ConSA introduces a framework that learns optimal full vs. sliding-window attention allocation using L0 regularization and augmented Lagrangian constraints. It outperforms rule-based methods, with SWA placed in bottom layers and FA concentrated in middle-layer blocks, a pattern consistent across model scales and sparsity levels.