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Under review as a conference paper at ICLR 2027

FACET: Geometry and Guarantees at the Conformal Prediction-Box Interface

Abstract

Multivariate conformal methods are calibrated and compared as prediction regions, while many downstream interfaces expose only coordinatewise lower and upper bounds: the axis-aligned box of that region. These are different prediction tasks. We characterize the resulting interface. For any nested box family, exact box membership depends only on its coordinate-envelope path, so two arbitrarily different native regions with the same envelopes become the identical box predictor after exact recalibration; for homothetic families this reduces the geometry to its axial support vector, and for Mahalanobis ellipsoids to . Containing conversions preserve coverage but can be asymptotically vacuous: the box of a calibrated Gaussian ellipsoid has coverage tending to one and is wider than a valid centered box. Finite non-containing conversions admit no distribution-free transfer and can instead lose nearly all coverage. We test these predictions in a locked, external-only benchmark on high-dimensional forecasting panels. At the native threshold, containing conversions add - percentage points of coverage, whereas a rank-truncated non-containing conversion loses points in median. The box-task winner differs from the native-region winner in of eligible cells. Exact online box calibration reduces median absolute coverage error from to percentage points at a median width ratio of , and equal envelope paths yield equal boxes to . The strongest external geometry remains wider in median than a validation-selected coordinatewise-support box. On four untouched panels, time-varying coordinate supports reduce width by over static supports, while pooled support models win of comparisons. The result is a task-faithful evaluation principle and a simple prescription: predict coordinate supports, then calibrate exact membership in the box that is actually delivered.

open until 14 Dec 2026

est. 32% chance this paper gets accepted at ICLR 2027.

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