Shieldstral
Authors: name: Antonia Calvi, name: Avinash Sooriyarachchi, name: Giada Pistilli, name: Guillaume Lample, name: Maarten Buyl, name: Maximilian Augustin, name: Maximilian Müller, name: Pierre Stock, name: Tom Bewley, name: Wassim Bouaziz, name: Yimu Pan
Organization: Mistral AI_

We introduce Shieldstral, a 3B-parameter policy-adaptive multimodal safety classifier that matches or outperforms models nearly 7times its size on text safety benchmarks and sets a new state of the art on multimodal safety classification. Shieldstral formulates content moderation as a binary question-answering task. This simple formulation unifies diverse moderation tasks into a single yes/no problem, enabling heterogeneous safety datasets with divergent taxonomies to be consolidated under one training framework. We present the data construction recipe, covering curation and generation of approximately 54.1M samples and a fine-grained evaluation set to evaluate policy adaptability. Together, these enable a small adaptive model to match or outperform much larger models.