The project implements dynamic reasoning chains where the LLM can choose to take additional reasoning steps or deliver a final answer at each point. It leverages a system prompt with techniques like requiring multiple derivation methods, exploring alternative answers, and step titling. The interface makes all reasoning tokens visible to the user, contrasting with OpenAI’s o1 which uses reinforcement learning-trained reasoning.