AI RESEARCH
The Offline-Frontier Shift: Diagnosing Distributional Limits in Generative Multi-Objective Optimization
arXiv CS.LG
•
ArXi:2602.11126v2 Announce Type: replace Offline multi-objective optimization (MOO) aims to recover Pareto-optimal designs given a finite, static dataset. Recent generative approaches, including diffusion models, show strong performance under hypervolume, yet their behavior under other established MOO metrics is less understood. We show that generative methods systematically underperform evolutionary alternatives with respect to other metrics, such as generational distance.