobi-Bellman (HJB) equation in the vi…

Conformalized Gaussian processes for online uncertainty quantification over graphs
Jinwen Xu, Qin Lu, Georgios B. Giannakis
https://arxiv.org/abs/2510.06181 https://

Conformalized Gaussian processes for online uncertainty quantification over graphs
Uncertainty quantification (UQ) over graphs arises in a number of safety-critical applications in network science. The Gaussian process (GP), as a classical Bayesian framework for UQ, has been developed to handle graph-structured data by devising topology-aware kernel functions. However, such GP-based approaches are limited not only by the prohibitive computational complexity, but also the strict modeling assumptions that might yield poor coverage, especially with labels arriving on the fly. To…

Geopolitics, Geoeconomics and Risk:A Machine Learning Approach
Alvaro Ortiz, Tomasa Rodrigo
https://arxiv.org/abs/2510.12416 https://arxiv.org/pdf/2510.124…

Geopolitics, Geoeconomics and Risk:A Machine Learning Approach
We introduce a novel high-frequency daily panel dataset of both markets and news-based indicators -- including Geopolitical Risk, Economic Policy Uncertainty, Trade Policy Uncertainty, and Political Sentiment -- for 42 countries across both emerging and developed markets. Using this dataset, we study how sentiment dynamics shape sovereign risk, measured by Credit Default Swap (CDS) spreads, and evaluate their forecasting value relative to traditional drivers such as global monetary policy and m…

From tests to effect sizes: Quantifying uncertainty and statistical variability in multilingual and multitask NLP evaluation benchmarks
Jonne S\"alev\"a, Duygu Ataman, Constantine Lignos
https://arxiv.org/abs/2509.22612

From tests to effect sizes: Quantifying uncertainty and statistical variability in multilingual and multitask NLP evaluation benchmarks
In this paper, we introduce a set of resampling-based methods for quantifying uncertainty and statistical precision of evaluation metrics in multilingual and/or multitask NLP benchmarks. We show how experimental variation in performance scores arises from both model- and data-related sources, and that accounting for both of them is necessary to avoid substantially underestimating the overall variability over hypothetical replications. Using multilingual question answering, machine translation, …

AdaNav: Adaptive Reasoning with Uncertainty for Vision-Language Navigation
Xin Ding, Jianyu Wei, Yifan Yang, Shiqi Jiang, Qianxi Zhang, Hao Wu, Fucheng Jia, Liang Mi, Yuxuan Yan, Weijun Wang, Yunxin Liu, Zhibo Chen, Ting Cao
https://arxiv.org/abs/2509.24387

AdaNav: Adaptive Reasoning with Uncertainty for Vision-Language Navigation
Vision Language Navigation (VLN) requires agents to follow natural language instructions by grounding them in sequential visual observations over long horizons. Explicit reasoning could enhance temporal consistency and perception action alignment, but reasoning at fixed steps often leads to suboptimal performance and unnecessary computation. To address this, we propose AdaNav, an uncertainty-based adaptive reasoning framework for VLN. At its core is the Uncertainty Adaptive Reasoning Block (UAR…

Uncertainty Quantification for Regression using Proper Scoring Rules
Alexander Fishkov, Kajetan Schweighofer, Mykyta Ielanskyi, Nikita Kotelevskii, Mohsen Guizani, Maxim Panov
https://arxiv.org/abs/2509.26610

Uncertainty Quantification for Regression using Proper Scoring Rules
Quantifying uncertainty of machine learning model predictions is essential for reliable decision-making, especially in safety-critical applications. Recently, uncertainty quantification (UQ) theory has advanced significantly, building on a firm basis of learning with proper scoring rules. However, these advances were focused on classification, while extending these ideas to regression remains challenging. In this work, we introduce a unified UQ framework for regression based on proper scoring r…

A Gradient Guided Diffusion Framework for Chance Constrained Programming
Boyang Zhang, Zhiguo Wang, Ya-Feng Liu
https://arxiv.org/abs/2510.12238 https://ar…

A Gradient Guided Diffusion Framework for Chance Constrained Programming
Chance constrained programming (CCP) is a powerful framework for addressing optimization problems under uncertainty. In this paper, we introduce a novel Gradient-Guided Diffusion-based Optimization framework, termed GGDOpt, which tackles CCP through three key innovations. First, GGDOpt accommodates a broad class of CCP problems without requiring the knowledge of the exact distribution of uncertainty-relying solely on a set of samples. Second, to address the nonconvexity of the chance constraint…

