Paper recorded by Signals 4 on 2026-10-08 in cs.LG. Abstract reproduced from arXiv; link to the original below.
Published 2026-10-08 on arXiv · recorded by Signals 4 on 2026-10-09
Category: cs.LG · 机器学习 · first seen 2026-10-09
We study whether persistent out-of-distribution (OOD) degradation can be predicted before it is directly observed using only source-side training dynamics. In a controlled shortcut-learning setting, a simple logistic regression predictor develops a clear prospective signal, while training time alone does not. Temporal summaries of the source-side quantities are substantially more informative than