In-Context Learning Amplifies a Latent Symbolic Circuit

The paper investigates how large language models activate a latent symbolic reasoning circuit—comprising abstraction, induction, and retrieval—when presented with in-context examples. By tracking this circuit across different shot counts and model families, the authors show that its components become detectable and functional long before the model reaches high accuracy. They further demonstrate that per-head causal contributions can increase eightfold from 1- to 10-shot, and that interventions such as cross-shot activation patching or function vector injection can dramatically improve accuracy, even at 0-shot, by leveraging the pre‑existing circuit in the model weights.

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