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English(EN) CP-Agent: A Calibrated Risk-Controlled Agent for Feedback-Driven Competitive Programming

CP-Agent 通过反馈提升 LLM 在竞争性编程中的成功率

研究人员开发了 CP-Agent,一个旨在提高大型语言模型在竞争性编程任务中性能的新系统。该代理利用校准停止过程模型来有效整合执行反馈,重点在于减少错误接纳并增加对错误程序的证据。通过实施双粒度验证和测试增强等机制,CP-Agent 在 LiveCodeBench Pro 和 ICPC-Eval 等基准测试中显著提高了成功率,而无需更新模型参数。 AI

影响 增强了 LLM 在复杂问题解决中的能力,有可能提高代理在专业领域的性能。

排序理由 这是一篇详细介绍一种改进 LLM 在特定任务上性能的新方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CL 阅读 →

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

CP-Agent 通过反馈提升 LLM 在竞争性编程中的成功率

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这是一篇详细介绍一种改进 LLM 在特定任务上性能的新方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.CL TIER_1 English(EN) · Peisong Wang, Bowen Liu, Zehua Li, Yuyao Wang, Zhiwei Ma, Yuhan Li, Jia Li ·

    CP-Agent:一种用于反馈驱动的竞赛编程的校准风险控制代理

    arXiv:2605.24693v1 Announce Type: new Abstract: Large language models still struggle with contest-level programming, while many agentic remedies rely on massive inference-time sampling or expensive multi-stage post-training. We study when execution feedback reliably helps an LLM …