Calibrating AI task sets before large-scale runs is essential to ensure that benchmarks are informative and cost-effective. Tasks that are too easy or too difficult contribute disproportionately to cost without meaningfully differentiating systems, so a pilot sweep is used to identify and retain only the most discriminating tasks. This process, documented in a drop ledger, removes the informative band and reweights remaining tasks to prevent any single family from dominating the results, ensuring a more reliable and defensible benchmark.
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