This paper introduces an author-assisted mechanism, based on the Isotonic Mechanism, to improve the selection of best paper awards at large machine learning and AI conferences. It relaxes the common convexity assumption for author utility functions, ensuring truthfulness even with simpler monotonicity assumptions, and empirically demonstrates its effectiveness in improving award selection quality through simulations.