An Empirical Study of Maintainer-Identified AI Slop Pull Requests in Free and Open Source Software Projects · Hugo Raskin, Xavier Devroey and Benoît Vanderose, University of Namur · arXiv preprint, November 11, 2026
Maintainers flagged 1,786 pull requests as AI slop and merged 11
Machine-readable
Download Markdown
The authors searched 10,000 popular, mature GitHub repositories for pull requests that maintainers had titled or labelled as slop. After removing false positives they kept 1,786 PRs from 76 repositories, covering the period up to the end of July 2026. Flagged PRs rose from 62 in January 2026 to a peak of 342 in May. Of the 1,786, 97.9% were closed unmerged, with a median time to close of 0.27 days, and 11 were merged. Two repositories, huggingface/transformers (454) and pallets/click (291), account for about 42% of the dataset. Only 13.9% of authors disclosed AI use up front.
The largest group had no stated reason: maintainers closed or flagged 439 PRs without saying why. Among stated reasons, AI policy violations (141), preposterous modifications (122) and "looks AI to me" (117) lead, with checkable ones such as insufficient verification (67) and build failures (42) further down. The authors call their counts a lower bound, since the method only finds projects that use the word, and the paper is a preprint.
Why it matters: If your agents open pull requests on projects you do not maintain, expect a fast close and often no explanation. The stated reasons that can be checked, a failing build, no evidence of testing, an issue already assigned to someone else, are things a harness can check before it submits.