The paper argues that pretending that the so-called thinking traces represent real reasoning can lead users into trusting wrong answers, if the thinking traces appear convincing enough. Researchers might inspect these traces to try to determine the “intent” of a model, as well.
For an example of the latter, when OpenAI spoke about the hacking of HuggingFace at Black Hat, they repeatedly showed the thinking traces of their model as “proof” of what the model was “thinking” as it performed the attack, calling out “surprise” moments, etc.
Now, it’s possible that the employees presenting didn’t truly believe that the thinking traces would give them useful clues, and presented them only for a “wow” factor, but I wouldn’t discount the possibility that even the people working at frontier companies can fall for this tendency to anthropomorphize LLMs.