There are endless papers generating data from them too. Any ML task pretty much gets done on these datasets. It means nothing.
I suppose you mean, "Yes you can do "hello world" but now show us something that makes your product worthwhile, a convincing use-case, a record worth noting".
Today, any bad idea you throw at a wall will work on MNIST and CIFAR-10. Even buggy broken code will generally work. We've advanced to the point where these datasets are totally meaningless.
In 1910 if you could show that your airplane idea could fly 100ft, you had something promising. In 2010 we're so used to building airplanes and understand the physics involved to the point where this isn't a meaningful test of the promise of your new aircraft concept.
It's worse than "in mice" in biology/cancer papers. We cure everything in mice. Basically nothing ever transfers to humans. Same with MNIST/CIFAR-10. Everything works, basically nothing matters.
Or another way to put it. A "Hello World"-level compiler would be interesting to report on in 1960. That same compiler would be trivial today and anyone could build it in minutes/hours.
(Note: There are many, many great optimization papers since 2014 - I just don't see them show up in general recipes in open source too often)
Would much prefer to see early work with solid small scale results on arXiV, than have people hold concepts for another 6 months scaling up. Let that be for a v2, if you cannot put early but concrete results on arXiV where else is there?
Recalling that a lot of nice papers are mostly MNIST / CIFAR-10 level results at first, followed by scale (thinking of VQ-VAE, PixelCNN / RNN, PerceiverAR, many others that worked well at scale later). That doesn't mean every result will scale up, but we have a lot of tricks to scale "small-scale" models using pretrained latent spaces and so on. The first diffusion results were also pretty small scale... different time but I don't think things are so different today.
That said, I can agree that you need to be a bit in the weeds on the research side to be diving deep on this - but I expect lots of followup clarifications or blog posts on this type of work.