For example, spearheaded by Knuth, the community effectively abandoned the Journal of Algorithms and replaced with with ACM Transactions on Algorithms.

however it's difficult. a big factor is that professors feel obligated towards their students, who need to get jobs. even if the subfield can shift to everybody publishing in a new journal, non-specialists making hiring decisions may not update for a few years which hurts students in the job market.

Somehow the general public and policymakers got the idea that if a paper gets published in any non-fake journal, this is an official endorsement that it's 100% correct, everything in it can be read in isolation, and it's safe to use all claims in the paper to direct policy immediately.

I think academia is partially to blame for encouraging people to believe this rather than insisting on explaining the nuances of how to interpret published research. On the other hand, nobody wants to hear a message that things are nuanced, and they will have to do costly hard work to get at the truth.

I think a world where "you can take any published paper at face value...without going direct to primary sources and bouncing it off an expert in the space" would be great, but it never existed, and it's just fundamentally impossible.

in CS you will have intense grind weeks around conference deadlines and a more manageable but challenging pace of life otherwise.

in wet lab science you live by the schedule set by your experiments, which often involves intense hours.

however, even this advantage is eaten away somewhat because the models themselves are decent at solving hard integrals.

if prediction market contracts really are regulated as commodities, then presumably a lot of insider trading must be legal, although there must be limits of one kind or another and probably if you do something really egregious you might be prosecuted under some legal theory.

the early years of LLMs (when they were good enough to correct grammar but not enough to generate entire slop papers) were an equalizer. we may end up here but it would be unfortunate.

i don't think there are any AI detection tools that are sufficiently reliable that I would feel comfortable expelling a student or ending someone's career based on their output.

for example, we can all see what's going on with these papers (and it appears to be even worse among ICLR submissions). but it is possible to make an honest mistake with your BibTeX. Or to use AI for grammar editing, which is widely accepted, and have it accidentally modify a data point or citation. There are many innocent mistakes which also count as plausible excuses.

in some cases further investigation maybe can reveal a smoking gun like fabricated data, which is academic misconduct whether done by hand or because an AI generated the LaTeX tables. punishments should be harsher for this than they are.

Most big tech PhD intern job postings have NeurIPS/ICML/ICLR/etc. first author paper as a de facto requirement to be considered. It's like getting your SAG card.

If you get one of these internships, it effectively doubles or triples your salary that year right away. You will make more in that summer than your PhD stipend. Plus you can now apply in future summers and the jobs will be easier to get. And it sets your career on a good path.

A conservative estimate of the discounted cash value of a student's first NeurIPS paper would certainly be five figures. It's potentially much higher depending on how you think about it, considering potential path dependent impacts on future career opportunities.

We should not be surprised to see cheating. Nonetheless, it's really bad for science that these attempts get through. I also expect some people did make legitimate mistakes letting AI touch their .bib.

Most of those I spot checked do not give an impression of high quality. Not just AI writing assistance but many seem to have AI-generated "ideas", often plausible nonsense. the reviewers often catch the errors and sometimes even the fake citations.

can I prove malfeasance beyond a reasonable doubt? no. but I personally feel quite confident many of the papers I checked are primarily AI-generated.

I feel really bad for any authors who submitted legitimate work but made an innocent mistake in their .bib and ended up on the same list as the rest of this stuff.

not just some hallucinated citations, and not just the writing. in many cases the actual purported research "ideas" seem to be plausible nonsense.

To get a feel for it, you can take some of the topics they write about and ask your favorite LLM to generate a paper. Maybe even throw "Deep Research" mode at it. Perhaps tell it to put it in ICLR latex format. It will look a lot like these.

https://web.stanford.edu/~jurafsky/slp3/ed3book_aug25.pdf

I don't know the history but I would guess there have been times (like the 90s) when the best neural language models were worse than the best trigram language models.