Unify ML4MILP: Disentangle, Recombine, and Reevaluate to Benchmark Neural Primal Heuristics with Solver-backed Search

Machine learning for (mixed) integer linear programming (ML4MILP) is rapidly expanding from solver-internal assistance toward end-to-end primal solution generation. Yet progress remains difficult to compare and interpret: neural prediction, solver-backed completion, and incumbent refinement are often entangled within monolithic pipelines, while datasets, training styles, solver backends, and evaluation protocols vary substantially across studies. We introduce ML4MILP-Bench, a modular benchmark and systematic analysis framework that decomposes learning-based MILP primal heuristics into three reusable stages. A *Learner* maps a standardized bipartite graph to variable-wise soft scores; an *Enforcer* converts these predictions into a first complete solution; and a *Refiner* further improves a feasible incumbent under fixed budgets. Within the unified taxonomy and re-implementation, we streamline, recombine, and systematically probe 8 learners, 6 enforcers, and 3 refiners via re-modularized learning components and re-encapsulated solver backends. Across experiments on diverse settings, we separately assess raw neural predictions and their interactions with solver-backed components to discern where performance gains originate and which design principles remain effective under controlled ablation. We further envision ML4MILP-Bench as a living benchmark to maintain up-to-date reproductions and empirically grounded insights.