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English(EN) Code Researcher: Deep Research Agent for Large Systems Code and Commit History

Code Researcher 代理将 Linux 内核崩溃解决率提高了 48%

一种名为 Code Researcher 的新型深度研究代理已被开发出来,通过分析大型代码库及其提交历史来解决复杂的系统代码问题。该代理在 kBenchSyz 等基准测试中的表现显著优于现有方法,使用 GPT-4o 达到了 48% 的崩溃解决率,使用 Gemini 2.5-Flash 甚至达到了更高的解决率。该研究强调了收集广泛的全局上下文和采用多方面推理对于在大型系统中进行有效代码修改的关键作用。 AI

影响 新代理显著提高了代码修复率,可能加速软件开发和维护。

排序理由 详细介绍用于代码分析和补丁生成的新的 AI 代理的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

Code Researcher 代理将 Linux 内核崩溃解决率提高了 48%

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详细介绍用于代码分析和补丁生成的新的 AI 代理的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Ramneet Singh, Sathvik Joel, Abhav Mehrotra, Nalin Wadhwa, Ramakrishna B Bairi, Aditya Kanade, Nagarajan Natarajan ·

    代码研究员:用于大型系统代码和提交历史的深度研究代理

    arXiv:2506.11060v2 Announce Type: replace-cross Abstract: Large Language Model (LLM)-based coding agents have shown promising results on coding benchmarks, but their effectiveness on systems code remains underexplored. Due to the size and complexities of systems code, making chan…