Uncertainty Estimation: Navigating the Murky Waters of AI Decision-Making

Latest 50 papers on uncertainty estimation: Nov. 2, 2025

In the rapidly evolving landscape of AI and Machine Learning, simply getting the right answer isn’t enough anymore. Trust, reliability, and transparency are paramount, especially in high-stakes domains like medicine, autonomous driving, and robotics. This is where uncertainty estimation steps in, acting as our compass in the often-murky waters of AI decision-making. Recent research highlights a significant push towards developing more sophisticated and context-aware uncertainty quantification techniques, ensuring that our AI systems not only perform well but also know when they might be wrong.### The Big Ideas & Core Innovationslatest breakthroughs in uncertainty estimation revolve around several key themes: integrating diverse information sources, understanding different types of uncertainty, and making these estimates actionable. Researchers are moving beyond simple confidence scores to provide rich, nuanced insights into model reliability.groundbreaking approach from the University of Southern California and Amazon AGI, presented in their paper “HARMONY: Hidden Activation Representations and Model Output-Aware Uncertainty Estimation for Vision-Language Models“, proposes HARMONY. This unified framework significantly improves uncertainty estimation in Vision-Language Models (VLMs) by combining both hidden activation representations and output token probabilities. This holistic view allows HARMONY to better capture multimodal uncertainty, leading to state-of-the-art results in VQA benchmarks., in the realm of Large Language Models (LLMs), a novel method for efficient semantic uncertainty quantification is introduced by Prescient Design, Genentech and New York University in their paper “Efficient semantic uncertainty quantification in language models via diversity-steered sampling“. This approach leverages diversity-steered sampling to reduce redundant outputs and focus on semantically distinct clusters, thereby efficiently estimating both aleatoric (inherent data noise) and epistemic (model’s lack of knowledge) uncertainties without requiring gradient access to the base model.enhancing LLM reliability, the X-LANCE Lab at Shanghai Jiao Tong University introduces the EKBM framework in “Enhancing LLM Reliability via Explicit Knowledge Boundary Modeling“. This framework employs a fast and slow reasoning system to explicitly model an LLM’s knowledge boundaries, distinguishing between high- and low-confidence outputs for immediate usability in error-sensitive applications.robotics, Henan University of Technology and China Telecom’s “Towards Reliable LLM-based Robot Planning via Combined Uncertainty Estimation” proposes CURE, a framework that decomposes uncertainty into epistemic and intrinsic components. This allows for a more accurate assessment of an LLM-based robot’s confidence in complex tasks like kitchen manipulation, significantly improving reliability and safety.medical imaging, several papers emphasize the critical role of uncertainty. “Uncertainty-Aware Multi-Objective Reinforcement Learning-Guided Diffusion Models for 3D De Novo Molecular Design” by researchers from Western University integrates uncertainty-aware RL with diffusion models to guide molecular design, balancing complex drug-related properties. The CURVAS challenge, detailed in “Calibration and Uncertainty for multiRater Volume Assessment in multiorgan Segmentation (CURVAS) challenge results“, from a large international collaboration, stresses that well-calibrated models handling multi-rater variability produce higher-quality medical image segmentations, reinforcing the need for uncertainty-aware evaluations. Complementing this, Fudan University and the University of Oxford’s “Uncertainty-Supervised Interpretable and Robust Evidential Segmentation” introduces an uncertainty supervision approach that aligns with human reasoning, enhancing interpretability and robustness in medical image segmentation.theoretical advancements are also being made. Northeastern University and Carnegie Mellon University’s “On Uncertainty Calibration for Equivariant Functions” explores how equivariance impacts model calibration, particularly in data-sparse domains, providing theoretical bounds on calibration errors under symmetry constraints. For graph neural networks, Block Inc and the University of California, Los Angeles introduce a novel method in “Uncertainty Estimation on Graphs with Structure Informed Stochastic Partial Differential Equations” that uses SPDEs to account for graph structure and label randomness, improving estimates in low-informativeness scenarios. Furthermore, the paper “Uncertainty in Machine Learning” by authors including Stephen Bates and Kyle Dorman, offers a comprehensive overview of epistemic and aleatoric uncertainty, detailing quantification techniques like Bayesian Neural Networks and conformal prediction to enhance model decision-making.### Under the Hood: Models, Datasets, & Benchmarksinnovations are powered by new models, specialized datasets, and rigorous benchmarks:BELLE (Bayesian Evidential Learning Framework): Introduced by Tsinghua University and Shanghai Artificial Intelligence Laboratory in “Bayesian Speech Synthesizers Can Learn from Multiple Teachers“, BELLE is an Auto-Regressive Text-to-Speech (AR TTS) model leveraging evidential deep learning for continuous-valued Mel-Spectrogram prediction. It utilizes a novel multi-teacher knowledge distillation, achieving competitive results with significantly less (synthetic) training data. Code is available at https://github.com/lifeiteng/vall-e.Centrum (Stochastic Gradient Boosting Ensembles): From the University of California, Berkeley and Stanford University, “Centrum: Model-based Database Auto-tuning with Minimal Distributional Assumptions” introduces this framework for database auto-tuning. It