Intelligent spectroscopy serves as a pivotal element in AI-driven closed-loop scientific discovery, functioning as the critical bridge between matter structure and artificial intelligence. However, conventional expert-dependent spectral interpretation encounters substantial hurdles, including susceptibility to human bias and error, dependence on limited specialized expertise, and variability across interpreters. To address these challenges, we propose SpecXMaster, an intelligent framework leveraging Agentic Reinforcement Learning (RL) for NMR molecular spectral interpretation. SpecXMaster enables automated extraction of multiplicity information from both 1H and 13C spectra directly from raw FID (free induction decay) data. This end-to-end pipeline enables fully automated interpretation of NMR spectra into chemical structures. It demonstrates superior performance across multiple public NMR interpretation benchmarks and has been refined through iterative evaluations by professional chemical spectroscopists. We believe that SpecXMaster, as a novel methodological paradigm for spectral interpretation, will have a profound impact on the organic chemistry community.

翻译：智能光谱学作为人工智能驱动的闭环科学发现中的关键要素，扮演着连接物质结构与人工智能的重要桥梁。然而，传统依赖专家的光谱解读面临重大挑战，包括易受人为偏见和错误影响、依赖有限的专业知识以及不同解读者之间的差异性。为解决这些问题，我们提出了SpecXMaster，一种利用智能体强化学习进行核磁共振分子光谱解读的智能框架。SpecXMaster能够直接从原始自由感应衰减数据中自动提取1H和13C谱的多重性信息。该端到端流程实现了从核磁共振谱到化学结构的全自动解读。它在多个公开的核磁共振解读基准测试中展现出优越性能，并经过专业化学光谱学家的迭代评估优化。我们相信，SpecXMaster作为一种全新的光谱解读方法论范式，将对有机化学领域产生深远影响。