I actually think on the LLM side it might be beneficial to refer to them as <thinking> because it explicitely guide the token generation towards a "thinking spa

I actually think on the LLM side it might be beneficial to refer to them as <thinking> because it explicitely guide the token generation towards a "thinking space". As weird as it is, anthropomorphizing LLMs in prompts has been actually pretty useful (think of the latest big math discoveries which were achieved by having the user giving supporting words). It would be interesting to see if a LLM would perform worse if you used a more neutral term. The paper's argument is rather than using terms like "thinking trace" can lead people to believe that the model is really thinking, and thus these traces can be used as a sort of interpratbility parameter. This can give a false sense of security when building a LLM-based system which requires guardrails and tracability.