SpectrumWorld: Artificial Intelligence Foundation for Spectroscopy
Zhuo Yang, Jiaqing Xie, Shuaike Shen, Daolang Wang, Yeyun Chen, Ben Gao, Shuzhou Sun, Biqing Qi, Dongzhan Zhou, Lei Bai, Linjiang Chen, Shufei Zhang, Qinying Gu, Jun Jiang, Tianfan Fu, Yuqiang Li
TL;DR
SpectrumWorld is a comprehensive platform that standardizes and advances deep learning research in spectroscopy through tools, benchmarks, and empirical evaluations of state-of-the-art models.
Contribution
It introduces SpectrumLab, a unified platform with data processing, benchmark suite, and annotation tools to accelerate spectroscopy research.
Findings
Current models have critical limitations in spectroscopy tasks.
SpectrumBench covers 14 tasks and 10 spectrum types with extensive data.
Empirical studies reveal gaps and challenges in existing deep learning approaches.
Abstract
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…
Peer Reviews
Decision·Submitted to ICLR 2026
* The benchmark is hierarchically structured to mirror real spectroscopy workflows (signal → perception → semantic → generation) across roughly 14 task types and more than 10 modalities, which makes failure modes and capability gaps observable. * Coverage of 20+ multimodal LLMs under a single evaluation protocol yields stable empirical patterns, strong performance on low-level recognition but weak generation/reasoning. * The curation pipeline combines automated item generation with human-in-the-
* The scope is limited to MLLMs, with no baselines from domain-specific models (e.g., neural network trained on molecular strings and spectra data). Including such baselines and perhaps a hybrid setting where an LLM orchestrates specialized models for reasoning would yield a more informative comparison. * Due to the focus on MLLMs, the task design and metrics are narrow. Reliance on multiple choice and plain accuracy underutilizes domain-specific measures (e.g., spectral similarity, shift MAE).
- Clear infrastructural positioning and broad coverage: The system systematically abstracts spectroscopy tasks into a four-level hierarchy and provides a modular evaluation ecosystem (tasks, evaluators, leaderboards), offering an extensible common platform for the research community. - Reproducible data and annotation workflow: The proposed SpectrumAnnotator automatically constructs multimodal QA/generation samples, and together with SpectrumVerifier and expert review forms a closed-loop qual
- Lack of quantitative evidence for annotation quality: Although the paper describes the quality control process, it lacks systematic statistics on annotation quality (e.g., inter-annotator agreement, error rate, or comparison between automatic and manual annotations). Providing such quantitative analysis or visualization would strengthen the reliability of the benchmark. - Single-dimensional evaluation metrics: For generative tasks, using GPT-4o as the sole evaluator may introduce evaluation
The benchmark library and other Python libraries introduced here seem useful for a specific community.
The manuscript is extremely dense, very hard to read, also due to the introduction of more than 20 abbreviations, and the use of a lot of domain jargon. The relationship between different components (SpectrumBench, SpectumLab) is hard to understand. The modalities that are actually being used are often unclear. Figure 1 mainly talks about images and text, while other parts discuss model inputs and outputs without specifying the modality or data representation (e.g. molecules, spectra, ...).
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