# SpectrumWorld: Artificial Intelligence Foundation for Spectroscopy

arXiv: https://arxiv.org/abs/2508.01188 · HTML: https://mlsanity.com/paper/2508.01188/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 88 references that are in the map.
- First posted on arXiv 2 Aug 2025.

## Builds on (most cited first, 17 of 17)

- [Qwen2.5-VL Technical Report](https://mlsanity.com/paper/2502.13923/) (2025)
- [GPT-4o System Card](https://mlsanity.com/paper/2410.21276/) (2024)
- [InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models](https://mlsanity.com/paper/2504.10479/) (2025)
- [Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling](https://mlsanity.com/paper/2412.05271/) (2024)
- [DeepSeek-VL2: Mixture-of-Experts Vision-Language Models for Advanced Multimodal Understanding](https://mlsanity.com/paper/2412.10302/) (2024)
- [GLM-4.5V and GLM-4.1V-Thinking: Towards Versatile Multimodal Reasoning with Scalable Reinforcement Learning](https://mlsanity.com/paper/2507.01006/) (2025)
- [MassSpecGym: A benchmark for the discovery and identification of molecules](https://mlsanity.com/paper/2410.23326/) (2024)
- [Unraveling Molecular Structure: A Multimodal Spectroscopic Dataset for Chemistry](https://mlsanity.com/paper/2407.17492/) (2024)
- [Machine Learning Molecular Dynamics for the Simulation of Infrared Spectra](https://mlsanity.com/paper/1705.05907/) (2017)
- [Deep Convolutional Neural Networks for Raman Spectrum Recognition: A Unified Solution](https://mlsanity.com/paper/1708.09022/) (2017)
- [Accurate and efficient structure elucidation from routine one-dimensional NMR spectra using multitask machine learning](https://mlsanity.com/paper/2408.08284/) (2024)
- [ChemMLLM: Chemical Multimodal Large Language Model](https://mlsanity.com/paper/2505.16326/) (2025)
- [MolSpectra: Pre-training 3D Molecular Representation with Multi-modal Energy Spectra](https://mlsanity.com/paper/2502.16284/) (2025)
- [Artificial Intelligence in Spectroscopy: Advancing Chemistry from Prediction to Generation and Beyond](https://mlsanity.com/paper/2502.09897/) (2025)
- [DiffSpectra: Molecular Structure Elucidation from Spectra using Diffusion Models](https://mlsanity.com/paper/2507.06853/) (2025)
- [NovoBench: Benchmarking Deep Learning-based De Novo Peptide Sequencing Methods in Proteomics](https://mlsanity.com/paper/2406.11906/) (2024)
- [Strategic priorities for transformative progress in advancing biology with proteomics and artificial intelligence](https://mlsanity.com/paper/2502.15867/) (2025)

## Built on by (most cited first, 2 of 2)

- [RamanBench: A Large-Scale Benchmark for Machine Learning on Raman Spectroscopy](https://mlsanity.com/paper/2605.02003/) (2026)
- [MolP2SBench: Do LLMs Really Reason from Molecular Properties to Structure?](https://mlsanity.com/paper/2610.12522/) (2026)

## Abstract (arXiv)

Deep learning holds immense promise for spectroscopy, yet research and evaluation in this emerging field often lack standardized formulations. To address this issue, we introduce SpectrumLab, a pioneering unified platform designed to systematize and accelerate deep learning research in spectroscopy. SpectrumLab integrates three core components: a comprehensive Python library featuring essential data processing and evaluation tools, along with leaderboards; an innovative SpectrumAnnotator module that generates high-quality benchmarks from limited seed data; and SpectrumBench, a multi-layered benchmark suite covering 14 spectroscopic tasks and over 10 spectrum types, featuring spectra curated from over 1.2 million distinct chemical substances. Thorough empirical studies on SpectrumBench with 18 cutting-edge multimodal LLMs reveal critical limitations of current approaches. We hope SpectrumLab will serve as a crucial foundation for future advancements in deep learning-driven spectroscopy.