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It's extremely bad in ML literature, where pressure to publish and get your citation + h-index up means that people publish all kinds of non-replicate-able junk. Nobody wants another incremental advancement paper so every paper is revolutionary. I cannot tell you the number of times I've taken a paper that looked interested, either used their code or re-implemented it approximately, used their datasets and gotten far worse results. The new trick, or really old trick making a resurgence, is to include all kinds of arcane math in the paper and not provide any code so its impossible to replicate it without a math PhD in that area.
Research papers are written for phds, and if you don't have a phd then you are not the target audience. Unreproducibility and over-mathiness of ML research is a common meme among the online ML-adjacent communities, but it's just not true. The ML community has done far more than any other community to encourage reproducibility and they've had a lot of success in doing so.
Source: I am an ML researcher with only a mediocre publication record. I've got my own gripes with the system that have led to my pub-record being mediocre, but reproducibility is not one of them.
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.
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Speaks really poorly of the quality of the publication too, if it’s allowed to pass. I remember reading a paper once in a “journal” (that actually had some legitimate backing) about the possibility of igniting Saturn with a nuclear weapon and I was thinking, “What the fuck is this shit?,” as I made my way through it. Academia in no way is this sort of purified, pristine landscape of nothing but rigorous logical and scientific clarity that people think it is. There’s at least as much bad science as people think there is good. And some areas of it are outright corrupt.
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