Guardrail classifiers receive formal guarantees by certifying convex harmful regions in pre-activation space, exposing safety holes in three toxicity models despite high empirical scores.
AEGIS 2.0: A Diverse AI Safety Dataset and Risks Taxonomy for Alignment of LLM Guardrails
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BELLS-O is the first vendor-neutral operational benchmark comparing specialized guardrails and repurposed frontier LLMs on accuracy, false-positive rates, speed, and monetary cost across 11 harm categories and 13 jailbreak techniques.
Guardian-as-an-Advisor prepends risk labels and explanations from a guardian model to queries, improving LLM safety compliance and reducing over-refusal while adding minimal compute overhead.
NVIDIA releases the Nemotron 3 model family with hybrid Mamba-Transformer architecture, LatentMoE, NVFP4 training, MTP layers, and multi-environment RL post-training for reasoning and agentic tasks.
TWGuard achieves +0.289 F1 improvement and 94.9% false-positive reduction for LLM safety guardrails in the Taiwan linguistic context compared to foundation models and baselines.
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NVIDIA Nemotron 3: Efficient and Open Intelligence
NVIDIA releases the Nemotron 3 model family with hybrid Mamba-Transformer architecture, LatentMoE, NVFP4 training, MTP layers, and multi-environment RL post-training for reasoning and agentic tasks.