Calibrating Scientific Foundation Models with Inference-Time Stochastic Attention
Provides a practical, efficient calibration method for high-stakes scientific applications where uncertainty quantification is critical.
Transformer-based scientific foundation models lack calibrated uncertainty. The authors propose Stochastic Attention, a lightweight inference-time modification that achieves the strongest native calibration and sharpest prediction intervals with minutes of post-hoc tuning versus days of retraining.
Transformer-based scientific foundation models are increasingly deployed in high-stakes settings, but current architectures give deterministic outputs and provide limited support for calibrated predictive uncertainty. We propose Stochastic Attention, a lightweight inference-time modification that randomizes attention by replacing softmax weights with normalized multinomial samples controlled by a single concentration parameter, and produces predictive ensembles without retraining. To set this parameter, we introduce a calibration objective that matches the stochastic attention output with the target, yielding an efficient univariate post-hoc tuning problem. We evaluate this mechanism on two scientific foundation models for weather and timeseries forecasting along with an additional regression task. Across benchmarks against uncertainty-aware baselines, we find that Stochastic Attention achieves the strongest native calibration and the sharpest prediction intervals at comparable coverage, while requiring only minutes of post-hoc tuning versus days of retraining for competitive baselines.