AI RESEARCH
Large Language Models as Amortized Pareto-Front Generators for Constrained Bi-Objective Convex Optimization
arXiv CS.AI
•
ArXi:2605.12106v1 Announce Type: new Generating feasible Pareto fronts for constrained bi-objective continuous optimization is central to multi-criteria decision-making. Existing methods usually rely on iterative scalarization, evolutionary search, or problem-specific solvers, requiring repeated optimization for each instance. We