#uncertainty

30 curated events

papersTODAY 04:00 UTC

Hybrid Machine-Learning Model Adds Uncertainty Estimates to Irrigation Scheduling

A new arXiv paper presents a hybrid mathematical and machine-learning approach for irrigation decision support that also quantifies confidence in its soil-moisture forecasts. The authors note that irrigation is typically scheduled reactively, even though agriculture uses about 70% of global freshwater withdrawals. The model aims to give growers both a forward-looking moisture prediction and a measure of how reliable that prediction is.

research/agriculture/uncertainty/forecasting

papersTODAY 04:00 UTC

arXiv Paper Proposes Online Bayesian Node Classification for Evolving Graphs

A new arXiv preprint addresses node classification on evolving graphs, where classifiers must generalize to newly arriving nodes despite distribution shift while also providing calibrated uncertainty. The authors propose an online Bayesian approach aimed at inductive settings where safety-sensitive applications require trustworthy confidence estimates. The work targets a gap left by standard graph neural networks, which typically assume static graphs and offer limited uncertainty quantification.

graph-neural-networks/bayesian/research/uncertainty

papersTODAY 04:00 UTC

Noise-Adaptive Conformal Classification With Marginal Coverage

A revised arXiv paper proposes a noise-adaptive approach to conformal inference for classification. Standard conformal methods give prediction sets with guaranteed coverage, but the work addresses how that reliability holds when label or feature noise is present. The method targets marginal coverage guarantees while adjusting to noisy data conditions.

conformal-prediction/uncertainty/classification/arxiv

papersTODAY 04:00 UTC

Paper proposes tighter confidence regions for importance weights in label shift

A new arXiv preprint addresses how finite-sample uncertainty degrades importance weights used for domain adaptation under label shift. Existing work often relies on Gaussian approximations, while this paper derives confidence regions that convert the problem into a matrix inversion and constraint formulation, yielding provably tighter bounds. The result is intended to make weight-based adaptation more reliable when sample sizes are limited.

label-shift/domain-adaptation/uncertainty/arxiv

papersTODAY 04:00 UTC

Method restores distance-awareness guarantees in spline-based Kolmogorov-Arnold networks

A new arXiv preprint addresses a limitation in DAREK, a computationally cheap bottom-up scheme for estimating uncertainty in Kolmogorov-Arnold Networks that use spline activations. The authors describe the problem as "fictitious knots" that weaken distance-awareness guarantees in high-dimensional settings, and propose a fix to restore them. The work targets interpretable function approximation, where reliable uncertainty estimates matter for trusting model outputs.

research/uncertainty/kan/arxiv

papersTODAY 04:00 UTC

Sensory Precision Inference Proposed for Multimodal Arbitration in Agents

A new arXiv preprint introduces a method for autonomous agents to weigh sensory modalities against each other when inputs are noisy, incomplete, or contradictory. The approach infers how reliable each stream is and uses that estimate to arbitrate between modalities rather than assuming all sensors are equally trustworthy. The work targets robustness in real-world environments where sensory data quality varies over time.

agents/multimodal/uncertainty/arxiv

papersTODAY 04:00 UTC

Deep Evidential Regression Model Estimates Forest Height from Satellite Imagery

A revised arXiv paper presents a deep evidential regression approach for estimating forest height using multimodal satellite imagery. The method targets applications including carbon accounting, biodiversity monitoring, and ecosystem management. It aims to give accurate predictions while quantifying uncertainty in sparse-data settings.

remote-sensing/uncertainty/climate/deep-learning

papersTODAY 04:00 UTC

Chance-Constrained Maneuver Planning for Satellite Collision Avoidance Under Uncertainty

A new arXiv paper presents a planning method that decides whether a spacecraft has enough information to justify a collision-avoidance maneuver in low Earth orbit. The approach frames the decision as a chance-constrained problem in belief space, accounting for uncertainty in the predicted encounter. It aims to help operators handle the rising number of conjunctions without committing to unnecessary burns.

spacecraft/planning/uncertainty/research

papersTODAY 04:00 UTC

Thesis Examines Introspective Uncertainty Estimation for LLM Code Generation

A newly posted arXiv thesis investigates whether large language models can gauge the reliability of the code they produce, addressing the problem of fluent but functionally incorrect output. The work focuses on introspective uncertainty estimation as a way to flag low-confidence generations in software engineering workflows. The abstract is truncated, so the full methods and results are not yet detailed in the listing.

llm/code-generation/uncertainty/research

papersTODAY 04:00 UTC

Multi-source conformal prediction method uses localization to handle heterogeneous data

A new arXiv paper proposes a conformal prediction approach that draws on multiple heterogeneous data sources rather than treating them as one pool. The method exploits differences between sources through localization, aiming to keep prediction sets reliable when the test distribution departs from any single source. This targets settings where combining sources is useful but naive pooling would break coverage guarantees.

