AI RESEARCH
Position Paper: Post-Solve Robustness in Decision Engines: Feasible Regions and Smoothness Under Perturbations
arXiv CS.AI
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ArXi:2606.00002v1 Announce Type: new Mixed-Integer Linear Programming (MILP) decision engines routinely output nominally optimal plans for high-stakes industrial systems. Yet deployment rarely matches solve-time assumptions: small perturbations in costs, demands, or resource availability can invalidate feasibility or trigger discontinuous shifts to qualitatively different solutions. We argue that this post-solve robustness gap is a missing layer in today's optimization pipelines and a missing evaluation dimension for learning-enabled decision systems.