my suggestion is more extreme than most I'm seeing here in the comments... but desperate times call for desperate measures, and as the 60k ICLR submissions show, the ML community has been too slow to rise to the challenge. the gist of my proposal is to institute two parallel submission tracks; let's call them Track A and Track B. at submission time, authors choose which track. in Track B, papers are reviewed and judged entirely by AI. This is not to say that it's an accept-all, AI-slop track: the conference still sets standards for acceptance, and papers that don't meet them are rejected. The difference is that those standards are enforced by an AI review process, not by human reviewers. each conference can design this process however it likes, based on the principles it values and acceptance criteria set by its human steering committee. It could choose to mimic existing reviewing practices (blind reviews, multiple reviewers, discussion, AC/meta-review), or invent something entirely different from scratch. I expect this process to improve empirically over time. in fact, the community can actively research better methods for AI review, test where they fail, and iterate from conference to conference. this process itself should be public and scrutible; the new open review, of you'd like. crucially, in Track B, there is no limit on the number of submissions. in Track A, on the other hand, papers are reviewed and judged by human reviewers, much as they are today. But there is a hard cap on how many papers an individual can submit to this track, to keep the demand for human attention under control. I have in mind a cap much smaller than what people are currently churning ou, say, something on the order of 1–3 papers per author per conference. The exact mechanism is a design question; the important part is that access to human review is deliberately scarce. are you a PI whose students typically produce 10–15 papers per conference? great. think hard about which ones would really benefit from human review, or which contributions you especially want the community to pay attention to. submit those to Track A, the rest to Track B. in the longer run, maybe this actually aligns incentives much better. e.g. two talented students could be incentived to join forces on one really ambitious project with their PI as author, instead of producing six incremental works between them without their PI as author. I don't think it's controversial to say the community would benefit from fewer, more substantial research contributions, and I think this has been true for years. AI agents coming for our peer-review process may simply be the tsunami strong enough to finally get enough stakeholders onboard to change the status quo.