Before deploying learned proposals in constraint solvers, compare against random multi-start under equal budget—learned models help mainly in high-dimensional uncoupled systems, but classical methods often suffice for real problems.
This paper investigates when learned diffusion models help solve continuous algebraic constraint systems by comparing them against a simple baseline: random multi-start search with the same computational budget.