Stay informed: know when reviewers update their final feedback.

In the context of the ICML review process, it’s crucial to understand how final justifications from reviewers are communicated.

3 min readMachine Learning

The question of whether researchers are notified when reviewers update their original feedback with final justifications is one that strikes at the heart of the academic review process. For too long, the system has operated as a one-way street: reviewers submit their thoughts, authors respond, and then everyone waits in the dark. If you are a researcher who has refreshed your inbox a hundred times hoping for clarity, only to find silence, you know the frustration. Our take is simple: the lack of notification is a missed opportunity for transparency, and it is a problem worth solving with the same urgency we bring to our data.

What this means for you, practically, is that the current workflow leaves critical information stranded. When a reviewer adds a final justification to an existing comment, that update might as well be invisible if you are not manually checking every thread. This is not just an inconvenience; it is a breakdown in communication that can stall your progress or, worse, lead you to resubmit with unresolved concerns. The person who asked this question on Reddit is not alone in their confusion. They are pointing to a gap in the system that many of us have simply accepted as normal. But accepting it does not make it right. You deserve to know when the conversation has moved, especially when that movement could shape your next revision.

The practical takeaway here is that you should not rely on passive waiting. If the platform you are using does not send alerts for these updates, build your own checkpoints. Schedule time to revisit reviewer comments before deadlines, and treat any new text as a signal to re-engage. This is not ideal, but it is workable. The broader point is that the tools we use for research collaboration should be smarter about surfacing changes, not just storing them. We have seen how AI can transform spreadsheets from static grids into active partners in analysis. The same logic applies here: if a system can tell you when a cell changes, it should be able to tell you when a reviewer changes their mind.

So, what is the concrete next step? Do not wait for a notification that may never come. Build a habit of checking the original review threads right before you plan to respond, and treat any newly added justification as a priority. And if you are building or choosing the next generation of research tools, demand that this kind of update triggers a visible alert. The technology exists. The question is whether we will stop accepting silence as a feature and start asking for the transparency we deserve.

From Machine Learning

Do we get notified if any reviewer put their final justification into their original review comment?

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