Qwen3微调模型在化学机理推理上直接击败了专用FlowER，准确率从5.1%提升到8.3%，化学和AI交叉领域值得一看。
研究团队构建了大规模反应机理推理数据集，并创建了FukuyamaBench基准，该基准源自Fukuyama的《高级有机反应机理》一书，用于评估模型的分层机理推理能力。微调后的Qwen3-30B-A3B模型在FukuyamaBench Set A上达到8.3%的精确路径匹配率，超过专用FlowER模型的5.1%。结果表明机理感知训练可显著增强语言模型的化学推理能力。
Learning Mechanistic Reasoning for Chemical Reactions with Large Language Models
Reaction mechanisms consist of the step-by-step sequences of elementary reactions that explain chemical transformations. Learning the mechanism logic is therefore essential for enhancing the fundamental chemical intelligence of large language models (LLMs). The stepwise deduction of reaction mechanism aligns naturally with the reasoning paradigms of reasoning LLMs. However, current chemical LLMs primarily emphasize coarse-grained name reactions for product prediction and retrosynthesis, often leading to physical inconsistencies and hallucinations. In contrast, specialized small-scale generative models for mechanism inference typically suffer from restricted generalization capacity across diverse chemical spaces. To overcome these limitations, we built a novel, large-scale reasoning dataset of reaction mechanisms. Furthermore, we established the FukuyamaBench, a difficult benchmark derived from Fukuyama's Advanced Organic Reaction Mechanism book, to rigorously evaluate model performance on hierarchical mechanism reasoning. Our fine-tuned Qwen3-30B-A3B achieves 8.3% exact pathway match on FukuyamaBench Set~A, surpassing the specialized FlowER model (5.1%), demonstrating that mechanism-aware training substantially enhances chemical reasoning in language models.