Learning Menu-Based Mechanisms for Truthful Budget-Feasible Procurement

Budget-feasible mechanisms (BFMs) are a fundamental tool for budget-constrained procurement, but designing strong BFMs remains challenging, particularly for general valuation classes. Classical approaches rely on problem-specific analysis and worst-case approximations, often yielding conservative mechanisms in practice. This motivates data-driven methods for discovering mechanisms with stronger empirical performance. However, strict budget constraints expose a key limitation of existing neural automated mechanism design (AMD): they either fail to provide exact economic guarantees or suffer from severe scalability bottlenecks. We propose BFMNet, a scalable menu-based neural framework for learning truthful budget-feasible procurement mechanisms. BFMNet first learns *self-bid-independent* menus from market context, ensuring dominant-strategy incentive compatibility (DSIC) and individual rationality (IR) by construction, and then applies an ex-post, instance-level menu transformation to enforce budget feasibility. We prove that the resulting mechanism satisfies exact DSIC, IR, and budget feasibility. By avoiding intractable global discretization grids and restrictive Lipschitz constraints, BFMNet remains both expressive and scalable. Extensive experiments show that BFMNet improves utility over classical baselines by up to $34.1$%, remains competitive with state-of-the-art neural baselines that provide only approximate economic guarantees, and scales robustly to larger auction instances.