Following this puts new work involving it at the top of your briefing, with a note saying why it is there. Links are taken from the source record, never inferred.
An unrefereed arXiv preprint describing a novel machine learning framework for graph anomaly detection with computational validation across benchmarks, but lacking peer review and clinical or real-world outcome data.
This is an unsupervised exploratory analysis characterizing behavioral similarities across language models using multiple distance metrics, without a clinical endpoint or validated predictive outcome; it establishes methodology and describes phenomena but does not establish causal mechanisms or clinical utility.