Veto Later, Repair First
TL;DR for operators Most decision systems treat hard constraints like a trapdoor. Candidate violates requirement, candidate disappears. Efficient, clean, and occasionally absurd. The paper behind Repair-Augmented Constraint Learning, or RACL, argues that this is the wrong semantics for systems that already know how to modify an option before showing it to the user.1 A flight missing a checked bag, a hotel missing breakfast, a product bundle missing an accessory, or a schedule slot needing a resource adjustment may not be a bad option. It may be a good option one repair away from being acceptable. ...