This is Jessica. Reviews from ICLR (one of the three biggest ML conferences) came out last Friday, and a large number of them appear to have been primarily AI-generated, at least if we believe what authors are reporting on X. I believe it. On my own submissions, about half had Pangram scores over 50% (with some papers getting majority reviews with 90+% Pangram scores). There are other signs as well, like appendix mention reportedly being nearly twice as common in reviews compared to last year.
Was this rampant AI use in review surprising? The official review policy for ICLR 2027 permitted “limited and responsible use of AI tools to assist human writing of peer reviews and meta-reviews,” but said it was “mandatory to disclose use of AI tools,” with the review form soliciting this information and asking that if you use AI you also report your original self-written paper assessment and any LM interactions.
It’s a little mind-boggling to me that anyone would still think asking people to disclose AI use is a viable policy. By now it should be common knowledge that when you say something like “mandatory to disclose use of AI tools” that will be interpreted as “suggested to disclose,” and no one will do it.
It’s also a bit mind boggling that no one anticipated this mess. At ICML 2026, many reviewers who deliberately selected into a pool that would not be allowed to use AI were caught using AI by watermarking. Turns out that when you pump high numbers of papers at each reviewer their good intentions start to crumble. And ICLR received a record number of abstract submissions – over 60,000, suggesting total valid submissions probably surpassed last year’s total near 20,000.
When reviewers feel comfortable telling authors “I have used AI tool to do the review since I do not have enough time”* something is broken. Good for that reviewer for being honest, but who is sending the impression that an AI review you produced but can’t understand is a viable way to fulfill your reviewer agreement?
This situation makes a few things clear. One is that it’s irresponsible to give reviewers high review loads but not restrict how reviewers can use AI, at least in a field like ML where many reviewers are inexperienced and required to do multiple reciprocal reviews.
The second thing is that it’s irresponsible not to prioritize preserving review over hurting authors’ feelings. ICLR did introduce a cap of 20 submissions per author, plus a limit of one paper per author if none of the authors qualified for reviewing. But it’s too conservative. Compare to the policy adopted by STOC, a CS theory conference, which this year capped submissions at five per author, dropped anonymity, required authors to submit a video in which they explain their paper, and reserved the right to desk reject for poor exposition. We can debate whether these are the right steps, but at least someone on that organizing committee had some balls.
Why letting external reviewers use AI freely is a bad idea
At this point, every author is (or should be) using AI to check their own work if they care about quality. Frontier models can find errors that you and your co-workers miss. It can suggest limitations that reviewers might bring up. I don’t send out papers anymore if I haven’t run them through Refine.ink.
So what is the point of reviewers, with less expertise on what the authors did than they have (and in ML, we’re often talking junior PhD students), meat-proxying to send them more AI-generated feedback? Seemingly none.
Consider what we lose if we ban AI in peer review. The main risk would seem to be to peer review’s supposed function as a soundness filter. LLMs enhance a reviewer’s ability to comb through 50+ pages of paper and appendix to verify that what is presented is accurate. But even if the authors are lazy and miss some problems themselves, letting individual reviewers feed papers to LLMs using basic prompts to “write a review for [conference X]” is far from the best solution. Strong LLMs too can miss major errors when not directed what to look for (see e.g., examples from Nihar Shah’s work).
Error identification and basic soundness checks are much better addressed by the venue itself, using validated AI tools. Centralize checks like whether key results can be reproduced from the provided data/code, verify whether equations, proofs, or calculated statistics are correct, detect leakage or violated assumptions, make sure key claims can be traced to evidence, and so on. There’s precedent for all of this in existing research, see Table 1 here or arguments others have made for using AI in review for such purposes here and here.
Some of these automated checks can be used to determine if a paper should be desk rejected. Some will be more controversial (and they should be, as I discuss in more detail here, because there are no hard-and-fast filters to separate good from bad science). Anything contestable can be surfaced for reviewers.
Naturally this can shift what cues get attention during review. When we give reviewers a shared summary representation of the paper, we correlate errors relative to what we’d get if they relied on their private signals alone. But these are problems that machine learning researchers can define and study! See e.g., our work formalizing what it means to have a good LLM-implemented checklist (i.e., use review corpora to learn a set of checks that reduce the combination of bias and variance under LLM-assisted review relative to the variance you’d have from human-only review). We can also educate reviewers on what their role is–-figure out what matters that can’t be verified directly across the paper (significance, novelty, etc.) and decide how to weight the surfaced checks.
But can’t good reviewers write better reviews with AI?
One counterargument to banning reviewers from using AI at their discretion is that we are crippling review quality. It’s easier to get more information from a paper if you can query an LLM about it while you read. A reviewer may reach better insights if they can rely on LLMs to help them run simulations or produce counterexamples to understand the method or theory. It’s not that all AI use is bad. It depends on whether the critiques or suggestions that AI surfaces are within the convex hull of the reviewers’ expertise. If they are, the value of their review might be amplified by AI use. But when they’re not, we get reviewers confidently reporting claims from an LLM that they cannot themselves verify, which the authors may already have decided were not good advice in their own interactions with AI. This is where review becomes pointless.
To retain the value of AI for engaging with papers, venues may also want to provide access to LLMs for interacting with the paper. This makes it possible to preserve some provenance in where claims in reviews are coming from, which authors and ACs can use to appraise reviews as needed. It becomes possible for venues to run checks that reviewers are contributing some kind of unique information to the process, rather than parroting directly. If reviewers are adding minimal information, then those reviews should be removed.
Ultimately, we need reviewers to be using their own judgment to weigh in on the significance and importance and novelty of ideas. Otherwise fields may drift in directions that are misaligned with what the humans working in those fields value. The problem with unchecked AI use is that even when people might want to stay in control of their ultimate judgments, they are not always good at recognizing when they are taking on biases reflected by LLM output. There was a study out of Anthropic recently where they endowed LLMs with preferences that made them lean toward particular recommendations, then had people use them to do a task. The majority of the time, people adopted the preference that was endowed to their LLM, and many of them also attributed the recommendations to themselves.
Why are we still protecting authors’ rights to submit slop?
Reviewers turning to AI en masse is a symptom of conferences being hesitant about desk reject policies. For example, at the top ML conferences, one can still submit a mostly AI-produced paper and expect a team of human reviewers to provide feedback. I’ve heard many students who are reviewing for ICLR this year complain about how many papers in their review piles were hard-to-read AI slop.
Organizers add some minimal friction for authors, and expect the reviewers to suck it up and deal with the remaining onslaught. But when you don’t prioritize reviewers’ ability to actually do their job by requiring more authorial accountability, you hasten the “review death spiral”, where as review gets noisier, authors are further incentivized to submit low quality papers, in the hopes that they get lucky.
There are better ways to deal with this problem than the ultra-conservative don’t-hurt-any-feelings approaches we’ve been seeing. We should not be seeing teams of undergrads submitting 9 papers at a time only to withdraw them once they see the initial reviews, as reported by one AC. For example, start desk rejecting papers that are flagged by Pangram as mostly AI-written, or that are very hard to read. If an author can’t take the time to signal that there is a human on the other hand who is accountable for the work, do we really owe them 3+ other humans’ time providing feedback?
I really hope that policies tighten. As imperfect as peer review has always been, removing people from it entirely is not going to lead us to better science.
*From a screenshot of a review shared on X before the author took it down.
