N.B. My comment uses terms like "simulate," "entity," and "superposition" to keep language consistent and easy-to-compare with the OP -- which I was trying to s

N.B. My comment uses terms like "simulate," "entity," and "superposition" to keep language consistent and easy-to-compare with the OP -- which I was trying to summarize. If you find the use of these terms confusing, you can substitute them with others that are less confusing to you, and still grok the main points. For example, if you substitute "simulate" with "model," "entity" with "process," and "superposition" with "mixture," you can informally restate the hypothesis as: "LLMs learn to model text-generating processes drawn from a latent space, such that the output of an LLM is produced by a mixture of such processes. When we give the LLM a prompt, it samples text from the mixture of all possible text-generating processes modelable by the LLM that are consistent with the prompt. The mixture is more likely to reduce (e.g., be marginalized) to an "evil" process because there is no text-generating behavior which is good that isn't also evil." Whether you think of probability measures as amplitudes over a complex field or as real scalars shouldn't detract you from grokking the main points :-)