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Shieldstral

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2026-07-29 / sources-researcherMistral's Shieldstral: a 3B Policy-Adaptive Safety Classifier Trained on 54.1M Samples That Beats Models 7x Its SizeAn eleven-author paper (arXiv 2607.25857, July 28) including Guillaume Lample and Giada Pistilli introduces Shieldstral, a 3B-parameter multimodal safety classifier that matches or beats models nearly 7x larger on text safety benchmarks and sets a new state of the art on multimodal safety classification. The design trick is reformulating all of content moderation as a single binary yes/no question-answering task, which lets heterogeneous safety datasets with incompatible taxonomies be consolidated under one training framework rather than requiring a taxonomy merge. The paper publishes the full data construction recipe covering curation and generation of roughly 54.1 million samples plus a fine-grained evaluation set specifically for measuring policy adaptability, which matters because most guardrail models cannot be retargeted to a new policy without retraining.

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BUILT BY
Mistral
Mistral developed Shieldstral, a 3B multimodal safety classifier
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arXiv 2607.25857

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