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Introduces ELVAE, a VAE with evidential learning that models latent coordinates with normal-inverse-gamma posteriors to obtain explicit uncertainty estimates. Experiments on MNIST show that within-class uncertainty ranking can stratify synthetic sample reliability and stress-testing, though results require class-wise normalization and vary across seeds.