This paper’s method might look slick on a first pass—some new architecture tweak or loss function that nudges benchmark metrics upward. But as an ML engineer, I’m more interested in whether this scales cleanly in practice. Are we looking at training times that balloon due to yet another complex attention variant? Any details on how it handles real-world noise or distribution shifts beyond toy datasets? The authors mention improved performance on a few benchmarks, but I’d like to see some results on how easily the approach slots into existing pipelines or whether it requires a bespoke training setup that no one’s going to touch six months from now. Ultimately, the big question is: does this push the needle enough that I’d integrate it into my next production model, or is this another incremental paper that’ll never leave the lab?