Harnessing Textual Refusal Directions for Multimodal Safety.

arXiv:2606.31876v3 Announce Type: replace Abstract: Safety in Multimodal Large Language Models (MLLMs) depends on unsafe multimodal data for post-training alignment or activation steering. However, such data is hard to collect, must be gathered anew for each m