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LLM test-time sharpening via power sampling doesn't actually target the intended power distribution; its gains come from simple sequence selection.
Learnable shape parameterizations unlock superior sample efficiency and design exploration in Bayesian optimization, outperforming hand-crafted approaches across diverse engineering domains.
