DARC: Dependency-Aware Relational Compiler

Relational feature compilers summarize records linked to a prediction target, but column-wise aggregates cannot represent dependencies among source attributes. We introduce DARC, a dependency-aware relational compiler that first constructs a compact point-in-time representation from column-wise aggregates and then augments it with features derived from empirical functional dependencies. Because such features are not uniformly useful, DARC selects them on temporal folds within the training split and decides per task whether to include them at all. DARC activates dependency-aware features on 14 tasks and retains a purely aggregate representation on the rest. DARC achieves positive win–loss margins against budget-matched Canonical DFS, JUICE, and FastProp; the selected representation also transfers across six downstream predictors. Controlled collisions and real-data rewiring show that dependency features can distinguish histories with identical conventional aggregates.