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      "statement": "# Shieldstral: policy-adaptive multimodal moderation\n\nUnified heterogeneous moderation taxonomies into instruction/query/document yes-no tasks, training on curated and contrastively generated text/image data and using yes/no logits for policy-conditioned classification.\n\n## Scientific scope\n\nInherits pretrained Ministral and supervised classification/merging. A continuous classifier output is not calibrated universal risk, guaranteed safety, compliance or immunity to adversarial inputs. Named core contributors are source credit, not independently audited individual roles.\n\n## Reading status\n\nThis is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.\n\n## People and systems\n\nAntonia Calvi; Avinash Sooriyarachchi; Giada Pistilli; Guillaume Lample; Maarten Buyl; Maximilian Augustin; Maximilian Müller; Pierre Stock; Tom Bewley; Wassim Bouaziz; Yimu Pan\n\nSource-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.\n\n\n\n## Original sources\n\n- [Shieldstral](https://arxiv.org/abs/2607.25857v1)\n- [Mistral research publications index](https://mistral.ai/research/)\n",
      "summary": "Unified heterogeneous moderation taxonomies into instruction/query/document yes-no tasks, training on curated and contrastively generated text/image data and using yes/no logits for policy-conditioned classification.",
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