Uncertainty Quantification (UQ) is critical for detecting hallucinations in black-box Large Vision-Language Models (LVLMs). However, prevailing methods like Discrete Semantic Entropy (DSE) are unreliable, as their scores are primarily dominated by the number of semantic clusters. This renders them incapable of distinguishing between benign semantic ambiguity (varied but coherent responses) and severe belief conflict (contradictory responses). We address this limitation by proposing a novel framework rooted in Dempster-Shafer theory of evidence, built on the premise that not all inconsistency is equal. Our method decomposes uncertainty into two complementary metrics: Belief Divergence, which quantifies ambiguity by measuring the separation between viewpoints, and Belief Conflict, which captures direct logical contradictions. Extensive experiments demonstrate that our framework provides a more reliable measure of uncertainty.

Trusted multi-view classification (TMC) aims to improve prediction reliability by integrating evidence from multiple views. Existing TMC methods extract evidence from single view and use a regularization term to shape the evidence distribution. However, existing methods typically enforce a uniform regularization objective across all views, overlooking critical view-specific biases: intra-view class ambiguity caused by confusable features and inter-view quality disparities reflected in evidence uncertainty. To address these issues, we propose an adaptive regularization strategy that enhances robustness on two levels. At the intra-view level, it quantifies feature ambiguity to apply targeted relaxation to confusable classes, preventing over-penalization of inherent uncertainty. At the inter-view level, it evaluates relative view quality to impose stronger constraints on unreliable views and suppress noise from low-quality ones. Extensive experiments across multiple benchmarks demonstrate the superiority and reliability of the proposed method.

Active retrieval-augmented generation (RAG) triggers external knowledge retrieval during generation based on model-side uncertainty signals to support knowledge-intensive, multi-hop reasoning. However, existing methods often retrieve only after producing a complete answer, failing to surface and fill information gaps in time; moreover, relying on a single internal signal as the trigger cannot adequately capture the multifaceted nature of uncertainty. We therefore propose a conflict-aware active RAG framework. We first decompose complex questions into a sequence of step-level sub-problems. At each step, we quantify local distributional uncertainty via a sliding-window peak token entropy, and estimate cross-sample consensus via the variation ratio computed over multiple Monte Carlo samples. After calibrating both signals onto a probabilistic scale, we quantify their conflict using a symmetric, bounded divergence over Bernoulli parameters, and fuse the three quantities into a single uncertainty score that gates retrieval. Experiments demonstrate the effectiveness of our framework.

Diffusion-Weighted Imaging (DWI) is essential in prostate cancer diagnosis, yet multi-b-value acquisition increases scan time and susceptibility to motion artifacts. Traditional interpolation methods fail to model complex tissue diffusion, and existing deep learning models often compromise lesion contrast and structural fidelity. We propose a novel framework that integrates latent diffusion models with a mask-guided lesionpreserving (MGLP) mechanism to synthesize low-b-value DWI from high-b-value inputs. The model leverages VQ-VAE-based discrete latent representations for feature alignment and employs lesion masks as spatial priors to preserve diagnostic regions during diffusion. A dynamic feature fusion strategy further enhances anatomical-functional consistency. Experiments on clinical datasets demonstrate superior performance over state-of-the-art methods in PSNR, SSIM, and especially lesion-specific SSIMROI metrics. Our approach effectively reduces the need for multi-b-value scans without sacrificing diagnostic reliability, showing potential as a supporting approach to reduce b-value scanning requirements in prostate MRI.

Cross-modal retrieval aims to match videos and texts by mapping them into a shared feature space. Most existing approaches achieve alignment through contrastive learning based on one-to-one supervised pairs. However, these methods rely too much on supervised signals and do not fully use the unsupervised semantic relationships within each modality. As a result, samples that are semantically similar may be spread in the shared space, which hurts retrieval performance. To solve this problem, we propose a method called Leveraging Intra-Modal Consistency for Cross-Modal Alignment and Retrieval (LICA). Our method introduces a consistency constraint between intra-modal similarities and cross-modal similarity distributions. In this way, samples that are close in meaning stay closer together in the shared space. Experiments on standard text-video retrieval benchmarks show that LICA helps optimize the distribution of the cross-modal feature space and improves retrieval accuracy.

