reviewers

Reviewer feedback remains pending as rebuttal process continues

Waiting on reviewer feedback is a specific kind of limbo, and it sounds like you are handling it with a clear head.

3 min readMachine Learning

The wait for reviewer feedback is one of the most exposed positions in academic publishing. You have done the work, addressed the concerns, and now you are waiting for a response that may never come. The researcher is living that reality, with two solid reviews at 3 and 4, and one outlier at 2 with high confidence. What makes this frustrating is not the score itself, but the reasoning. The reviewer cited a limitation that the author had already included in the paper, which the conference's own rules say should not be used as a weakness. That is not a difference of opinion. That is a procedural misstep, and it is fair to want it corrected.

This is where the system shows its humanity, and its flaws. The researcher is asking whether they should message the Area Chair after two days of silence. The answer is yes, but with a clear head. The rebuttal period exists for dialogue, and if a reviewer chooses not to engage, that is their right. But the AC is there to ensure the process is fair, not perfect. A concise, factual note that you have addressed the comments and are awaiting clarification is not aggressive. It is professional. It signals that you are tracking the process and that you expect the same rigor from others that you have applied to your own work.

This moment also reflects a larger tension in how we evaluate research, one that shows up beyond the review process. In Exploring Real-World Computer Vision: Deployments, Edge Models, and Current Challenges, the conversation centers on practical constraints and what actually works in production. Similarly, ICLR Submissions Exposed: Addressing Data Privacy Concerns in AI Research highlights how process failures can overshadow technical merit. And Explore the Forrester Function: Beyond Mathematics, a Tool for Machine Learning reminds us that even abstract tools carry assumptions about their use. The pattern is consistent: the gap between what a method promises and how it is assessed is where the real friction lives.

The takeaway for anyone in this position is simple. Do not let a single outlier define your submission, but do not let it slide either. The researcher's instinct to wait two days is reasonable, and sending a brief note to the AC is a legitimate step. The process is not a black box, but it only works when participants push back with evidence. This is not about being difficult. It is about holding the review process to the same standard we hold our own work. If a limitation is dismissed once, that is feedback. If it is dismissed twice, that is a signal the conversation needs to move up a level. Watch for that moment. It is the difference between accepting the outcome and shaping it.

From Machine Learning

The rebuttal is still ongoing so this could also change, but my reviewers have not responded back to my responses yet. I know they have the right not to reply. One of the reviewers gave a 2 with a 5 confidence and the whole review is actually one of the many limitation I included in the paper. Limitations are not to be used as a weakness according to the conference own rules. The other two reviews are 3,4 which I am happy with although I did address their points. Should I send a confidential message to AC if nothing happens…

Read the original at Machine Learning