As models get more advanced and generate an increasing throughput of increasingly higher quality results, how are you determining when to trust its results and when human review is needed on what?

E.g., 948 results is a high number to review in a timely manner unless your team is very large or spending several months combing through the results

EDIT: nvm i shouldve kept reading, answered

> At this scale, manually investigating every report is impractical for our research team. We therefore focused on validating findings that a) were reachable by a low-privileged user, b) required no special capabilities, and c) produced measurable evidence of memory corruption.