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English(EN) S3C-LLM: Skill-Code Guided Agentic Language Models for Spectrum-to-Structure Elucidation

新型代理LLM S3C-LLM增强分子结构阐明

研究人员开发了S3C-LLM,这是一种新颖的代理语言模型,用于分子分析中的光谱到结构阐明。与直接将光谱转换为SMILES的先前方法不同,S3C-LLM通过检索特定技能、执行分析代码以及整合证据来生成分子结构,从而模仿光谱学家的分析过程。该方法使用Qwen3-4B模型的监督微调和强化学习策略进行训练,与现有的通用和光谱特定模型相比,表现出卓越的性能。 AI

影响 该模型可以提高化学和药物发现中分子分析的准确性和效率。

排序理由 该集群描述了一篇详细介绍用于特定科学任务的新型模型的最新研究论文。[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 →

新型代理LLM S3C-LLM增强分子结构阐明

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该集群描述了一篇详细介绍用于特定科学任务的新型模型的最新研究论文。[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) · Xuanle Zhao, Xinyuan Cai, Xiang Cheng, Bo Xu ·

    S3C-LLM:用于频谱到结构阐释的技能代码引导式智能语言模型

    arXiv:2608.30910v1 Announce Type: cross Abstract: Spectroscopic structure elucidation is central to molecular analysis, but recent Large Language Model (LLM)-based methods mostly formulate it as direct spectrum-to-SMILES generation. Although this paradigm can leverage paired spec…