> Tokenization has been the final barrier to truly end-to-end language models.
> We developed the H-Net: a hierarchical network that replaces tokenization with a dynamic chunking process directly inside the model, automatically discovering and operating over meaningful units of data
This is a dumb critique. A thin wrapper running a new samples of training data and updating weights is something already done in many situations. A sophisticated rag system incorporates new information even if the weights themselves aren't updated, effectively giving "new memory".
LLMs have problems, in practice being "static" aint one of them.
It won't prove intelligence, but at least it won't be static like a book.