SpecXMaster Technical Report

arXiv cs.LG / 3/25/2026

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Key Points

SpecXMaster is introduced as an end-to-end intelligent spectroscopy framework for NMR molecular spectral interpretation that takes raw FID data and outputs chemical structure-related interpretation, including multiplicity for both 1H and 13C spectra.
The approach addresses limitations of expert-dependent spectral interpretation, such as human bias, limited specialized expertise, and inter-interpreter variability by using agentic reinforcement learning rather than manual or fixed-rule workflows.
The authors report improved performance on multiple public NMR interpretation benchmarks and state the system was refined through iterative evaluations involving professional chemical spectroscopists.
The work positions SpecXMaster as a new methodological paradigm expected to significantly impact organic chemistry workflows within AI-driven closed-loop scientific discovery.

Abstract

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.