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Calibration-Aware Uncertainty Cascades is proposed, a simple post-hoc framework that independently calibrates each model's confidence and selects deployment policies using validation data and shows theoretically that calibration gives confidence thresholds an explicit selective-risk interpretation, whereas uncalibrated scores offer no comparable reliability guarantee.
Allocating additional computation during decision-making boosts long-horizon robot manipulation success by significantly enhancing subtask prediction accuracy.
