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Ming-Xuan Li

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Preprint Aug 2026

Never Stop Speaking: a Denial-of-Service Attack on End-to-End Speech Language Models

The perturbation-based DoS attack targeting E2E speech models is proposed, formulated as a composite optimization objective that jointly suppresses EOS generation, encourages prolonged decoding, and largely preserves semantic consistency by integrating weighted EOS loss, top-k logit loss, length loss, and semantic alig...

Shuo Cheng, Kunlan Xiang, Ming-Xuan Li et al. · 0 citations

#artificial intelligence Preprint Aug 2026

Emergent Misalignment Is Not Magical

The EM generalization metric is extended from a scalar distance to a dataset-specific generalization direction, which robustly predicts EM models'evilness under semantics-preserving prompt perturbations including appending random tokens and paraphrasing, where other methods do not reliably generalize.

Ming-Xuan Li, Qirun Dai, Hesi Wang et al. · 0 citations