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Financial LLMs can now be rigorously evaluated against targeted risks, reducing critical false negatives in safety assessments from 28 to 12.
A unified action embedding space across diverse humanoid robots boosts performance and interpretability, transforming how we approach robot policy learning.
Mapping LLM attack strategies onto a multiplex network reveals interpretable vulnerability clusters and dramatically improves red teaming efficiency.
RIS models struggle with motion-based queries, but a new data augmentation and contrastive learning approach closes the gap without sacrificing performance on appearance-based descriptions.
