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This paper introduces Minimalist Genetic Programming (MGP), a novel algorithm that replaces evolution with a syntactic derivation process inspired by the Minimalist Program from linguistics, using a MERGE operator to construct symbolic expressions. MGP consistently finds exact ground truth models on symbolic regression tasks where standard GP struggles due to bloat.
This paper investigates whether large language models encode syntactic abstractions like phase boundaries that are not captured by Universal Dependencies, using structural probes on wh-movement stimuli with invariant UD distances, finding evidence across 13 LLMs for phase-structure representations that are causally active.