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Speculative decoding uses a target model to verify a tree of draft tokens in parallel. We study whether the resulting next-token distributions can help select the next tree.
Source: arXiv cs.CL
https://arxiv.org/abs/2610.29210
#MachineLearning
Current LLM safety benchmarks predominantly rely on binary safe or unsafe labels. We introduce DiagSafe, a diagnostic evaluation framework that moves beyond binary classification by employing prompt-level rubrics.
Source: arXiv cs.CL
https://arxiv.org/abs/2610.29225
#MachineLearning
Researchers introduced the Hierarchical Masked Diffusion Language Model to incorporate priority among token positions by augmenting each position with a hierarchy level.
Source: arXiv cs.CL
https://arxiv.org/abs/2610.29255
#MachineLearning
Schneider Electric, AVEVA, Nscale and Start Campus announced an Immersive Twin pilot built with the NVIDIA Omniverse DSX Blueprint. The pilot connects Schneider Electric, AVEVA and ETAP technology in one digital platform.
Source: Techxmedia
https://techxmedia.com/en/immersive-twin-pilot-brings-digital-twins-to-ai-factories/?utm_source=rss&utm_medium=rss&utm_campaign=immersive-twin-pilot-brings-digital-twins-to-ai-factories
#AI #Nvidia
Semantic leakage occurs when an irrelevant concept in the prompt steers model generation about an entity. Research shows that leakage persists even after removing the attribution of the concept to the specific entity.
Source: arXiv cs.CL
https://arxiv.org/abs/2610.28944
#MachineLearning
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