# PAGENT: Learning to Patch Software Engineering Agents

arXiv: https://arxiv.org/abs/2506.17772 · HTML: https://mlsanity.com/paper/2506.17772/

## Key facts

- Cited by 4 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 15 of its 35 references that are in the map.
- First posted on arXiv 21 Jun 2025.

## Placed in

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

## Builds on (most cited first, 15 of 15)

- [GPT-4o System Card](https://mlsanity.com/paper/2410.21276/) (2024)
- [SWE-bench: Can Language Models Resolve Real-World GitHub Issues?](https://mlsanity.com/paper/2310.06770/) (2023)
- [SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering](https://mlsanity.com/paper/2405.15793/) (2024)
- [The Rise and Potential of Large Language Model Based Agents: A Survey](https://mlsanity.com/paper/2309.07864/) (2023)
- [Agentless: Demystifying LLM-based Software Engineering Agents](https://mlsanity.com/paper/2407.01489/) (2024)
- [AutoCodeRover: Autonomous Program Improvement](https://mlsanity.com/paper/2404.05427/) (2024)
- [RepairAgent: An Autonomous, LLM-Based Agent for Program Repair](https://mlsanity.com/paper/2403.17134/) (2024)
- [LLM-Based Multi-Agent Systems for Software Engineering: Literature Review, Vision and the Road Ahead](https://mlsanity.com/paper/2404.04834/) (2024)
- [From LLMs to LLM-based Agents for Software Engineering: A Survey of Current, Challenges and Future](https://mlsanity.com/paper/2408.02479/) (2024)
- [Impact of Code Language Models on Automated Program Repair](https://mlsanity.com/paper/2302.05020/) (2023)
- [Automated Repair of Programs from Large Language Models](https://mlsanity.com/paper/2205.10583/) (2022)
- [UniDebugger: Hierarchical Multi-Agent Framework for Unified Software Debugging](https://mlsanity.com/paper/2404.17153/) (2024)
- [Otter: Generating Tests from Issues to Validate SWE Patches](https://mlsanity.com/paper/2502.05368/) (2025)
- [Memory-Efficient Large Language Models for Program Repair with Semantic-Guided Patch Generation](https://mlsanity.com/paper/2410.16655/) (2024)
- [PatchZero: Zero-Shot Automatic Patch Correctness Assessment](https://mlsanity.com/paper/2303.00202/) (2023)

## Built on by (most cited first, 4 of 4)

- [Agentic Software Issue Resolution with Large Language Models: A Survey](https://mlsanity.com/paper/2512.22256/) (2025)
- [From Trace to Line: LLM Agent for Real-World OSS Vulnerability Localization](https://mlsanity.com/paper/2510.02389/) (2025)
- [ORACLE-SWE: Quantifying the Contribution of Oracle Information Signals on SWE Agents](https://mlsanity.com/paper/2604.07789/) (2026)
- [One Tool Is Enough: Reinforcement Learning for Repository-Level LLM Agents](https://mlsanity.com/paper/2512.20957/) (2025)

## Abstract (arXiv)

LLM Agents produce patches automatically to resolve an issue. However, they can generate inaccurate patches. Little is known about the root causes behind those failed patches or how those could be fixed. This paper reports an empirical study of the failed patches generated by seven top LLM code agents. We collected 114 issues from the SWE-bench Lite dataset that remained unresolved across the agents. The seven agents produced a total of 769 failed patches for those issues, which we checked with a combination of GPT-4o and manual analysis. We present a taxonomy of the failure reasons across the patches. The taxonomy contains six categories, with several sub-categories under each category. For example, a frequently observed category is the inability of an LLM to correctly infer/produce the appropriate variable type in the produced patch. As a first step towards addressing such type-related errors, we designed PAGENT (Patch Agent). PAGENT utilizes program analysis techniques like CFG creation and exploration to infer the type of information of a patch. PAGENT does this by applying repository-level static code analysis techniques. Then, PAGENT refines the inferred type by further utilizing an LLM-based inference technique. We tested PAGENT on all 127 type-related failed patches from the top three agents in our study. PAGENT could fix 29 of the 127 failed patches.