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Results show that the effectiveness of TabPFN is highly problem dependent, and it cannot replace conventional surrogates universally, and it should be adopted selectively according to data availability, landscape complexity, search space characteristics, and its role within the algorithm.
Forget hand-tuning: AutoSG automatically conjures high-performing solvers for expensive optimization problems directly from task prompts.