Position paper scoring trends remain an open question for authors and ACs.

In the dynamic landscape of ICML 2026, discussions have primarily centered around the main track, leaving some uncertainty regarding the position paper track.

3 min readMachine Learning

The conversation around ICML 2026 has been almost entirely consumed by the main track, leaving position paper authors and ACs to wonder if their quieter corner is moving to the same rhythm. It is a fair question, and one that deserves more than silence. When the loudest signals all come from one track, it creates an echo chamber that distorts expectations for everyone else. The absence of discussion does not mean the position paper track is stable; it may simply mean that no one is watching closely enough to notice the divergence.

For authors and ACs in the position paper track, this is not an abstract concern. It directly shapes how you read your scores, how you calibrate your revisions, and how you advise colleagues who are weighing whether to submit next year. If the main track is trending toward harsher scoring or tighter acceptance criteria, you need to know whether that pressure is uniform or isolated. Without data points from your own track, you are left guessing whether a borderline score is a rejection signal or a normal variance for position papers. That ambiguity is costly, especially when you are deciding whether to invest another cycle in a project or pivot early.

We think the real issue here is not the scores themselves, but the transparency gap. The community has built robust discussion threads for the main track, but the position paper track operates in a fog. That is a structural weakness, not a curiosity. When a track is underrepresented in public discourse, the feedback loop slows down, and the people who suffer are the ones who need the signal most: the authors making time-sensitive decisions and the ACs trying to give useful guidance. The fact that this question had to be asked on a forum rather than answered by official channels is itself a sign that the system is not serving the position paper community as well as it could.

The practical takeaway is straightforward: if you are involved in the position paper track, do not wait for a centralized summary. Start the conversation yourself. Post your own observations about score distributions, ask ACs to share aggregate trends, and push for a thread that matches the main track's visibility. The data exists, but it will only surface if enough people demand it. For now, the open question is not whether the trends are similar; it is whether the position paper track will get the same level of scrutiny it deserves. That part is up to you.

From Machine Learning

I've been seeing mainly discussions about the main track. Any ACs or other reviewers here who know if the position paper track is following similar trends as the main track?

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