Navigating a tough review cycle: insights from SIGIR 2026 outcomes

As we eagerly await the results of SIGIR 2026, I’m opening this thread to foster a discussion on the reviews and outcomes of this year's submissions.

2 min readMachine Learning

This review cycle for SIGIR 2026 tells us something important, and it's not that the work was weak. It's that the system is tightening. When one reviewer reports that all ten papers they handled were rejected, the signal isn't about individual quality, it's about a field raising the bar faster than most researchers can adjust.

We think this is a moment to step back and ask what that bar actually measures. A tough review cycle can mean reviewers are demanding more rigor, more novelty, or both. But it can also mean that the evaluation criteria themselves haven't caught up to the kind of work the community needs. If every paper that challenges an established method gets dinged for not following the expected template, the field doesn't advance, it just polishes what already works. That's a trap.

For researchers, this isn't just a discouraging season. It's a practical signal to examine how you're framing your contribution. If your paper addresses a real bottleneck in information retrieval but gets rejected because it didn't benchmark against the same three datasets everyone else uses, the issue isn't your idea. It's that you haven't told the story in the language the reviewers are listening for. That's fixable. The best response to a tough cycle isn't to lower your ambition, it's to sharpen your narrative.

The practical takeaway is this: look at the reviews you do get, even the rejections, and ask whether the criticism targets your method or your framing. If it's the latter, rewrite the introduction. If it's the former, test harder before the next submission. The field doesn't need less work, it needs work that knows how to speak to the room.

From Machine Learning

SIGIR 2026 results will be released soon, so I’m opening this thread to discuss reviews and outcomes.

Unfortunately, all the papers I reviewed (4 full papers and 6 short papers) were rejected. It seems like this year has been particularly tough for everyone.

Read the original at Machine Learning