Clinical Uncertainty Impacts Machine Learning Evaluations
Simone Lionetti, Fabian Gr\"oger, Philippe Gottfrois, Alvaro Gonzalez-Jimenez, Ludovic Amruthalingam, Alexander A. Navarini, Marc Pouly
https://arxiv.org/abs/2509.22242

Clinical Uncertainty Impacts Machine Learning Evaluations
Clinical dataset labels are rarely certain as annotators disagree and confidence is not uniform across cases. Typical aggregation procedures, such as majority voting, obscure this variability. In simple experiments on medical imaging benchmarks, accounting for the confidence in binary labels significantly impacts model rankings. We therefore argue that machine-learning evaluations should explicitly account for annotation uncertainty using probabilistic metrics that directly operate on distribut…

Uncertainty-Guided Expert-AI Collaboration for Efficient Soil Horizon Annotation
Teodor Chiaburu, Vipin Singh, Frank Hau{\ss}er, Felix Bie{\ss}mann
https://arxiv.org/abs/2509.24873

Uncertainty-Guided Expert-AI Collaboration for Efficient Soil Horizon Annotation
Uncertainty quantification is essential in human-machine collaboration, as human agents tend to adjust their decisions based on the confidence of the machine counterpart. Reliably calibrated model uncertainties, hence, enable more effective collaboration, targeted expert intervention and more responsible usage of Machine Learning (ML) systems. Conformal prediction has become a well established model-agnostic framework for uncertainty calibration of ML models, offering statistically valid confid…

Statistical Uncertainty Learning for Robust Visual-Inertial State Estimation
Seungwon Choi, Donggyu Park, Seo-Yeon Hwang, Tae-Wan Kim
https://arxiv.org/abs/2510.01648 https://…

Statistical Uncertainty Learning for Robust Visual-Inertial State Estimation
A fundamental challenge in robust visual-inertial odometry (VIO) is to dynamically assess the reliability of sensor measurements. This assessment is crucial for properly weighting the contribution of each measurement to the state estimate. Conventional methods often simplify this by assuming a static, uniform uncertainty for all measurements. This heuristic, however, may be limited in its ability to capture the dynamic error characteristics inherent in real-world data. To improve this limitatio…

Optimal Regularization Under Uncertainty: Distributional Robustness and Convexity Constraints
Oscar Leong, Eliza O'Reilly, Yong Sheng Soh
https://arxiv.org/abs/2510.03464 ht…

Optimal Regularization Under Uncertainty: Distributional Robustness and Convexity Constraints
Regularization is a central tool for addressing ill-posedness in inverse problems and statistical estimation, with the choice of a suitable penalty often determining the reliability and interpretability of downstream solutions. While recent work has characterized optimal regularizers for well-specified data distributions, practical deployments are often complicated by distributional uncertainty and the need to enforce structural constraints such as convexity. In this paper, we introduce a frame…

Unsupervised Conformal Inference: Bootstrapping and Alignment to Control LLM Uncertainty
Lingyou Pang, Lei Huang, Jianyu Lin, Tianyu Wang, Akira Horiguchi, Alexander Aue, Carey E. Priebe
https://arxiv.org/abs/2509.23002

Unsupervised Conformal Inference: Bootstrapping and Alignment to Control LLM Uncertainty
Deploying black-box LLMs requires managing uncertainty in the absence of token-level probability or true labels. We propose introducing an unsupervised conformal inference framework for generation, which integrates: generative models, incorporating: (i) an LLM-compatible atypical score derived from response-embedding Gram matrix, (ii) UCP combined with a bootstrapping variant (BB-UCP) that aggregates residuals to refine quantile precision while maintaining distribution-free, finite-sample cover…

HyPlan: Hybrid Learning-Assisted Planning Under Uncertainty for Safe Autonomous Driving
Donald Pfaffmann, Matthias Klusch, Marcel Steinmetz
https://arxiv.org/abs/2510.07210 http…

HyPlan: Hybrid Learning-Assisted Planning Under Uncertainty for Safe Autonomous Driving
We present a novel hybrid learning-assisted planning method, named HyPlan, for solving the collision-free navigation problem for self-driving cars in partially observable traffic environments. HyPlan combines methods for multi-agent behavior prediction, deep reinforcement learning with proximal policy optimization and approximated online POMDP planning with heuristic confidence-based vertical pruning to reduce its execution time without compromising safety of driving. Our experimental performan…

Uncertainty-Aware Deep Learning for Wildfire Danger Forecasting
Spyros Kondylatos, Gustau Camps-Valls, Ioannis Papoutsis
https://arxiv.org/abs/2509.25017 https://

