Comment by formerOpenAI

OP here — adding a bit more color.

RCC isn’t a new model or training method. It’s basically a boundary effect you get when a predictor has no access to its own internal state or to the “container” it’s running inside.

What stood out to me is that when the model steps too far outside its grounded reference frame, the probability space it’s sampling from starts to warp — things stop being orthogonal in the way the model implicitly assumes. What we call “hallucination” looks more like a geometric drift than random noise.

I’m not pitching this as some grand unifying theory — just a lens that helped me understand why scaling cleans up certain failure modes but leaves others untouched.

If anyone has examples of models that maintain long-chain consistency without external grounding, I’d genuinely like to hear about them.