Lévy Attention replaces softmax with a probabilistic formulation that automatically outputs calibrated uncertainty estimates alongside predictions—no extra parameters or passes needed, making it practical for high-stakes applications like patient risk ranking.
This paper introduces Lévy Attention, a new attention mechanism for time series that predicts both values and uncertainty in a single pass. Instead of using softmax, it formulates attention as a stochastic integral over a Poisson random measure, which naturally captures prediction confidence through two signals: disagreement (value spread) and evidence (compatibility mass).