Uncertainty-Aware Deep Learning for Wildfire Danger Forecasting
Wildfires are among the most severe natural hazards, posing a significant threat to both humans and natural ecosystems. The growing risk of wildfires increases the demand for forecasting models that are not only accurate but also reliable. Deep Learning (DL) has shown promise in predicting wildfire danger; however, its adoption is hindered by concerns over the reliability of its predictions, some of which stem from the lack of uncertainty quantification. To address this challenge, we present an…

U-LAG: Uncertainty-Aware, Lag-Adaptive Goal Retargeting for Robotic Manipulation
Anamika J H, Anujith Muraleedharan
https://arxiv.org/abs/2510.02526 https://

U-LAG: Uncertainty-Aware, Lag-Adaptive Goal Retargeting for Robotic Manipulation
Robots manipulating in changing environments must act on percepts that are late, noisy, or stale. We present U-LAG, a mid-execution goal-retargeting layer that leaves the low-level controller unchanged while re-aiming task goals (pre-contact, contact, post) as new observations arrive. Unlike motion retargeting or generic visual servoing, U-LAG treats in-flight goal re-aiming as a first-class, pluggable module between perception and control. Our main technical contribution is UAR-PF, an uncertai…

Reinforcement Learning from Probabilistic Forecasts for Safe Decision-Making via Conditional Value-at-Risk Planning
Michal Koren, Or Peretz, Tai Dinh, Philip S. Yu
https://arxiv.org/abs/2510.08226

Reinforcement Learning from Probabilistic Forecasts for Safe Decision-Making via Conditional Value-at-Risk Planning
Sequential decisions in volatile, high-stakes settings require more than maximizing expected return; they require principled uncertainty management. This paper presents the Uncertainty-Aware Markov Decision Process (UAMDP), a unified framework that couples Bayesian forecasting, posterior-sampling reinforcement learning, and planning under a conditional value-at-risk (CVaR) constraint. In a closed loop, the agent updates its beliefs over latent dynamics, samples plausible futures via Thompson sa…

QDeepGR4J: Quantile-based ensemble of deep learning and GR4J hybrid rainfall-runoff models for extreme flow prediction with uncertainty quantification
Arpit Kapoor, Rohitash Chandra
https://arxiv.org/abs/2510.05453

QDeepGR4J: Quantile-based ensemble of deep learning and GR4J hybrid rainfall-runoff models for extreme flow prediction with uncertainty quantification
Conceptual rainfall-runoff models aid hydrologists and climate scientists in modelling streamflow to inform water management practices. Recent advances in deep learning have unravelled the potential for combining hydrological models with deep learning models for better interpretability and improved predictive performance. In our previous work, we introduced DeepGR4J, which enhanced the GR4J conceptual rainfall-runoff model using a deep learning model to serve as a surrogate for the routing comp…

Spatially-informed transformers: Injecting geostatistical covariance biases into self-attention for spatio-temporal forecasting
Yuri Calleo
https://arxiv.org/abs/2512.17696 https://arxiv.org/pdf/2512.17696 https://arxiv.org/html/2512.17696
arXiv:2512.17696v1 Announce Type: new
Abstract: The modeling of high-dimensional spatio-temporal processes presents a fundamental dichotomy between the probabilistic rigor of classical geostatistics and the flexible, high-capacity representations of deep learning. While Gaussian processes offer theoretical consistency and exact uncertainty quantification, their prohibitive computational scaling renders them impractical for massive sensor networks. Conversely, modern transformer architectures excel at sequence modeling but inherently lack a geometric inductive bias, treating spatial sensors as permutation-invariant tokens without a native understanding of distance. In this work, we propose a spatially-informed transformer, a hybrid architecture that injects a geostatistical inductive bias directly into the self-attention mechanism via a learnable covariance kernel. By formally decomposing the attention structure into a stationary physical prior and a non-stationary data-driven residual, we impose a soft topological constraint that favors spatially proximal interactions while retaining the capacity to model complex dynamics. We demonstrate the phenomenon of ``Deep Variography'', where the network successfully recovers the true spatial decay parameters of the underlying process end-to-end via backpropagation. Extensive experiments on synthetic Gaussian random fields and real-world traffic benchmarks confirm that our method outperforms state-of-the-art graph neural networks. Furthermore, rigorous statistical validation confirms that the proposed method delivers not only superior predictive accuracy but also well-calibrated probabilistic forecasts, effectively bridging the gap between physics-aware modeling and data-driven learning.
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