But we already know that LLMs can do much better than that. See the famous “grokking” paper[1], which demonstrates that with sufficient training, a transformer can learn a deep generalization of its training data that isn’t just a probabilistic interpolation or extrapolation from previous inputs.
Many of the supposed “fundamental limitations” of LLMs have already been disproven in research. And this is a standard transformer architecture; it doesn’t even require any theoretical innovation.