  Model-agnostic anomaly detection is one of the promising approaches in the
search for new beyond the standard model physics. In this paper, we present
Set-VAE, a particle-based variational autoencoder (VAE) anomaly detection
algorithm. We demonstrate a 2x signal efficiency gain compared with traditional
subjettiness-based jet selection. Furthermore, with an eye to the future
deployment to trigger systems, we propose the CLIP-VAE, which reduces the
inference-time cost of anomaly detection by using the KL-divergence loss as the
anomaly score, resulting in a 2x acceleration in latency and reducing the
caching requirement.