Trustworthy multi-view classification is essential for safety-critical applications, yet existing methods often fail when view quality degrades at test time due to noise or domain shifts. We propose a novel test-time adaptive fusion framework that leverages Conformal Prediction (CP) to transform pre-trained single-view classifier outputs into robust evidence representations. By integrating CP with Dempster-Shafer Theory, our approach dynamically generates well-calibrated evidence for each view, enabling reliable fusion under arbitrary view conditions without retraining. Extensive experiments demonstrate that our method achieves competitive performance on clean data and significantly outperforms prior methods in the presence of view corruption, offering a practical solution for robust multi-view classification in real-world scenarios.

Gradient-based methods provide a principled framework for Active Learning (AL), effectively quantifying a sample’s informativeness through the uncertainty captured from the model’s internal dynamics. However, their effectiveness is often hindered by the failure to disentangle epistemic and aleatoric uncertainty. This can lead to the suboptimal selection of noisy outliers over truly informative samples. To address this, we propose Dual-View Gradient Probes (DVGradProb), a framework that disentangles these uncertainties by probing gradients from two complementary spaces. Specifically, epistemic uncertainty is captured from the parameter space and introduce a novel metric for aleatoric uncertainty from the feature space. These metrics are integrated into a ratio-based acquisition function designed to filter out noisy outliers while prioritizing truly informative samples, those with high epistemic but low aleatoric uncertainty. Experiments on MNIST, CIFAR-10, and SVHN demonstrate that DVGradProb robustly outperforms baselines, validating the effectiveness of this uncertainty disentanglement strategy.

Effective exploration remains a core challenge in deep reinforcement learning, particularly in sparse-reward environments where agents receive limited feedback and traditional value-based methods often suffer from overestimation bias. Standard approaches, such as Deep Q-Networks, generate point estimates of Q-values and cannot represent the epistemic uncertainty introduced by limited and sparse interactions, which can lead to inefficient exploration and suboptimal policy learning. To address these limitations, we propose an uncertainty-aware exploration framework based on Dempster-Shafer theory to improve policy learning in sparse-reward scenarios. We model the uncertainty of the Q-value by constructing belief-plausibility intervals. These intervals are derived through basic probability assignments over action subsets, which models the confidence of value estimates. A credibility-driven decision mechanism is introduced to incorporate these intervals into action selection. Furthermore, we design a fusion strategy that combines the uncertainty bounds with traditional Q-values to form a unified hybrid value representation, which is used for both policy updates and target computation. Experimental results on multiple discrete control tasks with sparse reward demonstrate that our method significantly improves both the convergence speed and the exploration efficiency compared to standard baselines.

Hierarchical feature selection is considered an effective technique to reduce the dimensionality of data with complex hierarchical label structures. Incorrect labels are a common and challenging issue in complex hierarchical data. However, the existing hierarchical methods often struggle to dynamically adapt to label noise and lack the flexibility to adjust sample weights. Therefore, their effectiveness in managing complex data with many classes and mitigating label noise is significantly limited. To address these issues, in this article, an adaptive sample weighting-based progressive hierarchical feature selection (PHFS) method was proposed, which dynamically adjusts the sample weights to focus on high-quality data. PHFS integrates progressive sample selection and hierarchical feature selection into a unified framework, thus enhancing its effectiveness in reducing the impact of label noise and achieving optimal performance. The progressive selection process is divided into initial and subsequent stages, focusing on correct and incorrect samples. In the initial stage, PHFS selects valuable and correct samples based on the adaptive weights calculated through hierarchical classification feedback, maximizing the guiding effect of the correctly labeled examples. In the subsequent stages, PHFS uses matrix factorization to preserve the structure of the correctly labeled samples, preventing the forgetting of the early selected samples and minimizing the negative impact of the mislabelled samples. The superiority of PHFS over 13 state-of-the-art methods was demonstrated by performing extensive experiments on eight real-world datasets, highlighting its effectiveness in reducing label noise and achieving optimal performance.