Ah didn't realize you were referring to NoPE explicitly. And yea, the intuitions gained from that paper are pretty much what I alluded to above, you don't get away with never modeling the positional data, the question is how you model it effectively and from where do you derive that signal.

NoPE never really took off more broadly in modern architecture implementations. We haven't seen anyone successfully reproduce the proposed solution to the long context problem presented in the paper (tuning the scaling factor in the attention softmax).

There is a recent paper back in December[1] that talked about the idea of positional information arising from the similarity of nearby embeddings. Its again in that common research bucket of "never reproduced", but interesting. It does sound similar in spirit though to the NoPE idea you mentioned of the causal mask providing some amount of position signal. i.e. we don't necessarily need to adjust the embeddings explicitly for the same information to be learned (TBD on whether that proves out long term).

This all goes back to my original comment though of communicating this idea to AI/ML neophytes being challenging. I don't think skipping the concept of positional information actually makes these systems easier to comprehend since its critically important to how we model language, but its also really complicated to explain in terms of implementation.

[1] https://arxiv.org/abs/2501.00073