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AI Summary: MIT researchers have developed a novel approach to enhance Bayesian optimization by integrating a tabular foundation model as the surrogate model, addressing challenges in high-dimensional engineering problems. This method significantly accelerates the search for optimal solutions, achieving results 10 to 100 times faster than traditional techniques in benchmarks such as power-system optimization. The foundation model, pre-trained on extensive tabular data, eliminates the need for constant retraining, thereby improving efficiency and adaptability across various applications, including materials development and drug discovery. The findings will be presented at the International Conference on Learning Representations.