arXiv AI
The Tokens Strike Back: Safety-Aware Denoising with Directional Embedding-based Revision
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arXiv AI
Jun 12
The Safety-Aware Denoiser for Text Diffusion Models
arXiv:2605. 08116v2 Announce Type: replace-cross Abstract: Recent work on text diffusion models offers a promising alternative to autoregressive generation, but controlling their safety remains underexplored.
arXiv Computation and Language
3d ago
Adaptive Steering and Remasking for Safe Generation in Diffusion Language Models
arXiv:2605.13043v3 Announce Type: replace Abstract: Diffusion Language Models(DLMs) provide a promising alternative to autoregressive language models through iterative denoising and bidirectional gen...
arXiv AI
Sep 2
Beyond Token Positions: Safety Alignment Across Denoising Steps in Diffusion Language Models
This paper investigates safety alignment in diffusion large language models (dLLMs), which generate text via iterative denoising instead of left‑to‑right decoding. By tracking token distributions and commitment decisions across denoising steps, the authors find that refusal signals are concentrated early in the denoising process and at leading response positions, and that early committed tokens strongly influence the final safety outcome. They introduce Refusal‑Aware Early Commitment (RAEC), a training‑free decoding method that preserves early refusal signals, and demonstrate that RAEC reduces attack success rates on LLaDA and Dream while largely maintaining utility.
arXiv Computation and Language
Aug 21
Remask, Don't Replace: Token-to-Mask Refinement in Diffusion Language Models
arXiv:2604. 18738v3 Announce Type: replace Abstract: Diffusion language models (dLLMs) generate text through iterative denoising, filling multiple masked positions at each step.
arXiv AI
Jun 10
Attention-Discounted Adaptive Sampler for Masked Diffusion Language Models
arXiv:2606. 10829v1 Announce Type: cross Abstract: Masked diffusion language models can reduce inference steps by revealing multiple tokens per denoising iteration, but this parallelism is fragile: positions that are individually confident may be unsafe to commit together when their predictions are coupled.
arXiv AI
Sep 28
Why Jailbreaks Succeed in Diffusion Language Models: An Energy Landscape Analysis
The paper proposes a framework that explains why jailbreak attacks succeed against diffusion-based large language models (dLLMs) by viewing safety alignment as shaping a denoising energy landscape. It identifies two attack strategies—obscuring the query’s safety disposition at initialization or forcing the denoising path across an energy barrier mid‑trajectory—and introduces three training‑free detection signals that monitor initial safety disposition and kinetic energy in complementary subspaces. Experiments on several dense and sparse dLLMs show that these signals complement each other, and any attack that evades detection also fails to produce harmful content.