# AgenticFlict: A Large-Scale Dataset of Merge Conflicts in AI Coding Agent Pull Requests on GitHub

arXiv: https://arxiv.org/abs/2604.03551 · HTML: https://mlsanity.com/paper/2604.03551/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use.
- Builds on 12 of its 61 references that are in the map.
- First posted on arXiv 4 Apr 2026.

## Placed in

- LLM agents and tool use / [Software engineering coding agents](https://mlsanity.com/agents/software-engineering-coding-agents/)

## Builds on (most cited first, 12 of 12)

- [The Impact of AI on Developer Productivity: Evidence from GitHub Copilot](https://mlsanity.com/paper/2302.06590/) (2023)
- [Productivity Assessment of Neural Code Completion](https://mlsanity.com/paper/2205.06537/) (2022)
- [Agentic Software Engineering: Foundational Pillars and a Research Roadmap](https://mlsanity.com/paper/2509.06216/) (2025)
- [Towards AI-Native Software Engineering (SE 3.0): A Vision and a Challenge Roadmap](https://mlsanity.com/paper/2410.06107/) (2024)
- [AI-Assisted Assessment of Coding Practices in Modern Code Review](https://mlsanity.com/paper/2405.13565/) (2024)
- [DevGPT: Studying Developer-ChatGPT Conversations](https://mlsanity.com/paper/2309.03914/) (2023)
- [How AI Coding Agents Modify Code: A Large-Scale Study of GitHub Pull Requests](https://mlsanity.com/paper/2601.17581/) (2026)
- [Program Merge Conflict Resolution via Neural Transformers](https://mlsanity.com/paper/2109.00084/) (2021)
- [Code Review Comprehension: Reviewing Strategies Seen Through Code Comprehension Theories](https://mlsanity.com/paper/2503.21455/) (2025)
- [Refactoring-Aware Patch Integration Across Structurally Divergent Java Forks](https://mlsanity.com/paper/2508.06718/) (2025)
- [Blockchain-Oriented Software Variant Forks: A Preliminary Study](https://mlsanity.com/paper/2204.11083/) (2022)
- [Structural and Connectivity Patterns in the Maven Central Software Dependency Network](https://mlsanity.com/paper/2508.13819/) (2025)

## Built on by (most cited first, 5 of 5)

- [ATM: CID-Brokered Pre-Write Admission for Multi-Agent Code Co-Synthesis](https://mlsanity.com/paper/2607.00041/) (2026)
- [AI Agent Pull Requests on GitHub: Frequency, Structure, and Merge Conflict Rates](https://mlsanity.com/paper/2607.04697/) (2026)
- [EA-Graph: Artifact-Anchored Verification Memory for Coding Agents under Upstream Drift](https://mlsanity.com/paper/2608.04278/) (2026)
- [Govern the Repository, Not the Agent: Measuring Ecosystem-Level Risk in AI-Native Software](https://mlsanity.com/paper/2606.28235/) (2026)
- [Passes Alone, Fails Together: Benchmarking Semantic Coordination in Parallel LLM-Agent Development](https://mlsanity.com/paper/2609.25396/) (2026)

## Abstract (arXiv)

Software Engineering 3.0 marks a paradigm shift in software development, in which AI coding agents are no longer just assistive tools but active contributors. While prior empirical studies have examined productivity gains and acceptance patterns in AI-assisted development, the challenges associated with integrating agent-generated contributions remain less understood. In particular, merge conflicts, a fundamental aspect of collaborative software development, remain underexplored in this context. In this paper, we present AgenticFlict, a large-scale dataset of textual merge conflicts in AI coding agent pull requests (Agentic PRs). The dataset comprises 142K+ Agentic PRs collected from 59K+ repositories, of which 107K+ are successfully processed through deterministic merge simulation. Our pipeline identifies 29K+ PRs exhibiting merge conflicts, yielding a conflict rate of 27.67%, and extracts 336K+ fine-grained conflict regions across these instances. Our preliminary exploratory analysis indicates that merge conflicts are both frequent and often substantial in AI-generated contributions, with noticeable variation across agents, emphasizing the need to better understand and manage integration challenges in AI-assisted software development. The dataset, code and supplementary materials are available in zenodo: https://doi.org/10.5281/zenodo.19396916.