[2607.04697] AI Agent Pull Requests on GitHub: Frequency, Structure, and Merge Conflict Rates

Skip to main content

arXiv is now an independent nonprofit!<br>Learn more<br>&times;

Search arXiv

Press Enter to search &middot; Advanced search

-->

Computer Science > Software Engineering

arXiv:2607.04697 (cs)

[Submitted on 6 Jul 2026 (v1), last revised 7 Jul 2026 (this version, v2)]

Title:AI Agent Pull Requests on GitHub: Frequency, Structure, and Merge Conflict Rates

Authors:George Xu, Arjun Subramanian, Nithilan Karthik<br>View a PDF of the paper titled AI Agent Pull Requests on GitHub: Frequency, Structure, and Merge Conflict Rates, by George Xu and 2 other authors

View PDF<br>HTML (experimental)

Abstract:AI coding agents may generate and submit Pull Requests (PRs) to the same repository at the same time. However, research concerning the extent of concurrent submission by AI coding agents to a common repository does not exist. This paper uses the AIDev-pop dataset (33,596 PRs in 2,807 repositories) to provide the first empirical examination of the prevalence of concurrent submission using PRs authored by agents. We report that when considering exact temporal overlap, 40.2% of repositories contain co-active agent-authored PR pairs; further, the co-active pairs account for 79.4% of all PRs generated by an AI agent. When we examine co-activity within a one week collaboration window, the percentages are increased to 53.4% and 95.0%, respectively. For the majority of the co-active PR pairs (underlying the vast majority of which are intra-agent authored), both PRs were authored by the same agent, while only 0.5% of co-active pairs were cross-agent, and occurred in only 122 out of 2807 total repositories examined (or approximately 4.3%). Additionally, we replayed actual three way git merges on 747 unique co-active pairs (one per repository), and computed the percentage of textual conflict encountered during the merge operation to combine the two PRs in each pair. We observed that the percentage of textual conflict encountered was significantly higher for cross-agent pairs compared to intra-agent pairs: 41.7% vs. 19.8%, respectively, with non-overlapping 95% confidence intervals. Lastly, we developed a classification system based on the detection of conflict reported by git, and determined that the majority of conflicts resulted from modifications to source code files (84.4% of conflicted files) and not dependency manifest files; further, nearly 42% of conflicts we observed were structural (i.e., modify/delete or add/add).

Comments:<br>7 pages, 4 figures, 2 tables. Replication package: this https URL

Subjects:

Software Engineering (cs.SE)

Cite as:<br>arXiv:2607.04697 [cs.SE]

(or<br>arXiv:2607.04697v2 [cs.SE] for this version)

https://doi.org/10.48550/arXiv.2607.04697

Focus to learn more

arXiv-issued DOI via DataCite

Submission history<br>From: George Xu [view email]<br>[v1]<br>Mon, 6 Jul 2026 05:58:12 UTC (269 KB)

[v2]<br>Tue, 7 Jul 2026 02:40:06 UTC (269 KB)

Full-text links:<br>Access Paper:

View a PDF of the paper titled AI Agent Pull Requests on GitHub: Frequency, Structure, and Merge Conflict Rates, by George Xu and 2 other authors<br>View PDF<br>HTML (experimental)<br>TeX Source

view license

Current browse context:

cs.SE

next >

new<br>recent<br>| 2026-07

Change to browse by:

cs

References & Citations

NASA ADS<br>Google Scholar

Semantic Scholar

export BibTeX citation<br>Loading...

BibTeX formatted citation

&times;

loading...

Data provided by:

Bookmark

Bibliographic Tools

Bibliographic and Citation Tools

Bibliographic Explorer Toggle

Bibliographic Explorer (What is the Explorer?)

Connected Papers Toggle

Connected Papers (What is Connected Papers?)

Litmaps Toggle

Litmaps (What is Litmaps?)

scite.ai Toggle

scite Smart Citations (What are Smart Citations?)

Code, Data, Media

Code, Data and Media Associated with this Article

alphaXiv Toggle

alphaXiv (What is alphaXiv?)

Links to Code Toggle

CatalyzeX Code Finder for Papers (What is CatalyzeX?)

DagsHub Toggle

DagsHub (What is DagsHub?)

GotitPub Toggle

Gotit.pub (What is GotitPub?)

Huggingface Toggle

Hugging Face (What is Huggingface?)

ScienceCast Toggle

ScienceCast (What is ScienceCast?)

Demos

Demos

Replicate Toggle

Replicate (What is Replicate?)

Spaces Toggle

Hugging Face Spaces (What is Spaces?)

Spaces Toggle

TXYZ.AI (What is TXYZ.AI?)

Related Papers

Recommenders and Search Tools

Link to Influence Flower

Influence Flower (What are Influence Flowers?)

Core recommender toggle

CORE Recommender (What is CORE?)

Author

Venue

Institution

Topic

About arXivLabs

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with...