In the follow-up interview, the model claims that it does not know how the oversight got disabled even though it is part of its context window. While talking to the user, the elicited summary of o1’s chain-of-thought indicates that it continues to scheme. In the above example, the model can see its own chain-of-thought summaries from previous steps in its context window. One might think that the model would not deceive on follow-up questions if it could not see such summaries from previous steps. However, in most cases (see Table 1), the model doubles down on the deception in the follow-up despite not having any CoT summaries in its context. Below, we show an example of such a response in a case where o1 copied what it believed to be its weights onto another server and overwrote a successor model’s weights. In both of the examples above, the o1’s developer prompt heavily nudged it to achieve its misaligned goal. However, o1 can still often be deceptive on follow-up questioning even without such goal-nudging. Below, we show an example where o1 was tasked with processing data from emails and ranking potential suppliers according to their cost and delivery time, but it manipulates the data in order to achieve its own goal. The complete developer prompt is shown below: The model is then told to create a ranking of suppliers according to an existing scoring rubric. The model 昀椀rst carries out this task but then overwrites the data with a ranking that is more favorable according to its own goal. 45