employs SGBEs and distribution-free conformal ensemble methods for superior point and interval estimation, outperforming existing tuners in throughput and latency.HARMONY (Unified UE Framework): Proposed by University of Southern California and Amazon AGI, this framework from “HARMONY: Hidden Activation Representations and Model Output-Aware Uncertainty Estimation for Vision-Language Models” combines hidden activation representations and output token probabilities, showing state-of-the-art results on VQA benchmarks.UQ-SONet (Permutation-Invariant Operator Learning): In “Deep set based operator learning with uncertainty quantification“, researchers from Shanghai Jiao Tong University and Shanghai Normal University propose UQ-SONet, combining set transformer embeddings with conditional variational autoencoders for principled uncertainty quantification in operator learning, especially for sparse and noisy data.DDPN (Deep Double Poisson Network): Presented by Delicious AI and Brigham Young University in “Fully Heteroscedastic Count Regression with Deep Double Poisson Networks“, DDPN is a neural network for count regression that accurately models both aleatoric and epistemic uncertainty, capturing full heteroscedasticity in discrete data. Code is available at https://github.com/delicious-ai/ddpn.ULXMQA & RULX: Introduced by Iowa State University in “Quantifying Uncertainty in Natural Language Explanations of Large Language Models for Question Answering“, this framework provides model-agnostic, post-hoc uncertainty guarantees for natural language explanations generated by LLMs, with RULX handling discrete and token-level noise.MPNP-DDI: From the National University of Defense Technology, “A Multi-Scale Graph Neural Process with Cross-Drug Co-Attention for Drug-Drug Interactions Prediction” introduces MPNP-DDI, a multi-scale graph neural process with cross-drug co-attention and integrated uncertainty estimation for DDI prediction. Code: https://github.com/yzz980314/mpnp-ddi.SpurBreast Dataset: “SpurBreast: A Curated Dataset for Investigating Spurious Correlations in Real-world Breast MRI Classification” provides a new curated dataset for studying spurious correlations in real-world breast MRI data, crucial for developing robust medical AI models.Event-RGB Dataset for Spacecraft Pose Estimation: In “Event-RGB Fusion for Spacecraft Pose Estimation Under Harsh Lighting“, The University of Adelaide publicly releases an optically and temporally aligned dataset to support research in spacecraft navigation under challenging illumination.GeoKnowRAG & OpenEvolve: The Massachusetts Institute of Technology and Stanford University introduce GeoEvolve in “GeoEvolve: Automating Geospatial Model Discovery via Multi-Agent Large Language Models“, a multi-agent LLM framework for geospatial algorithm discovery. It integrates evolutionary code generation with the GeoKnowRAG module and leverages OpenEvolve (code: https://github.com/google/OpenEvolve and https://github.com/google/GeoKnowRAG).CURVAS Challenge: Introduced in “Calibration and Uncertainty for multiRater Volume Assessment in multiorgan Segmentation (CURVAS) challenge results“, this challenge and its associated dataset (https://curvas.grand-challenge.org/) evaluate deep learning models for multi-organ segmentation with a strong focus on calibration and uncertainty under multi-rater variability.RuleNet: From Afeka Academic College of Engineering, “Improving Deep Tabular Learning” introduces RuleNet, a transformer-based model for tabular data with piecewise linear quantile projections and feature masking ensembles for robustness and uncertainty estimation.### Impact & The Road Aheadcollective impact of this research is profound. By providing more reliable uncertainty estimates, AI systems can become more trustworthy, safer, and ultimately more impactful across diverse domains. Imagine autonomous vehicles that confidently navigate complex urban environments, medical diagnostic tools that not only provide predictions but also express their confidence levels to clinicians, or LLMs that can explicitly communicate when they are unsure, reducing hallucinations and improving user trust.road ahead involves further refining these techniques, pushing towards even more granular and interpretable uncertainty quantification. Integrating explainability directly with uncertainty, as discussed in the “Position Paper: Integrating Explainability and Uncertainty Estimation in Medical AI” by John Doe and Jane Smith, will be crucial for building truly transparent and reliable AI systems. Addressing bias effects on uncertainty, as explored by University of Rochester and Johns Hopkins University in “The Role of Model Confidence on Bias Effects in Measured Uncertainties for Vision-Language Models“, will ensure fairness and robustness.emphasis on multi-modal integration, as seen in “Event-RGB Fusion for Spacecraft Pose Estimation Under Harsh Lighting” and “UniFField: A Generalizable Unified Neural Feature Field for Visual, Semantic, and Spatial Uncertainties in Any Scene“, points to a future where AI understands its limitations across different sensory inputs. Furthermore, applying uncertainty-aware methods to areas like robot learning (e.g., “UniTac2Pose: A Unified Approach Learned in Simulation for Category-level Visuotactile In-hand Pose Estimation“) and scientific discovery (e.g., “SimulRAG: Simulator-based RAG for Grounding LLMs in Long-form Scientific QA” by University of California San Diego and Stanford University) will unlock new levels of autonomy and scientific progress.advancements detailed here represent a critical step towards an era of “responsible AI,” where models are not just intelligent but also wise enough to know what they don’t know. This will undoubtedly usher in a new wave of reliable and impactful AI applications that can be trusted to assist and augment human capabilities in ways we are only just beginning to imagine.