# These Aren't the Reviews You're Looking For How Humans Review AI-Generated Pull Requests

arXiv: https://arxiv.org/abs/2605.02273 · HTML: https://mlsanity.com/paper/2605.02273/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 May 2026.

## Built on by (most cited first, 2 of 2)

- [3100 Opinions on Code Review in an AI World: Building Causal Theory from Practitioner Discourse](https://mlsanity.com/paper/2607.07980/) (2026)
- [Who Finishes the Job? A Study of Follow-Up Fixes and Commit Authorship on AI Coding Agent Pull Requests](https://mlsanity.com/paper/2609.26847/) (2026)

## Abstract (arXiv)

We analyze code review interactions for AI-generated pull requests (PRs) on GitHub using the AIDev dataset and compare them to human-authored PRs within the same repositories. We find that most AI-generated PRs receive no review and, when reviewed, are largely dominated by AI agents rather than humans. Human-authored PRs are more likely to receive human-only review and to attract direct human feedback. In contrast, reviews of AI-generated PRs more often take the form of automation-mediated interaction, with human involvement frequently expressed through agent steering rather than standalone evaluation. These results indicate systematic differences in how review activity is structured in agentic workflows and raise challenges for interpreting review metrics as indicators of human oversight in large-scale mining studies.