Stanford paper maps the in-context learning circuit in small LLMs
The practical implication is that few-shot prompting works by label lookup, not feature learning, so prompt design should repeat labels several times and use classes the model can already distinguish. Builders tuning small models for agent pipelines or classification tasks can use this to cut prompt size and failure cases. The finding also explains why tiny models fail on tasks needing unseen features.
