How do LLMs simulate reasoning chains internally?

They approximate reasoning by generating intermediate latent steps encoded in token space.

Chain-of-thought emerges when models are prompted or trained on structured reasoning data. Internally, transformers propagate intermediate semantic states across layers, simulating multi-step reasoning without explicit symbolic manipulation.