I will venture my 2 cents, the equations kinda sorta look like something - but in no way approach a derivation of the wall. Specifically, I would have looked for a derivation which proved for one of/all of

1. Sequence Models relying on a markov chain, with and without summarization to extend beyond fixed length horizons. 2. All forms of attention mechanisms/dense layers. 3. A specific Transformer architecture.

That there exists a limit on the representation or prediction powers of the model for tasks of all input/output token lengths or fixed size N input tokens/M output tokens. *Based On* a derived cost growth schedule for model size, data size, compute budgets.

Separately, I would have expected a clear literature review of existing mathematical studies on LLM capabilities and limitations - for which there are *many*. Including studies that purport that Transformers can represent any program of finite pre-determined execution length.