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This work runs a preregistered intervention on Spider-Realistic, swapping that database for the benchmark's distilled multi-instance test suite, and finds that every execution-consistency score looks better under the labels of the oracle that built its clusters, in 16 of 16 combinations.
Certification of selective predictors reveals a critical trade-off between coverage and risk that could redefine operational standards in machine learning under covariate shift.