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MARS框架使用专业化大语言模型进行竞争性编程

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了MARS，一个用于竞争性编程的新颖框架，它利用了专业化大语言模型（LLMs）的多代理中继系统。与之前使用通用角色的流水线不同，MARS分配了诸如动态规划或图论专家之类的特定主题专家。这些专家通过算法理论语料库的检索增强生成得到增强。该系统为每个问题动态选择一个相关专家的团队，由一个启动者生成初始的C++17解决方案。后续回合包括在沙箱中测试代码，允许活动专家改进或传递草稿，并将结构化数据包转发给下一个专家，最终通过最终传递规范化样板代码。 AI

影响 这种专业化的大语言模型中继系统可以显著提高AI在复杂编码任务和竞争性编程中的性能。

排序理由 该集群包含一篇学术论文，详细介绍了使用大语言模型进行竞争性编程的新系统。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.MA (Multiagent) 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

MARS框架使用专业化大语言模型进行竞争性编程

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该集群包含一篇学术论文，详细介绍了使用大语言模型进行竞争性编程的新系统。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [2]

  1. arXiv cs.AI TIER_1 English(EN) · Andrei Mikhailov, Mikhail Burtsev, Alsu Sagirova ·

    MARS：面向竞技编程的多专家大型语言模型中继系统

    arXiv:2608.23918v1 Announce Type: new Abstract: Large Language Models excel at code generation, yet competitive programming exposes a persistent failure mode: existing multi-agent pipelines distribute work over generic planner, coder, and debugger roles and delegate the choice of…

  2. arXiv cs.MA (Multiagent) TIER_1 English(EN) · Alsu Sagirova ·

    MARS：面向竞赛编程的多专家大语言模型中继系统

    Large Language Models excel at code generation, yet competitive programming exposes a persistent failure mode: existing multi-agent pipelines distribute work over generic planner, coder, and debugger roles and delegate the choice of algorithmic technique to the backbone alone. We…

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