Yes, ICLR submissions have gone 11k to 20k to 60k in 2025, 26, 27.
Most reviewers hate reviewing, don't get rewarded for it, the papers they reward are usually bad (as acceptance rate is around 20-25%), so they are forced to do it via reciprocal reviewing rules where submitter must also review, but they review with AI, the area chairs write their meta review with AI, they (area chairs) can arbitrarily reject a paper even with "accept" reviews, delaying people's graduation by half a year or more. Also, meanwhile much of the research in eg AI ML application areas where you tweak the architecture and beat a benchmark (vast majority of the tens of thousands of submissions) can now be done better with AI.
Yes there are elite academics who do deep foundational thoughtful stuff but the system is totally subverted because most participants don't give a shit and don't feel any long term allegiance to the "research community". They take what they can, and are out very soon. Of course this means the signal and prestige of papers on your CV will tank. And having too many may have the opposite effect l already today.
> Also, meanwhile much of the research in eg AI ML application areas where you tweak the architecture and beat a benchmark (vast majority of the tens of thousands of submissions) can now be done better with AI.
This is an underappreciated point. I was telling someone in the lab the other day after we saw our ICLR submission ID... that it more so revealed how much existing AI/ML research was very nearly slop.
I think when push comes to shove, academics will have to admit that this volume of papers even pre-AI just wasn't all about novel groundbreaking work. Now, mathematicians are starting to admit that oh the theorems and proofs don't actually matter, they are just steppings stones and learning opportunities for PhD students to hone their skills, and to form a community around etc.
Same way, most published papers are much more about a junior researcher gaining experience, learning how to speak clearly in a talk, how to summarize work in poster format, how to write, how to make figures, etc. etc. even if the final result is meh, they kind of got initiated into all this song and dance and that's what the academics really want to do, to pass on their culture basically. When this experience actually gets "truly" deployed is a good question though, because once you reach that level, you basically become a manager and all your later papers will be again written by the juniors honing their skills that you hired, because you no longer have time to do hands-on research once you were actually starting to become good at it, because now you just have meetings, write grant applications, sit on committees, teach courses, go to faculty meetings, help co-organize conferences etc. etc.
I'm convinced that engineering centers where after a point, you head a center for something something raise some money for it and it is much better than typical late-career academia work. One of my advisors does this and they enjoy it a lot. It's like a academia-startup hybrid. One other is a HoD and while they like it, it's more like what you describe.
Anyways the point is that, students working in that long term goal oriented project oriented setting is in many ways nicee than the typical "paper mill" approach. They still do get all the skills you mentioned since publishing something by yourself is mandatory still, but they spend a lot of time on practical problems.
Most reviewers hate reviewing, don't get rewarded for it, the papers they reward are usually bad (as acceptance rate is around 20-25%), so they are forced to do it via reciprocal reviewing rules where submitter must also review, but they review with AI, the area chairs write their meta review with AI, they (area chairs) can arbitrarily reject a paper even with "accept" reviews, delaying people's graduation by half a year or more. Also, meanwhile much of the research in eg AI ML application areas where you tweak the architecture and beat a benchmark (vast majority of the tens of thousands of submissions) can now be done better with AI.
Yes there are elite academics who do deep foundational thoughtful stuff but the system is totally subverted because most participants don't give a shit and don't feel any long term allegiance to the "research community". They take what they can, and are out very soon. Of course this means the signal and prestige of papers on your CV will tank. And having too many may have the opposite effect l already today.