> At least in machine learning

You should read math papers lol.

Honestly, I feel the opposite. But maybe that's because my doctorate is in ML. But we might also have a different definition of what the hallmark papers are. Many of the well known papers are ones that model scaled an existing method (ViT and ddpm are good examples). Important papers, but in which sense? I do think we have space for all types of papers and written to different audiences, but I don't think we should punish papers just because they're written to their niche domain. In the past decade there's been a huge increase in citation farming and I don't think that helps. Math is probably too much on one extreme but I think ML is on the other. You have to write your papers so a first year PhD can understand, because they're the ones reviewing your work. I don't think that's a desirable outcome