做LLM安全对齐的团队终于有了针对扩散模型的专用监控方案——$D^2$-Monitor用轻量探针+动态路由解决了资源效率问题，做模型部署和红队测试的可以直接参考论文中的实现思路。
扩散大语言模型（D-LLMs）通过多步去噪生成文本，其中间隐藏状态包含安全相关信息，但安全监控研究尚属空白。本文发现，中间隐藏状态反复接近分类器决策边界的“安全犹豫”信号能有效预测轻量级探针的失败。基于此，提出$D^2$-Monitor，采用轻量探针持续监控并估计犹豫程度，当犹豫超过阈值时激活更强但更重的探针，实现测试时资源动态分配。在WildguardMix、ToxicChat、OpenAI-Moderation三个数据集上，对四种D-LLMs评估，$D^2$-Monitor以≤0.85M参数取得最优性能，并在效果与效率间达到最佳平衡。
$D^2$-Monitor: Dynamic Safety Monitoring for Diffusion LLMs via Hesitation-Aware Routing
Despite the emergence of diffusion large language models (D-LLMs) as an alternative to autoregressive large language models (AR-LLMs), safety monitoring for D-LLMs remains largely unexplored. Unlike AR-LLMs, D-LLMs generate text through a multi-step denoising process, exposing intermediate hidden representations that may contain safety-relevant information unavailable in standard single-step monitoring setups. Motivated by the suitability of lightweight probes for always-on monitoring, we analyze which trajectory-level signals best indicate when such probes are likely to struggle. We find that the most informative signal is safety hesitation: intermediate hidden states repeatedly falling within a small margin of the probe's decision boundary. The number of such hesitation steps in D-LLM's trajectory predicts probe failure effectively, providing a proxy of sample difficulty. Building on this analysis, we propose $D^2$-Monitor, a bi-level safety monitor for D-LLMs. $D^2$-Monitor adopts a lightweight probe as an always-on monitor to jointly estimate hesitation and perform base classification. When the hesitation level exceeds a threshold, a more expressive but computationally heavier probe is activated. This dynamic routing mechanism allocates monitoring resources efficiently at test time. Evaluated on 3 datasets (WildguardMix, ToxicChat, OpenAI-Moderation) across 4 D-LLMs, $D^2$-Monitor achieves state-of-the-art performance with a compact parameter footprint ($\leq$ 0.85M parameters), and exhibits the best trade-off between effectiveness and efficiency relative to 8 baselines.