Uncertainty Estimation: Charting the Future of Trustworthy AI

Latest 50 papers on uncertainty estimation: Sep. 14, 2025

The quest for intelligent systems capable of not just making predictions, but also knowing what they don’t know, has never been more critical. As AI permeates safety-critical domains like healthcare, autonomous driving, and finance, the ability to accurately quantify uncertainty is paramount for building truly trustworthy and reliable applications. Recent breakthroughs across various AI/ML subfields are pushing the boundaries of uncertainty estimation (UE), transforming how we approach model calibration, robustness, and interpretability.### The Big Idea(s) & Core Innovationsits heart, recent research in uncertainty estimation is driven by the desire to move beyond simple point predictions, providing a nuanced understanding of model confidence. A central theme is the decomposition of predictive uncertainty into aleatoric (inherent noise in data) and epistemic (model’s lack of knowledge) components, offering deeper insights into model behavior. H. Martin Gillis, Isaac Xu, and Thomas Trappenberg from Dalhousie University introduce a novel variance-gated distribution approach to achieve this, outperforming traditional entropy-based methods by explicitly detecting ensemble diversity collapse—a critical indicator of model overconfidence.nuanced understanding extends to Large Language Models (LLMs), where confidence is particularly challenging. Papers like “GENUINE: Graph Enhanced Multi-level Uncertainty Estimation for Large Language Models” by Tuo Wang et al. from Virginia Polytechnic Institute and State University highlight the importance of semantic dependencies, proposing a graph-based framework using dependency parse trees to enhance uncertainty quantification. Complementing this, “TokUR: Token-Level Uncertainty Estimation for Large Language Model Reasoning” by Tunyu Zhang et al. focuses on token-level uncertainty in LLMs through low-rank random weight perturbations, providing self-assessment capabilities in reasoning tasks. Furthering LLM reliability, “Enhancing Uncertainty Estimation in LLMs with Expectation of Aggregated Internal Belief” by Zeguan Xiao et al. from Shanghai University of Finance and Economics introduces EAGLE, a training-free, self-evaluation method that leverages internal hidden states for superior calibration and safety. For numeric fidelity, “Proof-Carrying Numbers (PCN): A Protocol for Trustworthy Numeric Answers from LLMs via Claim Verification” by Aivin V. Solatorio of The World Bank proposes a ground-breaking presentation-layer protocol that deterministically verifies numeric outputs, directly addressing LLM “numeric hallucination.”LLMs, the push for robust UE is evident in perception and control systems. In computer vision, “SiLVR: Scalable Lidar-Visual Radiance Field Reconstruction with Uncertainty Quantification” combines lidar and visual data to not only reconstruct 3D scenes but also quantify the uncertainty of these reconstructions, crucial for robotics. For 3D geometry, “BayesSDF: Surface-Based Laplacian Uncertainty Estimation for 3D Geometry with Neural Signed Distance Fields” by Rushil Desai from Berkeley Artificial Intelligence Research offers surface-aware uncertainty estimates. In robotics, “SVN-ICP: Uncertainty Estimation of ICP-based LiDAR Odometry using Stein Variational Newton” improves LiDAR odometry, enhancing localization reliability for navigation. Meanwhile, “Uncertainty Aware-Predictive Control Barrier Functions: Safer Human Robot Interaction through Probabilistic Motion Forecasting” by Lorenzo Busellato et al. at the University of Verona dynamically adjusts safety margins for human-robot interaction based on predicted human motion uncertainty, making collaborative robots safer and more fluid.### Under the Hood: Models, Datasets, & Benchmarksof these advancements are propelled by new methodologies and robust evaluation frameworks:Variance-Gated Distributions: A novel approach for decomposing aleatoric and epistemic uncertainty, demonstrated by H. Martin Gillis et al. to detect ensemble diversity collapse.Graph-based Uncertainty Frameworks: GENUINE utilizes dependency parse trees and adaptive graph pooling to capture semantic and structural relationships for LLM uncertainty. Code: https://github.com/ODYSSEYWT/GUQToken-Level Uncertainty (TokUR): Tunyu Zhang et al. introduce low-rank random weight perturbations during LLM decoding to estimate uncertainty at the token level, crucial for mathematical reasoning.Deep Evidential Segmentation (DEviS): For medical imaging, this method (available at https://github.com/Cocofeat/DEviS) models evidential calibrated uncertainty, showing robustness in semi-supervised settings with noisy data, as explored in “Towards Reliable Medical Image Segmentation by Modeling Evidential Calibrated Uncertainty”.E3DPC-GZSL: This novel method for generalized zero-shot learning on point clouds addresses overconfidence bias through dynamic calibration using uncertainty, with code at https://github.com/Hsgalaxy/Kim/E3DPC-GZSL from Hyeonseok Kim et al.HalluEntity Dataset: Introduced in “HalluEntity: Benchmarking and Understanding Entity-Level Hallucination Detection” by Min-Hsuan Yeh et al., this new benchmark at https://huggingface.co/datasets/samuelyeh/HalluEntity evaluates entity-level hallucination detection across 17 LLMs.BayesSDF: A probabilistic framework from Rushil Desai for surface-aligned uncertainty in neural implicit 3D representations, leveraging Laplace approximation and Hessian-based curvature estimation.EnergyPatchTST: For energy forecasting, this model from Wei Li et al. enhances the PatchTST architecture with multi-scale and probabilistic forecasting modules for improved accuracy and reliability.UGD-IML: Yachun Mi et al. introduce a unified generative diffusion-based framework for image manipulation localization, leveraging class embeddings and parameter sharing to reduce reliance on large annotated datasets.### Impact & The Road Aheadcollective advancements in uncertainty estimation are rapidly paving the way for more robust, safe, and interpretable AI systems. From enhancing clinical decision-making with calibrated medical image segmentation and clinical QA (“Benchmarking Uncertainty and its Disentanglement in multi-label Chest X-Ray Classification” and “Mind the Gap: Benchmarking LLM Uncertainty, Discrimination, and Calibration in Specialty-Aware Clinical QA”) to enabling safer human-robot interaction and reliable autonomous driving (“Uncertainty Aware-Predictive Control Barrier Functions”, “ExtraGS: Geometric-Aware Trajectory Extrapolation with Uncertainty-Guided Generative Priors”, and “Open-Set LiDAR Panoptic Segmentation Guided by Uncertainty-Aware Learning”), the practical implications are vast.areas for continued exploration include developing more efficient and scalable uncertainty quantification methods for ever-larger models, particularly LLMs. The understanding of how LLMs form their “beliefs” and why they might hallucinate, as investigated by papers like “Semantic Energy: Detecting LLM Hallucination Beyond Entropy”, will be crucial. Furthermore, integrating uncertainty into human-AI collaborative decision-making, as highlighted in “Large Language Models Must Be Taught to Know What They Don’t Know”, will define the next generation of intelligent tools.journey toward truly intelligent and trustworthy AI is deeply intertwined with our ability to quantify and manage uncertainty. These papers underscore a vibrant, rapidly evolving field where theoretical breakthroughs and practical innovations are continually pushing the boundaries of what’s possible, promising a future where AI not only performs brilliantly but also understands its own limitations.