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English(EN) Coupled Harness--Model Evolution for Scientific Equation Discovery

大型语言模型演化科学方程发现的工具和策略

研究人员开发了一个使用大型语言模型(LLMs)进行科学方程发现的新框架。该系统将计算工具的演化与LLM的工具使用策略相结合,使两者都能从相同的错误信号中得到改进。工具演化阶段使代理能够通过诊断和纠正自身的失败来扩展其可执行工具库,而策略演化阶段则在成功的工具使用轨迹上对LLM进行微调。这种集成方法显著提高了模型解决复杂科学问题的能力,在各种基准测试中取得了高精度。 AI

影响 该框架通过使LLM能够更有效地发现和利用计算工具,有望加速科学发现。

排序理由 该条目是一篇学术论文,详细介绍了使用LLM进行科学发现的新框架。[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 →

大型语言模型演化科学方程发现的工具和策略

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该条目是一篇学术论文,详细介绍了使用LLM进行科学发现的新框架。[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) · Pengze Li, Jiaquan Zhang, Encheng Su, Yunbo Long, Zihang Zeng, Xinping Liu, Han Zhang, Junchi Yu, Shixiang Tang, Aoran Wang, Xi Chen ·

    耦合式模型演化用于科学方程发现

    arXiv:2610.17740v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used to discover scientific equations from experimental data. Such a discovery system has two parts that must work together: a library of executable computational tools and the model's t…