Repair Before Veto: Repair-Augmented Constraint Learning for Contextual Decisions

• URL: http://arxiv.org/abs/2606.02326v1
• Date: Mon, 01 Jun 2026 14:38:04 GMT
• Title: Repair Before Veto: Repair-Augmented Constraint Learning for Contextual Decisions
• Abstract summary: We introduce Repair-Augmented Constraint Learning (RACL), a contextual decision framework that lifts known repair operators into the semantics.<n>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.<n>Across controlled and DB1B-derived benchmarks, RACL recovers the intended credit and repair structure.
• Score: 6.016090674751934
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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