Potential concerns with their self-eval:
They evaluate their automated reviewer by comparing against human evaluations on human-written research papers, and then seem to extrapolate that their automated reviewer would align with human reviewers on AI-written research papers. It seems like there are a few major pitfalls with this.
First, if their systems aren't multimodal, and their figures are lower-quality than human-created figures (which they explicitly list as a limitation), the automated reviewer would be biased in favor of AI-generated papers (only having access to the text). This is an obvious one but I think there could easily be other aspects of papers where the AI and human reviewers align on human-written papers, but not on AI papers.
Additionally, they note:
> Furthermore, the False Negative Rate (FNR) is much lower than the human baseline (0.39 vs. 0.52). Hence, the LLM-based review agent rejects fewer high-quality papers. The False Positive Rate (FNR [sic]), on the other hand, is higher (0.31 vs. 0.17)
It seems like false positive rate is the more important metric here. If a paper is truly high-quality, it is likely to have success w/ a rebuttal, or in getting acceptance at another conference. On the other hand, if this system leads to more low-quality submissions or acceptances via a high FPR, we're going to have more AI slop and increased load on human reviewers.
I admit I didn't thoroughly read all 185 pages, maybe these concerns are misplaced.