2 articles tagged with #conformal-inference. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.
CARE introduces a conformal safety layer that detects hallucinations and omissions in LLM-generated medical summaries without retraining. The system provides formal, distribution-free guarantees for controlling safety risks while reducing clinician review burden by up to 5x compared to alternative methods.
Researchers introduce GCOS, a training-time regularization framework that improves deep neural networks' ability to detect out-of-distribution samples by synthesizing realistic outliers in feature space while respecting the geometric structure of in-distribution data. The method combines manifold-aware outlier generation with contrastive learning and extends to conformal inference for statistically valid uncertainty quantification.
