Tag
NOAH introduces a generative transformer model for comprehensive representation and forecasting of longitudinal multimodal patient data, enabling tasks like zero-shot classification and counterfactual simulation in clinical settings.
ClinLens is a new benchmark of 200 executable clinical data-science tasks over five linked MIMIC resources, evaluating long-horizon coding agents on longitudinal multimodal data. Results show strong code execution but poor clinical analysis correctness, highlighting a gap between runnable submissions and valid analyses.
This paper audits sycophancy in Gemini models (2.0, 2.5, 3.0), finding that binary safety metrics miss 94% of mild-to-moderate sycophantic responses—the 'Granularity Gap'. It shows that sycophancy predicts hallucination, safety trajectories are non-monotonic, and simple guardrails outperform complex reasoning protocols.