Model-Agnostic Signal Discovery with Machine Learning:

Model-Agnostic Signal Discovery with Machine Learning: Bridging the Gap Between Theory and Practice

arXiv:2605.31103v1 Announce Type: new Abstract: Searches for new phenomena in complex scientific data are predominantly model-dependent, optimized for specific hypotheses, and therefore limited in their coverage of the space of possible signals. Recently, new AI-based model-agnostic search strategies, many of which have been pioneered in high-energy physics, have been proposed which provide a complementary paradigm, prioritizing broad exploration over tailored analyses.