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Pre-training boosts CLMs' molecular structure awareness, but surprisingly, even randomly initialized models excel at encoding ring structures from the first layer.
Fork-think reduces token usage by 30% and runtime by 57% while achieving superior reasoning performance, challenging the efficiency of traditional methods.
