Demographic data are often collected across multiple dimensions such as time, age, subnational region, and sex. Modeling such data is challenging due to the interplay of high dimensionality, heterogeneity between groups, and stochastic variability within small groups. This paper proposes a Bayesian adaptive tensor smoothing model for multiway demographic counts that represents the latent log-rate array as a sum of rank-one components. Global-local shrinkage priors control the effective rank of the decomposition, the number of modes along which each component varies, and the smoothness that Gaussian process priors impose along ordered modes such as age and time. Posterior inference relies on a data augmentation Markov chain Monte Carlo algorithm that interweaves centered and noncentered parameterizations in every layer of the model to keep mixing satisfactory in large and sparse count arrays. Applied to Swedish county-level and sex-specific mortality from 1969 to 2024, the model recovers stable demographic surfaces and life table quantities from noisy small-population data. In cross-validation exercises, the method outperforms both canonical demographic benchmarks and existing count tensor factorizations.

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