It will depend on exactly what you mean by 'planning ahead', but I think the fact that features which rhyme with a word appear before the model is trying to predict the word which needs to rhyme is good evidence the model is planning at least a little bit ahead: the model activations are not all just related to the next token.

(And I think it's relatively obvious that the models do this to some degree: it's very hard to write any language at all without 'thinking ahead' at least a little bit in some form, due to the way human language is structured. If models didn't do this and only considered the next token alone they would paint themselves into a corner within a single sentence. Early LLMs like GPT-2 were still pretty bad at this, they were plausible over short windows but there was no consistency to a longer piece of text. Whether this is some high-level abstracted 'train of thought', and how cohesive it is between different forms of it, is a different question. Indeed from the section of jailbreaking it looks like it's often caught out by conflicting goals from different areas of the network which aren't resolved in some logical fashion)