Belief or Circuitry? Causal Evidence for In-Context Graph Learning

This paper investigates how LLMs learn in-context, using a graph random-walk task to explore whether they pattern-match or infer latent structure. It reveals that neither account alone is sufficient, presenting evidence of simultaneous encoding of graph topologies and causal interventions.