The cat example is from the section on their block-causal attention mask. I really don't think this fixes the issue. So far as I can see, the block schedule dictates when they sample at each position. It does _not_ change that they basically have an array-of-token-vars representation, and once `t_i` is sampled, nothing can "move" that value left or right.

Early draft yes. But when you write an early draft of prose or code, you leave yourself the ability to insert or remove material in a way that _changes the indexes of the tokens you already put in your draft_. If you write a letter, you may know that it ends with "Yours Truly, <your name>", but not know the absolute number of tokens the letter will use. In this framework, once you say that "Yours Truly, John Hancock" are tokens 501 to 506, infilling the preceding sentences requires that you exactly preserve the number of tokens before that point ... which to me seems silly. I'm sure it's computationally messy to be able to slide stuff around, but if it meaningfully changes the topology of the search process, it may be worth it.

I think the gap is, if they're building hybrids with _forward_ AR and diffusion, they risk giving up the cool part of diffusion which is reasoning back. I may be imposing unreasonable human biases on to this, but I really think it would be interesting to have the model engage with the structure of the text, rather than just being either a sequence or an array of tokens. E.g. "I'm going to _ tomorrow." If the _ is not just a token but an expansion in context, which might be a noun phrase, a verb phrase etc, it could be filled in with "the mall", "practice guitar". In code "if (_1) { return _2; }", _1 could be an expression whose type is bool, and which makes sense as a check to confirm that some process is finished. I don't care specifically how many tokens either of those is, but I do care that it makes sense in context.