Learning to Adapt: In-Context Learning Beyond Stationarity
arXiv:2604.10946v1 Announce Type: new
Abstract: Transformer models have become foundational across a wide range of scientific and engineering domains due to their strong empirical performance. A key capability underlying their success is in-context le…