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I am a ML researcher, in Industry without a PhD. The papers are absolutely for me. (And if they aren't then thats a major clique/circle-jerking issue, as I'm the one actually trying to apply what is being done)
https://www.nature.com/articles/s41598-025-07087-2 This paper I recently tried to replicate for research on IoT cuffless BP, it absolutely fails to replicate. Not only that, but it also suffers from massive subject leakage on how it splits the data. It's pretty much overfit with a 75% overlap between signals and then it shuffles those between train and val. Even copying it's splitting approach I failed to get more than a MAE SBP of 6.07 and DBP of 4.3. Paper claims sub 2.0 for both.
Then there's this: https://arxiv.org/pdf/2512.19428. Maybe you know Grassmann flows and manifolds but I definitely did not learn this naturally. I pretty much need a background tutorial on this.
I actually enjoyed this paper's concept: https://arxiv.org/pdf/2602.14972 But needing to read 2-3 additional papers, one of which was super mathy proving out the intuition was a lot of work. It still takes me a bit to conceptualize this because it is DEEP in the bayesian world.
Maybe you are in a different subfield than I am, but I have consistently failed to replicate paper results for the occasional paper for the last 4-5 years. It happens, it's a thing. If I say that to other industry researchers they pretty much agree. One of the reasons we think poorly of academics.
Without looking at this paper I agree it is shit. This paper is not a machine learning paper (and basically nothing in Nature is). The failure to replicate is a problem of the culture of medical science and not ML.
Just because a researcher uses a compiler in their research does not make them a "compiler researcher", and similarly, just because someone uses machine learning in their research does not make them a "machine learning researcher". Papers at PLDI are not targeted at people who are "trying to apply compilers" and papers at NeurIPS/ICML are not targeting people who are "trying to apply ML". (If you actually want to see a "mathy" paper, BTW, you should take a look at the papers at COLT... these are definitely not for you and these are definitely hard-core proper machine learning papers.)
This paper is definitely an ML paper, and honestly is pretty reasonable. It's not earth shattering, but it's exactly the kind of work that I would expect from a decent phd student (which the author is). It's pretty bread-and-butter ML to take a model and explore ways to reduce the representational complexity of the model. Grassmann manifolds are outside of standard ML math, but the explanation in 2.2 was easy to follow. The math here is no harder than the math in standard graduate textbooks.
Again, this doesn't seem very mathy to me. The notation all looks like standard stuff from the Pearl textbook (admittedly not standard ML, but definitely standard for anything causal), and anyone who has worked through Bishop (which should be literally everyone with an ML phd of a certain age) should have no problem.
Having to look up 3 references to read and understand a paper seems absolutely reasonable to me.
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