The confusion in that NeurIPS thread is entirely understandable, and it points to a deeper friction in how machine learning research is evaluated. When a venue changes its communication channels mid-stream, the burden shifts onto authors to decode the process while managing the emotional weight of a decision that could affect their careers. The user, u/ihatesalad1, is not asking a trivial logistics question. They are asking how to present their research in a way that is both honest and effective when the rules of engagement are unclear. This is the real problem worth addressing.
We often talk about AI and spreadsheets in terms of data transformation, but the same principle applies here: clarity is a feature, not a luxury. The meta-reviewer process is supposed to be a structured dialogue, yet the distinction between a confidential AC comment and a public rebuttal is significant. Posting a response in the wrong place could mean it is never seen by the right people, or worse, it could be perceived as circumventing the review process. In a field where Clean Data Starts With Catching AI Slop Before It Skews Your Model matters for model integrity, the same logic applies to communication integrity. You do not want your rebuttal lost in a poorly labeled channel.
What would we tell a reader who asked? First, do not panic. The fact that the option to post a public comment opens on July 28th is not a trap. It is a signal that the area chair wants reviewers to see the exchange. That is a good thing. It means the AC is looking for a collaborative resolution, not a unilateral one. So use the public comment for your substantive response to the meta-review, but keep your confidential AC comment for any sensitive context that should not be shared with the broader reviewer group. This is not about gaming the system; it is about using the tools as intended. The system is flawed, but it is not malicious.
The deeper issue here is that the review process is becoming a complex system in its own right, and like any complex system, it requires careful navigation. Consider the parallels in Exploring Real-World Computer Vision: Deployments, Edge Models, and Current Challenges. In that context, we see that deployment challenges are rarely about the model itself, but about the environment it operates in. Here, the model is your paper, and the environment is the review process. You cannot control the environment, but you can control how you adapt. The same goes for Explore the Forrester Function: Beyond Mathematics, a Tool for Machine Learning, where understanding the underlying function is more valuable than memorizing the formula. Understand the function of the meta-reviewer: they are not your adversary. They are a synthesizer of opinions, and your job is to give them the material to synthesize accurately.
Our take is simple. The confusion is a symptom of a process that has not fully caught up with its own ambition. But that does not mean you should wait for clarity. Take the initiative. Post your response publicly when the window opens, summarize your key points for the AC privately, and be transparent about what you have changed and why. If you are unsure whether a comment should be public or private, err on the side of transparency. The worst outcome is not a rejected paper; it is a rejected paper where the authors never made their best case because they were too worried about the mechanics. The concrete takeaway is this: on July 28th, treat the public comment as your primary voice, and use the confidential comment only for information that is not appropriate for the reviewers. Watch for the AC's acknowledgment. That response will tell you more about the process than any tweet ever will.