> The advent of large language models have made this type of content relatively easy to churn out on demand, and the majority of the review articles we receive are little more than annotated bibliographies, with no substantial discussion of open research issues.

I have to agree with their justification. Since "Attention Is All You Need" (2017) I have seen maybe four papers with similar impact in the AI/ML space. The signal to noise ratio is really awful. If I had to pick a semi-related paper published since 2020 that I actually found interesting, it would have to be this one: https://arxiv.org/abs/2406.19108 I cannot think of a close second right now.

All of the machine learning papers are pure slop to me now. The last one I looked at had an abstract that was so long it put me to sleep. Many of these papers aren't attempting basic decorum anymore. Mandatory peer review would fix a lot of this. I don't think it is acceptable for the staff at arXiv to have to endure a Sisyphean mountain of LLM shit. They definitely need to push back.