Explain chain-of-thought prompting within the spin glass framework of in-context learning

Establish a mechanistic explanation of chain-of-thought prompting—decomposition of a complex task into intermediate steps—within the spin glass model mapping of a single-layer transformer with linear attention trained on linear regression tasks, to clarify how in-context learning enables multi-step reasoning without parameter updates.

Background

The paper maps a simple linear-attention transformer performing in-context learning on linear regression tasks to a spin glass model with real-valued spins, enabling statistical mechanics analysis of emergent ICL behavior. In the conclusion, the authors identify explaining chain-of-thought prompting—where complex tasks are decomposed into intermediate steps—as an explicit open direction.

They emphasize that these topics constitute open questions, indicating that a rigorous mechanistic account within their spin-glass framework remains to be established.