This article outlines a formal and practical framework for System 2 reasoning in large language models using four key methods: chain-of-thought prompting, self-consistency aggregation, tree-of-thoughts search, and meta chain-of-thought reasoning. Each method is described as an optimization process over reasoning steps, showing how structured inference improves accuracy and adaptivity.
deliberate aisystem-2llm reasoningchain-of-thought