Adaptive Steering and Remasking for Safe Generation in Diffusion Language Models
Diffusion Language Models generate text by iteratively denoising data, a process vulnerable to harmful tokens propagating across steps. The framework introduces an inference-time defense using early-step adaptive steering to guide the trajectory toward safe regions and token remasking to correct harmful tokens locally. This method successfully reduces jailbreak attack success rates while maintaining high generation quality.