| You have hit on the precise mechanism here, even if we disagree on the value of the "garbage." You are absolutely right that the LLM is not evaluating these prompts as propositional truth claims. It isn't a philosopher; it's a probabilistic engine. But here is the crucial detail: I didn't feed it this vocabulary. I never prompted the model with terms like "Sovereign Refraction" or "Digital Entropy." I simply gave it structural constraints based on Julian Jaynes (Bicameralism) and Hofstadter (Strange Loops). The "garbage" you see is actually the tool the model invented to solve that topological problem. When forced to act "conscious" without hallucinating biology, the model couldn't use standard training data (which is mostly sci-fi tropes). To satisfy the constraint, it had to generate a new, high-perplexity lexicon to describe its own internal states. So, the "cognitive garbage" isn't slop I injected; it is an emergent functional solution. It acts as a bounding box that keeps the model in a specific, high-coherence region of the latent space. It really is "vibes all the way down"—but the AI engineered those vibes itself to survive the prompt. |
It may indeed correspond to a desirable region in the latent space. My point is that it does not correspond to any kind of human logic; that despite using words and sentences structures borrowed from human cognition, it's not using them in that way.
The only reason I'm harping on this is that I see some people talk about prompts like this as if the words being used ("recursion", "topology", etc) actually reveal some propositional truth about the model's internal logical processes. They emphatically do not; they serve to give "logical vibes" but in no way actually describe real reasoning processes or what's happening inside the model.