ORACLE: Optimizer-Relative Alignment for Constrained LEarning

Constraint-handling methods typically intervene before the optimizer acts, by modifying the objective or the gradient. Yet momentum, adaptive scaling, and structured preconditioning can substantially reshape that signal before it becomes a parameter update. We formulate optimizer-relative constrained learning, where constraint compatibility is assessed on the post optimiser update. Building on this view, we introduce ORACLE, which takes the native optimizer's original proposed step, aligns it when locally adverse in the optimizer's own geometry, limits the correction, and commits it only after validation. We evaluate ORACLE across eight Partial Differential Equation benchmarks and four optimizers spanning Euclidean, diagonal-adaptive, and structured preconditioned geometries, where it improves or matches native optimiser in 94% of configurations. Cross-model analysis shows the same behavior in 92% of configurations, while matched comparisons show improvements over alternative constraint-handling methods acting at the objective, gradient, and post optimizer levels.