Submitting to a major conference for the first time is a vulnerable experience. You have done the work, written the rebuttal, and now you are waiting for a signal that the process makes sense. When the meta-review arrives but the Area Chair stays silent, it is natural to wonder if you missed a step. One researcher recently asked exactly that on the community forum: are ACs supposed to give ratings during the author-reviewer discussion phase, or is silence standard? It is a fair question, and the honest answer is that the silence is not a bug. It is the design.
The confusion stems from a mismatch between what authors expect and what the system is built to do. In many venues, the AC's role is not to participate in every comment thread or offer a running score. Their job is to synthesize the reviewer discussions, weigh the meta-review against the conversation, and deliver a decision. That is why you received the meta-review. The AC has already given you a rating, in the form of that document. Expecting additional real-time feedback during Phase 2 is like asking an editor to annotate every draft while also writing the acceptance letter. It is not how the workflow is structured, and it is not a reflection of your submission's quality.
This is where the broader conversation about transparency in peer review becomes practical. If you are navigating this for the first time, you are also learning how to read the room. The same skill applies when you are exploring real-world computer vision deployments or debugging a model that behaves unpredictably. You look for the signal in the system, not the noise. That is why we would tell you this: do not treat the AC's silence as a verdict. Instead, focus on the meta-review's language. Does it mention specific strengths or weaknesses? Does it align with the reviewer scores? That is your real data. Similarly, when you are catching AI slop before it skews your model, you do not panic over a single mislabeled example. You look at the aggregate pattern. The same principle applies here.
What we find more interesting is what this reveals about the culture of machine learning research. We are used to instant feedback loops. Chat interfaces, automated tests, and continuous integration have trained us to expect a response within minutes. Conferences do not work that way, and they should not have to. The AC's role is to be a fair arbiter, not a tutor. If you want more clarity, the practical move is not to wait for comments. It is to read the meta-review carefully, compare it to the reviewer discussion, and then decide on your next step. If you are still unsure, asking the AC a direct, concise question after the decision is acceptable. But during the discussion phase, silence is not a slight. It is a signal that your file is being handled according to protocol.
The takeaway worth quoting is this: your paper's fate does not hinge on how chatty your AC is during the rebuttal. It hinges on how well your work survives the review process, and the meta-review is the record of that survival. So if you are new to this, stop refreshing the thread. Start reading the meta-review like a researcher reads a model's loss curve, looking for the trend, not the individual ticks. And if you are curious about how to think about these kinds of invisible processes, consider how exploring the Forrester function teaches you to separate the function's behavior from the noise of the optimization path. The same discipline applies here. Watch the decision, not the commentary. That is where the signal lives.