perfagent-profiler-guided-iterative-refinement-for

name	perfagent-profiler-guided-iterative-refinement-for
description	Skill generated from arXiv paper 2607.19653: PerfAgent: Profiler-Guided Iterative Refinement for Repository-Level Code Optimization
metadata	{"arxiv":{"id":"2607.19653","title":"PerfAgent: Profiler-Guided Iterative Refinement for Repository-Level Code Optimization","authors":["Ryan Deng","Yuanzhe Liu","Bastian Lipka","Yao Ma","Xuhao Chen","Tim Kaler","Jatin Ganhotra"],"published":"2026-07-22","categories":["cs.SE","cs.AI"],"url":"https://arxiv.org/abs/2607.19653","utility":0.97}}

PerfAgent: Profiler-Guided Iterative Refinement for Repository-Level Code Optimization

arXiv: 2607.19653
Published: 2026-07-22
Authors: Ryan Deng, Yuanzhe Liu, Bastian Lipka, Yao Ma, Xuhao Chen, Tim Kaler, Jatin Ganhotra
Categories: cs.SE, cs.AI
Utility: 0.97

Key Innovation

Large language model (LLM) agents now perform well on correctness-oriented repository-level tasks, including SWE-Bench issue resolution and feature implementation in real codebases. However, they still struggle with repository-level code optimization, which requires preserving behavior while improving runtime performance. Passing tests is not enough in this setting; a patch must preserve behavior, implement code optimization, and approach expert speedups. Current agents often miss bottlenecks hi...

Potential Application

This paper presents advancements that could be applied to enhance agent capabilities in the areas of cs.SE, cs.AI.

References

• arXiv: https://arxiv.org/abs/2607.19653