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It is found that finishing an experiment and reproducing its signatures are different things, and across four multimodal models, each run ten times per task, it is found that finishing an experiment and reproducing its signatures are different things.
This work introduces a symbolic distillation pipeline that converts trained neural likelihoods into explicit, interpretable expressions optimized for efficient parameter estimation, and uses a recovery-directed objective to guide symbolic regression toward expressions that preserve parameter-recovery accuracy.
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