AI RESEARCH
Conditional Coverage Diagnostics for Conformal Prediction
arXiv CS.AI
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ArXi:2512.11779v2 Announce Type: replace-cross Evaluating conditional coverage remains one of the most persistent challenges in assessing the reliability of predictive systems. Although conformal methods can give guarantees on marginal coverage, no method can guarantee to produce sets with correct conditional coverage, leaving practitioners without a clear way to interpret local deviations. To overcome sample-inefficiency and overfitting issues of existing metrics, we cast conditional coverage estimation as a classification problem.