Archive-Guided Inference Reallocation for Conditional Generative Models in Offline Multi-Objective Optimization

Conditional generative models provide a promising approach to offline multi-objective optimization, but existing inference procedures typically fix all conditioning targets before sampling begins. This prevents them from using information revealed by partially generated solutions. We introduce Archive-Guided Reallocation (ARC), a closed-loop inference controller for pretrained conditional diffusion and flow models. ARC audits intermediate generations, records their estimated trade-offs in an evolving archive, and redirects selected trajectories toward new target objective vectors while preserving their current states and the total number of backbone evaluations. Our experiments separate two effects - reallocating the inference budget improves Pareto-frontier quality, while selecting new targets from the archive improves convergence. On Off-MOO-Bench, closed-loop reallocation improves mean full-set hypervolume by 4.49% over a compute-matched open-loop control and improves all 15 engineering tasks over its backbone. Archive-guided fronts dominate random-target fronts more often (C-metric 0.36 vs. 0.16) at comparable objective-space occupancy. We further show that the benefit depends on how much of sampling remains steerable after a trajectory is redirected, consistent with the weaker, statistically non-significant effects on a flow-matching backbone whose direct guidance is active during the final 20% of integration. ARC requires no retraining, trajectory restarts, true-objective queries during generation, or additional backbone evaluations.