We present the performance of Machine Learning–based anomaly detection techniques for extracting potential new physics phenomena in a model-agnostic way with the CMS Experiment at the Large Hadron Collider. We introduce five distinct outlier detection or density estimation techniques, namely CWoLa, Tag N’ Train, CATHODE, QUAK, and QR-VAE, tailored for the identification of anoma- lous jets originating from the decay of unknown heavy ...