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MOLRN-BO is introduced, which combines a regularized shared-spectral surrogate with objective-specific residuals, prequential mean correction and tempered covariance scaling, and Pareto-local qLogEHVI optimization with periodic global search.
This work compares role specifications in which the language model acts as equation author, candidate decider or search controller, alongside end-to-end language-model and purely numerical baselines and suggests that language models should shape hypothesis exploration rather than decide which equations survive.
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