Introduces epistemic calibration as a strictly stronger criterion than classical calibration for second-order models and proposes the Expected Epistemic Calibration Error (EECE) as a consistent estimator of the True Epistemic Calibration Error (TECE).
Quarterly Journal of the Royal Meteorological Society , volume =
2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
UNVERDICTED 2representative citing papers
CRAFT is a Pareto-front prompt optimizer that allocates scarce LLM validation calls to candidates near the current front using accuracy- and cost-oriented generators plus NSGA-II retention.
citing papers explorer
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Can we trust our models? Epistemic calibration in second-order classification
Introduces epistemic calibration as a strictly stronger criterion than classical calibration for second-order models and proposes the Expected Epistemic Calibration Error (EECE) as a consistent estimator of the True Epistemic Calibration Error (TECE).
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CRAFT: Cost-aware Refinement And Front-aware Tuning of Prompts
CRAFT is a Pareto-front prompt optimizer that allocates scarce LLM validation calls to candidates near the current front using accuracy- and cost-oriented generators plus NSGA-II retention.