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Department of Chemical and Biological Engineering, Princeton University, Princeton, NJ 08540, USA
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Range-aware Bayesian optimization consistently uncovers a broader array of viable designs, outperforming traditional methods in both diversity and validity.
Domain-specific visual tasks get a 13.51% performance boost in LVLMs thanks to a new codebook-anchored adaptation method that decouples vision encoder tuning from the language model.
