The ICLR peer review guidelines are asking the right question but giving the wrong answer. A reviewer on r/MachineLearning has a straightforward dilemma: they know they must disclose AI use, so they disclose it. But they stop short of letting an LLM actually read the paper and flag something they missed, because the guidelines don't say whether that is allowed. That hesitation is rational. The guidelines are vague about what constitutes permissible assistance versus a delegation of judgment. This is not a hypothetical edge case. It is the central tension that will define peer review in the coming year, and it deserves a clear, practical answer rather than a cautious silence.
The reviewer's instinct is worth defending. They are not asking the AI to write their review. They are asking it to act as a second pair of eyes, checking for a contradiction they overlooked, a missing baseline, an unaddressed citation. That is not outsourcing judgment. That is using a tool to improve the thoroughness of a human process. In many fields, we call that a good practice. The concern, of course, is confidentiality. The reviewer is right to worry: sharing a submitted paper with a third-party LLM may violate the conference's data handling expectations, especially if the model is hosted on servers the reviewer does not control. That is a legitimate constraint, and it is one the guidelines should address explicitly. Instead, the guidelines leave the reviewer to guess, and guessing leads to underuse of a tool that could catch real errors. This connects directly to a broader call we published recently: A call to raise the bar on conference paper submissions. If reviewers are expected to hold papers to a higher standard, they should be equipped with better methods for doing so.
The practical takeaway for any reviewer reading this is simple: do not let the absence of a rule become a reason to avoid a responsible workflow. If you can run a local model that never sends the paper to an external server, you are likely on safe ground. If you must use a cloud service, check your institution's policy and the conference's stated terms first. But more importantly, push for clarity. The ICLR guidelines, and those of every major conference, should specify what data handling is acceptable and what is not. Without that, reviewers are left making judgment calls that could vary wildly from one person to the next, which undermines consistency. This is also a question of technical safety, as we explored in Understanding technical roots to prevent AI safety failures. A review process that cannot clearly define the boundaries of tool use is a process that invites inconsistent outcomes, and in peer review, inconsistency is a form of failure.
The specific question to watch is how conferences respond to this rising pressure. The reviewer who posted this is being careful. They are trying to act in good faith. But the current ambiguity means that the most conscientious reviewers will self-limit, while others may assume they have free rein. That asymmetry is a bug, not a feature. Until the guidelines draw a clear line on data sharing with LLMs, every reviewer is operating on an honor system with no shared standard. That is not sustainable, and it is not fair to the papers or the people reviewing them.