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It is found that configuration shift consistently erodes CP validity, often driving empirical coverage below the target, and coverage lower bounds are derived that attribute this loss to a discrepancy between calibration and test score distributions.
Evolutionary Soups, a mixture-of-experts framework for fine-grained generation control, with gating networks trained via an evolutionary algorithm, achieves the best hypervolume, linear utility, and Tchebyshev utility among controllable methods on all tasks.
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