Diffusion language models denoise a masked canvas, making them a natural setting for selective generation decisions such as answering, waiting, clarifying, or refusing. We introduce TAP-DiLM, which adds a typed policy variable to masked diffusion LMs, and study how this policy should be attached to the denoising process. Across BERT-base, MDLM-OWT, LLaDA-8B, and Dream-7B, we compare shared-vocabulary slots, dedicated slot heads, and CLS-based external heads. The results reveal a clear interface-backbone interaction: dedicated heads provide robust policy learning across scales, while shared-vocabulary heads can be effective when the LM-head geometry is favorable. A simple LM-head sharpness probe predicts when shared-head policy internalization is reliable, giving a practical diagnostic for interface selection. Causal clamp and slot-injection tests show that dedicated policy slots can influence body denoising through feedback, especially at 8B scale. Safety-refusal experiments further show that TAP-DiLM supports competitive typed refusal behavior when trained with diverse unsafe prompts.

Vision foundation models produce features that generalize across visual domains without fine-tuning, yet naively transferring labels through these feature spaces fails under large distribution shifts.We propose SAOT (Semantically Aware Optimal Transport), which learns a transport cost within a fused unbalanced optimal transport formulation for dense label transfer from frozen vision transformer features to new domains.SAOT combines a learnable appearance metric with semantic class-prototype priors, unbalanced transport for partial matching under distribution shift, and a block-sparse solver for tractable inference.We pair this with a two-stage decoder: an MLP trained on SAOT pseudo-labels, then refined via EMA-teacher self-training with class-balanced sampling.On GTA5→Cityscapes with frozen DINOv2 ViT-L/14 features, SAOT+Decoder reaches 25.7% mIoU, a 3.8× improvement over nearest-neighbor transfer (6.7%), without any backbone adaptation.Per-class results show large gains on spatially coherent classes (road 90.3%, car 76.2%, building 71.5%), demonstrating that learned semantic transport costs capture domain-invariant structure even under severe synthetic-to-real shifts. On VOC train→val with frozen ViT-B/16 features, the full pipeline reaches 47.5% mIoU, indicating that the approach extends beyond synthetic-to-real adaptation.