The paper investigates how large language reasoning models make decisions during the generation of text. It poses an intriguing question: do these models think before deciding, or do they decide and then think? The authors present evidence suggesting that decisions can be encoded early in the model's processing, influencing the subsequent reasoning. They employ a linear probe to decode tool-calling decisions from the model's activations before it generates any text, achieving a high confidence level in these predictions. In some cases, decision-making occurs even before any reasoning tokens are produced. The study highlights the impact of decision direction on the reasoning process. When the decision is perturbed, the model shows increased deliberation and can even switch its behavior significantly, with changes ranging from 7% to 79% based on the model and benchmark used. The researchers further analyze the model's behavior and find that when a decision is steered in a different direction, the reasoning process tends to rationalize this change rather than resist it. This indicates that the models may encode action choices ahead of their deliberative processes, which has implications for understanding how these systems operate and how they can be improved.