[noncentral]

Author here. Quick clarification: RCC is not proposing a new architecture. It’s a boundary argument — that some LLM failure modes may emerge from the geometric limits of embedded inference rather than from model-specific flaws.

The claim is simple: if a system lacks (1) full introspective access, (2) visibility into its container manifold, and (3) a stable global reference frame, then hallucination and drift become mathematically natural outcomes.

I’m posting this to ask a narrow question: if these axioms are wrong, which one — and why?

Not trying to make a grand prediction; just testing whether a boundary-theoretic framing is useful to ML researchers.

[verdverm]

I think it's simpler, the models are sampling from a distribution. Hallucinations are not an error, they are a feature