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PerfAgent boosts LLM code optimization to expert levels

By PulseAugur Editorial · [1 sources] ·

Researchers have developed PerfAgent, a novel system designed to enhance the code optimization capabilities of large language model (LLM) agents. Unlike previous agents that focused on correctness, PerfAgent uses profiler-guided iterative refinement to identify and address performance bottlenecks. This approach allows agents to achieve speedups comparable to human experts, even when dealing with complex codebases that include abstraction layers and native extensions. In evaluations on the GSO and SWE-fficiency-Lite benchmarks, PerfAgent significantly improved the rate of expert-matching patches, more than doubling it compared to existing methods. AI

IMPACT Enhances LLM agent capabilities in code optimization, potentially leading to more efficient software development.

RANK_REASON The cluster contains a research paper detailing a new method for code optimization using LLM agents. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

PerfAgent boosts LLM code optimization to expert levels

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The cluster contains a research paper detailing a new method for code optimization using LLM agents. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Ryan Deng, Yuanzhe Liu, Bastian Lipka, Yao Ma, Xuhao Chen, Tim Kaler, Jatin Ganhotra ·

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

    arXiv:2607.19653v1 Announce Type: cross Abstract: 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-lev…

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