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This paper introduces Enlightenment, a training-free post-tuning method for large-scale models that modifies shortcuts in key modules without weight updates, achieving sudden capability boosts. It demonstrates effectiveness across LLMs and VLMs via attention head-mixing and scalar-modulated residual connections.