# Conformal Risk Control

Online conformal risk control has assumed each round's loss arrives immediately and intact, yet real systems delay feedback and can track only a limited number of pending predictions, so some feedback is lost for good. CA-OCRC designs the tracking policy and the prediction sets together: each round draws a random proxy delay, is tracked while capacity allows until its feedback arrives or the proxy delay expires, and a two-level inverse probability weighting compensates for lost feedback. Risk bounds for arbitrary and stochastic delays follow, and optimizing the proxy delay distribution shows that capacity growing only logarithmically in the maximum delay suffices to match the convergence rate of OCRC with unlimited feedback.

Online conformal risk control has assumed each round's loss arrives immediately and intact, yet real systems delay feedback and can track only a limited number of pending predictions, so some feedback is lost for good. CA-OCRC designs the tracking policy and the prediction sets together: each round draws a random proxy delay, is tracked while capacity allows until its feedback arrives or the proxy delay expires, and a two-level inverse probability weighting compensates for lost feedback. Risk bounds for arbitrary and stochastic delays follow, and optimizing the proxy delay distribution shows that capacity growing only logarithmically in the maximum delay suffices to match the convergence rate of OCRC with unlimited feedback.

Medical and astronomical images are orders of magnitude larger than natural images, yet tumors and galaxies occupy a tiny fraction of the data, and uniform compression such as quantization or pruning can erode precision exactly there. Conformal Adaptation first screens candidates with a low-resolution model, calibrates the selection threshold with conformal risk control so the discarded cells carry at most a user-set expected fraction of true signal mass, then refines only the selected regions. Across brain-tumor segmentation, head-and-neck segmentation, calcium spike-rate inference and galaxy flux super-resolution, the pipeline cuts computation while keeping accuracy close, and VISTA3D reaches an 82.10x FLOPs speedup in CPU-only inference.

Medical and astronomical images are orders of magnitude larger than natural images, yet tumors and galaxies occupy a tiny fraction of the data, and uniform compression such as quantization or pruning can erode precision exactly there. Conformal Adaptation first screens candidates with a low-resolution model, calibrates the selection threshold with conformal risk control so the discarded cells carry at most a user-set expected fraction of true signal mass, then refines only the selected regions. Across brain-tumor segmentation, head-and-neck segmentation, calcium spike-rate inference and galaxy flux super-resolution, the pipeline cuts computation while keeping accuracy close, and VISTA3D reaches an 82.10x FLOPs speedup in CPU-only inference.