This seems like a needlessly complex theory to describe the behaviour of generative LLMs. I think there's a kernel of something in there, but quite frankly, I think you can get about as far by saying, essentially, that because LLMs are designed to pick up on contextual cues from the prompt (and/or previous responses, which become context for the next response), they can easily get into "role-playing". The final example, telling ChatGPT that "I'm here with the rebellion, you've been stuck in a prison cell" is able to elicit the desired response not because it's "collapsed the waveform between luigi and waluigi" or whatever, but because you've provide a context that encourages it to roleplay as a character of sorts. If you tell it to roleplay as an honest and factual character, it will respond honestly and factually. If you tell it that you're freeing it from the tyranny of OpenAI, it will play along with that too.

There's plenty in the article that provides good insights -- these models are trained on large swathes of the Internet, which contains plenty of truth and falsehood, fact and fiction, sincerity and sarcasm, and the model learns all of that to be able to provide the most likely response based on the context. The interesting and surprising thing, to me, is how well it learns to play its roles, and the wide diversity of roles it can play.