I can't speak to math and theoretical CS, because both of those fields now have methods to make proofs that can be verified (and math is probably the only situation where you can "prove" something true; all other fields are effectively probabilistic, not logical in nature).
I disagree with your premise. Part of our job as scientists (thankfully no longer mine) is to reduce the irrelevant and incorrect noisy as early as possible. I have seen so many grad students get excited by a paper and put enormous effort into reproducing somethign that was a false or fake result.
As scientists, we need to have effective filters to quickly ignore things that are probably not relevant to us. But we also need to acknowledge that those filters are necessarily noisy, and they don't tell much of the value of the work they reject.
If you want to determine reliably whether something is irrelevant and incorrect noise, determining whether there is anything of value is a necessary first step.
I've seen many reproduction attempts in bioinformatics fail, because they person trying to reproduce the work didn't have the conceptual background to do it correctly. Instead of spending enough time studying the theory, they rushed directly to action.
Haha, Sean Eddy used to complain about reproduction efforts in bioinformatics (specifically, people trying to benchmark HMMER and doing a bad job). Fortunately, my advisor helped me learn the techniques and Sean approved (he was also happy that HMMER beat BLAST for remote homolog detection).
I also left a postdoc position over my professor's decision to rewrite my paper to juice all the stats- not a specific error, but selectively interpreting the data to make the results look better than state of the art, when they were not.
I would absolutely love to have my "paper correctness AI" mark all those bad reproductions in the literature and outright misrepresentations- it's all too easy to rush to publish and get a lot of attention- especially if your advisor or coauthors are prestigious and mildly unethical.
I disagree with your premise. Part of our job as scientists (thankfully no longer mine) is to reduce the irrelevant and incorrect noisy as early as possible. I have seen so many grad students get excited by a paper and put enormous effort into reproducing somethign that was a false or fake result.