I flagged two research papers for fake authors and both were accepted as orals

But is there a not-simple metric?

The advantage of "publish or perish" is that it is based on something concrete. The system is gamed, for sure, and even more so with AI, but still, a paper which is cited a lot tend to be useful and the authors get rewarded.

Remove that metric, and for the lack of a better idea, it will just turn into a game of who has the best connections or who talks the most convincingly, I mean, even more than it is now.

> you get nothing out of it but karma or a sticker, and the platform/publisher gets all the benefit.

Peer review means 3 people spend their time to review your work and you don’t have to pay them a cent, so you review others' work for free. The compensation for reviewing is reviews.

They’re not always great (sometimes they’re downright nasty), but I’ve gotten enough high quality reviews over the years that I’m willing to say the time I’ve spent reviewing others’ work has been fairly compensated.

It is in alpha (hoping to do Show HN in a few weeks), but for those interested I am working on an application to do this, https://veruscite-data.com/

Most of the folks on HN will be more familiar with genAI tools and can just use the skill Caleb and Isaac provided in this blog post. My tool is just likely more token efficient and has a GUI where you can review the extracted bib and edit it more easily.

and don't forget, techniques described in papers are now implemented by AI's. For now, a human might point an AI at a paper and ask it to implement and benchmark the technique described there, but the human probably isn't writing the code anymore.

I was wondering why they don’t just feed the papers first to an LLM to spot obvious slop, and I was answered later in the post:

> Submissions are confidential, bibliographies included, and the audit works by sending pieces of one to a hosted LLM – even though the LLM never writes a word of your review. ECCV 2026’s reviewing policies state that LLMs “are NOT allowed to be used to write reviews or meta-reviews, whether it is run locally or via an API,” and separately bar reviewers from sharing substantial excerpts of a submission with an LLM. WACV’s reviewer guidelines call LLM-generated reviews “highly irresponsible behavior,” sanctionable by desk rejection of the reviewer’s own papers, and their confidentiality rules forbid showing a submission’s material to anyone who is not a reviewer – which a hosted LLM is not. NeurIPS’s LLM policy restricts what reviewers can share with LLM services; its AI-assisted reviewing experiment is the sanctioned route.

So you can send some LLM generated crap to be published and even if you get caught, there is no consequences. Since your funding is likely connected to how much you publish, even if it’s toilet paper, so this system actually rewards one from spewing out shit papers no-one reads. But if you use LLM to review them, guess what, you will get punished harshly.

I think if you send in LLM crap with hallucinated citations you should get 5 year ban on even sending anything to that conference or publication. And perhaps we should create a local model -based application that filters out this crap. The one the authors had made is a good start, but surely you don’t need Claude to review a bibliography for errors? Surely Qwen with a SearXNG limited to arxiv etc. can do the job?