Learning to Discover at Test Time: When Search Learns Back
Opening — Why this matters now For years, scaling AI meant one thing: train bigger models, then freeze them. At inference time, we search harder, sample wider, and hope brute force compensates for epistemic limits. This paper challenges that orthodoxy. It argues—quietly but decisively—that search alone is no longer enough. If discovery problems are truly out-of-distribution, then the model must be allowed to learn at test time. ...