Only Project Once: Projection-Adaptive Loss for Exact Constraint Satisfaction

Excerpt

arXiv:2610.04572v1 Announce Type: cross Abstract: Precise constraint satisfaction is a prerequisite to deploying learned models in many areas, motivating methods that repair raw neural predictions with a repair procedure. Current methods unroll multiple repair steps in training and softly penalize constraint violations that remain after the unroll. This is compute- and memory-intensive, lacks robustness when the repair fails to converge, and surrenders most of the constraint satisfaction work to