conformal-prediction/research/uncertainty/machine-learning

papersTODAY 04:00 UTC

Conformal Prediction Method Targets Ambiguous Class Boundaries in Medical Imaging

A new arXiv paper addresses a common problem in medical image classification: transitional categories whose features overlap with neighboring classes, creating fuzzy decision boundaries. The proposed approach, adaptive conformal redistribution, aims to sharpen uncertainty-aware prediction sets so they remain informative when a case sits between two diagnoses. Conformal prediction normally provides coverage guarantees for such sets, and the work adapts that framework to inter-class transitions.

conformal-prediction/medical-imaging/uncertainty/research

papersTODAY 04:00 UTC

Calibrated Uncertainty Maps for Radiative Gaussian Splatting in Sparse-View CT

Researchers examine whether uncertainty maps reliably indicate where a sparse-view CT reconstruction is erroneous, and find a discrepancy between whole-volume scoring and localization of errors within the imaged object. They derive analytic, clamp-aware moments to produce calibrated uncertainty estimates for radiative Gaussian splatting reconstructions. The work suggests standard variance-based uncertainty is not equivalent to an error map in this setting.

medical-imaging/uncertainty/gaussian-splatting/sparse-view-ct

papersTODAY 04:00 UTC

Bayesian Optimisation Method Combines Expert Gaussian Processes With Calibrated Uncertainty

A new arXiv preprint proposes using a product-of-experts Gaussian process as the surrogate model in Bayesian optimisation, rather than a single global GP. The authors address the cubic scaling cost of standard GP regression with training set size, which restricts its use on larger datasets, and add a calibration step for uncertainty estimates. The work falls in the machine learning methodology category and has not yet been peer reviewed.

bayesian-optimisation/gaussian-processes/uncertainty/arxiv

papersTODAY 04:00 UTC

Calibrated Uncertainty Estimation for LLM Clinical Text Classification

A new arXiv paper addresses the risk of overconfident errors when large language models classify clinical text, where a wrong label can affect patient care. The authors note that current black-box approaches simply attach a confidence score to an unchanged LLM prediction, and they propose an uncertainty-aware method designed to produce better-calibrated results. The work targets medical NLP settings where knowing when a model is unreliable matters as much as the predicted label itself.

clinical-nlp/uncertainty/calibration/llm

papersTODAY 04:00 UTC

ABSOL Framework Combines Bayesian Subsampling with LLMs for Structured Data

A new arXiv paper introduces ABSOL, a method that pairs aggregated Bayesian subsampling with large language models to improve reasoning over structured data. The approach targets cases where reliable answers depend on consistent evidence, dependency-aware reasoning, and estimated uncertainty. The authors frame the work as addressing the unreliability of LLMs used as natural-language interfaces to Bayesian networks.

bayesian/llm/uncertainty/research

papersSEP 11 04:00 UTC

arXiv Study Benchmarks Non-Conformity Score Functions for Conformal Prediction

A revised arXiv preprint surveys and compares non-conformity score functions used in conformal prediction for classification. Conformal prediction generates prediction sets rather than single labels, with guarantees that the sets cover the true class at a chosen rate. The work evaluates how different scoring choices affect the efficiency and validity of those sets.

conformal-prediction/benchmarking/uncertainty/machine-learning

papersSEP 10 04:00 UTC

Conformal prediction method brings coverage guarantees to 3D Gaussian Splatting views

A new arXiv paper frames novel-view synthesis with 3D Gaussian Splatting as a structured regression problem and applies conformal prediction to it. The goal is to give rendered views formal statistical coverage guarantees, going beyond uncertainty heatmaps that offer no such certification. The approach is aimed at settings where rendered outputs must meet verifiable reliability standards.

gaussian-splatting/conformal-prediction/uncertainty/computer-vision

papersSEP 10 04:00 UTC

Accountable and uncertainty-aware evaluation of sensor-based AI under distribution shift

A new machine learning paper tackles the gap between training conditions and real-world deployment for sensor-based AI, where devices, personnel, and time periods all differ from the original training setup. The authors propose a staged evaluation methodology that quantifies uncertainty and captures performance degradation that conventional random train-test splits can hide. The work draws on data collected from multiple devices and subjects over nearly three years in an underground environment.

evaluation/distribution-shift/sensors/uncertainty

papersSEP 10 04:00 UTC

Decomposing LLM-Judge Uncertainty to Target Expert Labels

A research paper addresses how to decide which LLM-judged outputs actually need human expert review. It separates the judge's uncertainty into aleatoric uncertainty, which reflects genuine disagreement among experts and cannot be reduced by more labels, and epistemic uncertainty, which signals where expert annotation would help. The goal is to spend limited expert labeling effort on the cases where it is most useful.

evaluation/uncertainty/human-in-the-loop

papersSEP 10 04:00 UTC

MiNCE: Consistent Confidence Envelopes for Band-Limited Functions Introduced in New Paper

