Selection Rather Than Prediction
Summary
Selection Rather Than Prediction argues for using a cohort of multiple agents rather than a single model. It describes a best-of-N workflow with human arbiters, supported by a leaderboard and a Bradley-Terry/Elo-style ranking, showing substantial gains from cohorts (top-1 vs top-7) and the importance of task-specific evaluation signals.