Repair Before Veto: Repair-Augmented Constraint Learning for Contextual Decisions
2026-06-01 • Artificial Intelligence
Artificial Intelligence
AI summaryⓘ
The authors propose a new way to handle hard rules by allowing systems to try known fixes before outright rejecting an option. Their method, called Repair-Augmented Constraint Learning (RACL), accepts candidates if a feasible and affordable repair can be made, otherwise it provides a clear rejection along with suggested fixes. This approach improves over traditional methods that simply veto any violation without repair, reducing false rejections significantly in tests. They also provide theoretical insights into how these repair-based decisions work and perform evaluations showing RACL's effectiveness compared to other strategies.
hard constraintsconstraint learningrepair operatorsdecision frameworkfalse vetoclassificationfeasibilityvalidationconstraint relaxationmachine learning
Authors
Yifan Wang
Abstract
Hard constraints are usually treated as terminal vetoes: once a candidate violates a requirement, the learned rule rejects it and any repair is handled outside the decision semantics. This misses a common deployed regime in which the system already knows a finite menu of modifications, such as adding a ticket option, changing a configuration, or requesting an available service upgrade. Existing constraint-learning, soft-relaxation, and recourse methods address nearby problems, but they do not learn whether an option should be repaired before being vetoed. We introduce Repair-Augmented Constraint Learning (RACL), a contextual decision framework that lifts known repair operators into the classifier semantics. A candidate is accepted when an affordable repair makes it feasible and preferred enough; otherwise the system returns a structured rejection credit and, when applicable, a repair plan. This repair-before-veto view strictly generalizes no-repair HASSLE-style semantics, reveals an irreducible false-veto gap for terminal-veto rules, separates binary-label non-identifiability from decision-rule learnability, and gives capacity and calibration bounds for the observed-feasibility shared-weight setting. Across controlled and DB1B-derived benchmarks, RACL recovers the intended credit and repair structure. On the hardest raw-data-derived tier, validation-selected RACL reduces false vetoes to 10/4039 (FVR 0.0025), versus about 1064/4039 for the strongest repair-search black-box baseline, while making the FVR/EDR trade-off explicit.