Proximity-Based Evidence Retrieval for Uncertainty-Aware Neural Networks

Abstract--This work proposes an evidence-retrieval mechanism for uncertainty-aware decision-making that replaces a single global cutoff with an evidence-conditioned, instance-adaptive criterion. For each test instance, proximal exemplars are retrieved in an embedding space; their predictive distributions are fused via Dempster-Shafer theory. Because the supporting evidences are explicit, decisions are transparent and auditable. Experiments on CIF AR-10/100 with BiT and ViT backbones show higher or comparable uncertainty-aware performance with materially fewer confidently incorrect outcomes and a sustainable review load compared with applying threshold on prediction entropy. Notably, only a few evidences are sufficient to realize these gains; increasing the evidence set yields only modest changes. These results indicate that evidence-conditioned tagging provides a more reliable and interpretable alternative to fixed prediction entropy thresholds for operational uncertainty-aware decision-making. N the landscape of modern artificial intelligence (AI), the pursuit of predictive accuracy has driven neural networks (NNs) to achieve superhuman performance across a multitude of domains. However, in many real-world applications, particularly those with high stakes, a correct prediction is only part of the requirement. This is crucial because most conventional machine learning (ML) models issue single-point predictions. In particular, NNs typically output class probabilities through a softmax layer, which represent only a deterministic point estimate conditioned on the model's fixed parameters and training data. These probabilities reflect the model's relative preference among classes given its fixed state after training. High probability does not necessarily imply that the prediction is reliable. This is where uncertainty quantification (UQ) methods emerges as a critical paradigm.