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This work evaluates three evolutionary search strategies on five optimization tasks, commonly used by papers in the genre to report results, and suggests that the best way to split a fixed budget between more seeds and more iterations changes with the strategy, the task, and the total budget.
Spotlights is presented, a system that performs optimization-opportunity discovery as a distinct and empirically evaluable step between a broad engineering objective and subsequent implementation and validation.
A new 2B parameter vision-language model, Granite Vision, rivals larger models on visual document understanding tasks while offering a transparent and commercially-friendly open-source license.
