# RamanBench: A Large-Scale Benchmark for Machine Learning on Raman Spectroscopy

arXiv: https://arxiv.org/abs/2605.02003 · HTML: https://mlsanity.com/paper/2605.02003/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 112 references that are in the map.
- First posted on arXiv 3 May 2026.

## Builds on (most cited first, 12 of 12)

- [AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data](https://mlsanity.com/paper/2003.06505/) (2020)
- [The UEA multivariate time series classification archive, 2018](https://mlsanity.com/paper/1811.00075/) (2018)
- [TabICLv2: A better, faster, scalable, and open tabular foundation model](https://mlsanity.com/paper/2602.11139/) (2026)
- [TabDPT: Scaling Tabular Foundation Models on Real Data](https://mlsanity.com/paper/2410.18164/) (2024)
- [Mitra: Mixed Synthetic Priors for Enhancing Tabular Foundation Models](https://mlsanity.com/paper/2510.21204/) (2025)
- [CARTE: Pretraining and Transfer for Tabular Learning](https://mlsanity.com/paper/2402.16785/) (2024)
- [Table Foundation Models: on knowledge pre-training for tabular learning](https://mlsanity.com/paper/2505.14415/) (2025)
- [Tabular Data: Is Deep Learning all you need?](https://mlsanity.com/paper/2402.03970/) (2024)
- [PMLBmini: A Tabular Classification Benchmark Suite for Data-Scarce Applications](https://mlsanity.com/paper/2409.01635/) (2024)
- [SpectrumWorld: Artificial Intelligence Foundation for Spectroscopy](https://mlsanity.com/paper/2508.01188/) (2025)
- [An efficient label-free analyte detection algorithm for time-resolved spectroscopy](https://mlsanity.com/paper/2011.07470/) (2020)
- [Benchmarking Deep Learning Models for Raman Spectroscopy Across Open-Source Datasets](https://mlsanity.com/paper/2601.16107/) (2026)

## Built on by (most cited first, 3 of 3)

- [RamanPFN: learning from Raman spectral structure with a tabular foundation model](https://mlsanity.com/paper/2608.02157/) (2026)
- [Attributing Preprocessing Invariance in Spectral Foundation Models](https://mlsanity.com/paper/2608.14227/) (2026)
- [TabBench-Bio: A Living Benchmark for Machine Learning on High-Dimensional Biomedical Tables](https://mlsanity.com/paper/2609.07441/) (2026)

## Abstract (arXiv)

Machine Learning (ML) has transformed many scientific fields, yet key applications still lack standardized benchmarks. Raman spectroscopy, a widely used technique for non-invasive molecular analysis, is one such field where progress is limited by fragmented datasets, inconsistent evaluation, and models that fail to capture the structure of spectral data. We introduce RamanBench, the first large-scale, fully reproducible benchmark for ML on Raman spectroscopy, consisting of streamlined data access, evaluation protocols and code, as well as a live leaderboard. It unifies 74 datasets (including 16 first released with this benchmark) across four domains, comprising 325,668 spectra and spanning classification and regression tasks under diverse experimental conditions. We benchmark 28 models under a standardized protocol, including classical methods (e.g., PLS), Raman-specific (e.g., RamanNet), Tabular Foundation Model (TFM) (e.g., TabPFN), and time-series approaches (e.g., ROCKET). TFM consistently outperform domain-specific and gradient boosting baselines, while time-series models remain competitive. However, no method generalizes across datasets, revealing a fundamental gap. Therefore, we invite the community to contribute new approaches to our living benchmark, with the potential to accelerate advances in critical applications such as medical diagnostics, biological research, and materials science.