Failed on benchmark
2026
Prolate Energy-Preserving Bottleneck
Insert a fixed DPSS/prolate projection before an expensive neural block, retaining exactly the modes whose time-frequency concentration eigenvalues exceed a target threshold. Use the paper's tail-quantile formula to choose the projection rank from sequence length, effective bandwidth, and tolerated energy loss, then optionally learn a small correction in the retained coordinates. Unlike a Fourier truncation, the basis is optimized for simultaneous localization in the finite input window and the…
Paper: Uniform sine-kernel determinant asymptotics, tail-side quantiles, and prolate eigenvalue bounds
arXiv:2608.15808