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This paper frames machine unlearning in LLMs as an asymmetric generalization problem, introduces the SUITE evaluation protocol and training corpus to address under- and over-forgetting, and presents JensUn++, an algorithm achieving the best forget-retain utility trade-off across three LLMs.