Why is a separate MTP model even necessary? An LLM forward inference doesn't just predict token vectors for the new last token: In diagrams the forward pass i

Why is a separate MTP model even necessary? An LLM forward inference doesn't just predict token vectors for the new last token: In diagrams the forward pass is typically depicted as taking input tokens vectors <t1, t2, t3, ... t98, t99, t100> (here native context being 100 for didactic purposes) and generating output token vectors <t2, t2, t4, ..., t99, t100, t101>. As far as I understand that is didactically only semi correct, it correctly depicts the locations of tokens in the input and output string, but actually the token vector at the t2 output position is NOT identical to the t2 vector from the input, but a token vector which after softmax gives P(t2 | t1). And output token position t5 actually corresponds to P(t5 | t1,t2,t3,t4). I.e. the forward inference is modelling the statistical conditional N-gram function from inputs to outputs, from the bigram conditional probability P(t2 | t1) all the way up to P(t101 | t1, t2, t3, ..., t98, t99, t100). Suppose you want to take bigger steps, nothing prevents one from calculating the forward function by sliding a fixed (committed output string) to the left not 1 position but say 10 positions, and then using the last 10 predictions as the new output prediction. That doesn't need a new MTP model. Perhaps it would take some careful modification to ensure the same original output distributions as if the tokens were generated one at a time, but this hints at the possibility. One could also slide to the left 5 positions twice, not committing to all 10 new tokens at once but only commiting to the 5 oldest values of the 10 new values, and using the noncommited 5 last values as input vectors for the next invocation, so the model can push the new 5 vectors towards its final commited output vector value in 2 steps for better convergence... Is there any reason multitoken prediction doesn't work this way, or is there some aspect of the conditional N-gram interpretation of LLM models that I am miscomprehending?