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This work proposes RMT-Jailbreak, a general framework that transfers existing autoregressive (AR) jailbreak attacks to dLLMs through their induced harmful responses, revealing strong cross-paradigm transferability of jailbreak attacks.
SCSP is a training-free framework that leverages semantic compression for informative token selection in long-context embedding and can be integrated into both zero-shot and fine-tuned models in a plug-and-play manner, consistently improving their performance.
A Dynamic Distribution-Aware Uncertainty Quantification framework (DDA-UQ) is proposed that shifts the paradigm from static mapping to a dynamic distribution-aware process and significantly outperforms state-of-the-art methods.
This work proposes a structured suffix modeling method that incorporates the decoding results from the previous step into the suffix token representations at the current step, allowing them to carry evolving denoising information across generation steps.
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