A new arXiv preprint presents MiNCE, a minimum-norm method for constructing nonparametric, simultaneous confidence regions around band-limited functions and their smoothed spectra. The approach draws on reproducing kernel Hilbert space theory to deliver confidence envelopes that are valid for finite samples and provably strongly consistent.

research/uncertainty/statistics/rkhs

papersSEP 10 04:00 UTC

Bayesian deep learning predicts 3D copper mineralization and drill targets at Rudny Altai

Researchers present an uncertainty-aware workflow that fuses geophysical inversion results with drilling data via Bayesian deep learning to model copper mineralization in three dimensions. Applied to the Kogodai prospect in the Rudny Altai region, the method quantifies prediction uncertainty to help prioritize exploration drilling in structurally complex terrain where sampling is sparse.

deep-learning/mining/uncertainty/geoscience

papersSEP 10 04:00 UTC

ProbPlug: A Plugin Network for Reliable Confidence Estimates in LLM Binary Classification

Researchers have introduced ProbPlug, a plugin uncertainty network designed to attach to large language models and produce more trustworthy confidence scores for binary classification tasks. The work addresses the gap between strong LLM predictive performance and the reliability required for deployment in high-stakes settings. The paper is available as a preprint on arXiv.

llm/calibration/uncertainty/research

papersSEP 10 04:00 UTC

Reliability-Aware Hybrid-K Ensemble Selection Proposed for Cervical Cytology Classification

A new arXiv preprint introduces a hybrid ensemble selection framework for multiclass cervical cytology image classification that weighs discriminative performance alongside calibration and selective prediction. The authors argue that raw accuracy is not enough for clinical image analysis, and that the method is meant to deliver trustworthy confidence scores and uncertainty flags so users know when to distrust a prediction. The work appears in the cs.AI category as an early-stage research contribution.

medical-ai/computer-vision/calibration/uncertainty

papersSEP 10 04:00 UTC

Machine learning model maps sea-ice types with uncertainty estimates from multiple ice charts

Researchers describe a machine learning approach that classifies sea ice by its stage of development, using labels drawn from several operational ice charts compiled by human analysts. The method also estimates uncertainty, which is relevant for navigation and ice monitoring where chart interpretations can vary.

machine-learning/remote-sensing/uncertainty/climate

papersSEP 12 04:00 UTC

ActMap: Single-pass uncertainty quantification from generation-time activation maps

A new arXiv preprint introduces ActMap, a method that estimates how much an LLM answer should be trusted using internal activation maps captured during a single generation pass. The authors position it as an alternative to approaches that require multiple sampled outputs or rely solely on output-token probabilities. The work targets practical uncertainty quantification for large language models.

uncertainty/llm/research/inference

papersSEP 12 04:00 UTC

Calibration Audit Questions Confidence Scores in Feed-Forward 3D Reconstruction Models

A study examines whether the per-pixel confidence values produced by feed-forward 3D reconstruction models can be treated as reliable uncertainty estimates. Because these scores are trained mainly as loss weights, the authors audit how well they are calibrated for downstream use. The paper reports on the limits of reusing them as an uncertainty signal.

3d-reconstruction/uncertainty/calibration/arxiv

papersSEP 12 04:00 UTC

Study Finds Confidence Scale Design Shapes LLM Uncertainty Estimates

A new arXiv paper examines how the numerical scale used for verbalized confidence, usually 0-100, affects the reliability of LLM uncertainty estimates. The authors argue this design choice is not neutral and that scale design should be treated as a meaningful variable in black-box calibration research.

llm/calibration/uncertainty/research

papersSEP 12 04:00 UTC

Paper proposes scheduling business processes under control-flow uncertainty

A new arXiv paper tackles how to plan and schedule activities in business processes when the exact order of steps is not known in advance, since choices often depend on data that only appears during execution. The work aims to improve efficiency measures such as makespan despite this uncertainty. This is an extended version of the paper.

research/scheduling/business-processes/uncertainty

papersSEP 12 04:00 UTC

Calibration-Aware Uncertainty Cascades for Heterogeneous Model Collaboration

A new arXiv preprint proposes a routing method for combining multiple models that uses calibration-aware uncertainty estimates to decide when to escalate a query to a larger, more expensive model. The approach aims to avoid the rigidity of trained routers, which are tied to fixed cost or accuracy trade-offs, while still balancing predictive quality against inference cost. The work targets heterogeneous model collaboration settings where different models offer complementary strengths.

research/model-routing/uncertainty/inference-cost

papersSEP 12 04:00 UTC

Paper Proposes Multi-Signal Pipeline for Detecting LLM Hallucinations

A new arXiv preprint describes a hallucination detection method that combines a fine-tuned DeBERTa-v3 classifier with Monte Carlo dropout uncertainty estimates and other signals. The approach targets domain-specific settings where unfaithful model outputs are a concern. The work is cross-listed under cs.AI.

hallucination/detection/research/uncertainty