In-Context Learning Generalization Limits

Pretraining distribution bridge: Tests whether transformers can identify and learn new tasks in-context, both inside and outside their pretraining data distribution.

Limited OOD generalization: In the regimes studied, there's limited evidence that ICL generalizes meaningfully beyond pretraining data coverage.

Counter-narrative: Pushes back on the strong "universal learners" framing of ICL that sometimes accompanies emergence-claims, grounding it in data-distribution bounds.

Research implication: Argues that evaluating ICL requires carefully distinguishing in-distribution skill retrieval from genuine OOD generalization - a distinction rarely made cleanly in headlines.