ACCURACY WITHOUT OPACITY: MONOTONIC TRANSFORMER ADDITIVE MODELS

Transformer-based models achieve strong performance on tabular data by learning complex feature interactions, but they remain black boxes and offer no guarantee that learned relationships respect domain-specific monotonicity constraints. Interpretable additive and monotonic models, conversely, provide transparent feature effects but lack the expressiveness to capture rich interactions. To bridge this gap, we introduce the Monotonic Transformer Additive Model (MTAM), which decomposes the model output into additive main effects and feature interactions learned by a Differential Transformer. MTAM addresses monotonicity at three levels: (a) the main effects are monotonic by construction; (b) the Differential Transformer is adapted to reduce sources of non-monotonicity in the interactions; and (c) an augmented Lagrangian scheme imposes monotonicity of the full model as a training constraint and supports an identifiable decomposition through marginal clarity. Across six regression and classification tasks, MTAM narrows the gap between interpretable models and unconstrained transformers while keeping monotonicity violations below $3$% on average in public datasets, providing an accurate and transparent alternative to black-box transformers.