用技能与代码引导的智能体模型，更像化学家解分子结构
S3C-LLM: Skill-Code Guided Agentic Language Models for Spectrum-to-Structure Elucidation
- 分步调用专业技能和分析代码，逐步推理光谱数据
- 在多个数据集上超越现有模型，仅用1/10训练数据
- 适合需要可解释性、流程化推理的分子结构解析场景
光谱结构解析是分子分析的核心，但现有基于大语言模型的方法多将问题简化为直接从光谱生成SMILES，未显式建模化学家的实际分析流程，如诊断峰解读、片段推理、分子式约束和化学一致性检查。本文提出S3C-LLM，一种技能引导且代码驱动的智能体语言模型，用于光谱到结构的解析。它不直接预测分子，而是检索特定模态的光谱技能，执行分析代码以在输入光谱上实例化这些技能，整合峰级证据与分子式约束后生成SMILES。我们贡献了自演化的光谱技能库、增强思维的技能-代码轨迹构建流程，以及两阶段训练策略：先通过监督微调（SFT）训练Qwen3-4B，再采用提出的步骤级强化学习（RL）。在多种基准上的实验表明，S3C-LLM在各类光谱上持续优于当前通用大模型与专用模型，同时使用的训练语料不足SpectraLLM的1/10。
原文摘要 · Abstract (English)
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 spectral data, it does not explicitly model the analytical workflow used by spectroscopists, such as diagnostic peak interpretation, fragment reasoning, formula constraints, and chemical consistency checking. In this paper, we introduce S3C-LLM, a skill-guided and code-grounded agentic LLM for spectrum-to-structure elucidation. Rather than directly predicting a molecule, S3C-LLM retrieves modality-specific spectroscopy skills, executes analysis code to instantiate these skills on the input spectra, and integrates the resulting peak-level evidence and formula constraints before generating SMILES. Specifically, we contribute a self-evolving spectroscopy skill library, a thinking-augmented skill-code trajectory construction pipeline, and a two-stage training strategy that teaches Qwen3-4B through supervised fine-tuning (SFT) followed by our proposed step-level reinforcement learning (RL). Experiments on diverse benchmarks show that S3C-LLM consistently outperforms current general LLMs and spectrum-specific models across spectra, while using less than 1/10th of SpectraLLM's training corpus.
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