Context parroting: A simple but tough-to-beat baseline for foundation models in scientific machine learning

ArXi:2505.11349v3 Announce Type: replace Recent time-series foundation models exhibit strong abilities to predict physical systems. These abilities include zero-shot forecasting, in which a model forecasts future states of a system given only a short trajectory as context, without knowledge of the underlying physics. Here, we show that foundation models often forecast through a simple parroting strategy, and when they are not parroting they exhibit some shared failure modes such as converging to the mean.