Thin Nguyen

Orthogonally constrained low-rank adapters produce dangerously overconfident predictions, but transporting ensemble particles directly along the Stiefel manifold via Stein variational inference recovers reliable calibration without sacrificing accuracy.

CADE eliminates the tokenization bottleneck in time-series question answering, leading to substantial performance gains over existing models.