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Safety-aligned language models often refuse a harmful request stated directly but answer the same request inside a role-play or narrative wrapper. We measure this vulnerability across languages and registers: attack success on Qwen3-1.7B is already 89.4% in English and 93.0% in modern Chinese, and reaches 95.7% in Clas...
GroupMask is proposed, which generates the group selectors of all layers with a lightweight hypernetwork, relaxes them with a Gumbel-Sigmoid parameterization and a straight-through estimator, and learns them through sparsity-budget regularization and self-distillation while keeping the pretrained weights frozen.
MUGEN is proposed, a unified motion--language framework that pays neither cost: no codebook, one draw, and surpasses the discrete-token state of the art on every retrieval and alignment metric on SnapMoGen.
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