How do reasoning-focused LLM architectures differ from traditional next-token prediction models?

Reasoning-focused LLM architectures extend traditional next-token prediction systems with planning, intermediate reasoning, verification, and structured cognitive workflows.

Traditional transformer-based LLMs are fundamentally autoregressive next-token predictors. They generate outputs by estimating the probability distribution of the next token given previous context.

Although this approach produces surprisingly capable behavior, raw next-token prediction has important limitations:

Reasoning-focused architectures attempt to overcome these weaknesses by introducing additional cognitive structures.…