SOURCE-LINKED INTELLIGENCE

Conformal Robustness in Prediction-Driven Decision-Making

arXiv · AI, language, vision and robotics · article · Sep 19, 2026 · UTC

Modern prediction-driven decision systems often rely on black-box predictors, but a point forecast alone does not provide the uncertainty scale required for robust downstream decision-making. We build a score-calibrated robustness framework that converts any fixed point predictor into a decision-relevant uncertainty representation through distribution-free conformal calibration. We use the conformal score, rather than a particular uncertainty set, as the primitive unit of robustness. The same score determines coverage-calibrated uncertainty sets for reliability-based robust optimization and no

Read original source ↗ Open in workspace

recordType

paper

region

Global