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English(EN) Build or Adopt? Custom-Built vs. General-Purpose LLM Agent Harnesses for Automotive GUI Testing

汽车测试中的定制化与通用大语言模型(LLM)代理

一篇来自arXiv的新论文探讨了开发定制化大语言模型(LLM)代理与采纳通用型代理之间的权衡,并以沃尔沃集团的汽车GUI测试为例进行了案例研究。研究发现,虽然Claude Code和OpenCode等通用型代理产生了更高的推理成本,但它们显著减少了工程投入,从而降低了夜间回归测试套件的总体月度成本。与内部解决方案相比,这些外部工具还在开放式任务上展现出更高的成功率,并能更好地从错误中恢复,尽管它们对模型输入的控制较少。 AI

影响 这项研究为组织在构建定制化大语言模型(LLM)代理解决方案或采纳现有解决方案之间做出决策提供了见解,影响了人工智能应用的开发策略和成本效益。

排序理由 发布在arXiv上的研究论文,讨论大语言模型(LLM)代理。 [lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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汽车测试中的定制化与通用大语言模型(LLM)代理

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发布在arXiv上的研究论文,讨论大语言模型(LLM)代理。 [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) · Arsham Gholamzadeh Khoee, Ruyu Fu, Jiaqi Wei, Ruikun Deng, Robert Feldt, Dhasarathy Parthasarathy, Yinan Yu ·

    自建还是采纳?定制化与通用型大模型代理在汽车GUI测试中的应用

    arXiv:2610.33636v1 Announce Type: cross Abstract: As general-purpose LLM agent harnesses mature, organizations must decide whether to continue developing in-house systems or adopt existing alternatives. We study this decision at Volvo Group for GUI testing of Android-based in-veh…