Should I follow-up with the editor for a TMLR paper awaiting final decision? [D]

The author of this post is living a familiar academic nightmare: a paper with mostly positive reviews, a promised decision deadline in late March, and now May with silence. It’s a situation that feels both personal and systemic—a tiny, slow-moving cog in a giant machine that has enormous consequences for one career. The tension between patience and proactive follow-up is something nearly every researcher has faced, and for those navigating the job market, the stakes are existential. We’ve seen similar frustrations before, as discussed in our coverage of [TMLR reviews stalled [D]](/post/tmlr-reviews-stalled-d-cmntxet8h08jxzxsxu7rg6hnf), where delays in the review pipeline tested authors’ composure. This is not an isolated case; it reflects a deeper misalignment between the ideals of rapid, transparent peer review and the reality of human schedules.

Why does this matter beyond one person’s anxiety? TMLR (Transactions on Machine Learning Research) was designed, in part, to be a faster, more community-driven alternative to traditional journals. The promise of a “fast-paced process” is a core selling point for authors who need timely decisions for grants, tenure, or job applications. When that promise breaks down, trust erodes. The issue here isn’t rudeness or incompetence—the editor and reviewers are likely overburdened. But the lack of communication, the missed March deadline, and the absence of any update create a vacuum that fills with worry. The author’s question—“Should I follow up?”—reveals a broader need for accountability signals in academic publishing. When systems fail to deliver on their own timelines, authors should not have to second-guess basic professional courtesy.

From an authoritative yet human-centered perspective, the answer is clear: yes, you should follow up, but with tact and clarity. A gentle reminder to the Action Editor is entirely appropriate after two months of silence, especially when a deadline was set. The medium matters: OpenReview’s private comment (visible only to the editor) is the safer choice, as it preserves the paper trail and avoids cluttering email inboxes. Mentioning that the paper is time-sensitive for the job market is not only acceptable—it’s responsible. Editors need context to prioritize. The key is to frame it as a request for an update, not an accusation: “I understand reviewers are busy, but I would be grateful for any status update, as this decision affects my job search timeline.” This is respectful, action-oriented, and aligns with the ethos of collaborative review.

Looking forward, this episode points to a larger question worth watching: How can peer review systems adopt the transparency and automation that modern workflows demand? Imagine a spreadsheet-native tool that tracks review progress, sends automatic nudges, and gives authors real-time visibility into where their paper stands. That’s not a fantasy—it’s the direction AI-native data management can take us. The academic community deserves tools that empower, not erode, trust. Until then, the humble follow-up remains our best, and often only, lever.