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English(EN) COMPAS: Difficulty-Aware Joint Search for Optimizing Code Generation

COMPAS方法通过联合调整模型、提示和设置来优化代码生成

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种新颖的COMPAS方法，通过联合搜索模型、提示和解码设置来优化代码生成。这种难度感知方法学习特定组的质量-成本前沿，使其能够在不进行额外搜索的情况下将测试任务路由到最高效的配置。COMPAS在LiveCodeBench和SWE-bench等基准测试中显著提高了性能，提高了pass@1率和任务解决率，同时大幅降低了成本。 AI

影响 该方法可能带来更高效、更具成本效益的AI代码生成工具。

排序理由 该集群包含一篇详细介绍代码生成优化新方法的论文。[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 →

COMPAS方法通过联合调整模型、提示和设置来优化代码生成

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该集群包含一篇详细介绍代码生成优化新方法的论文。[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) · Jingzhi Gong, Jie M. Zhang, Gunel Jahangirova, Dong Huang, Mohammad Reza Mousavi, Mark Harman ·

    COMPAS：一种面向代码生成的优化难度感知联合搜索方法

    arXiv:2608.04336v1 Announce Type: cross Abstract: Code generation systems make each LLM call with a model, a prompt, and decoding settings. However, existing optimization methods usually tune only part of these choices or use one fixed configuration for all tasks: global optimize…

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