# TokaMark: A Comprehensive Benchmark for MAST Tokamak Plasma Models

arXiv: https://arxiv.org/abs/2602.10132 · HTML: https://mlsanity.com/paper/2602.10132/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 51 references that are in the map.
- First posted on arXiv 5 Feb 2026.

## Builds on (most cited first, 10 of 10)

- [Hybrid deep learning architecture for general disruption prediction across tokamaks](https://mlsanity.com/paper/2007.01401/) (2020)
- [Full Shot Predictions for the DIII-D Tokamak via Deep Recurrent Networks](https://mlsanity.com/paper/2404.12416/) (2024)
- [Deep Learning for Plasma Tomography and Disruption Prediction from Bolometer Data](https://mlsanity.com/paper/1910.13257/) (2019)
- [Scenario adaptive disruption prediction study for next generation burning-plasma tokamaks](https://mlsanity.com/paper/2109.08956/) (2021)
- [Neural net modeling of equilibria in NSTX-U](https://mlsanity.com/paper/2202.13915/) (2022)
- [Learning Plasma Dynamics and Robust Rampdown Trajectories with Predict-First Experiments at TCV](https://mlsanity.com/paper/2502.12327/) (2025)
- [A machine-learning-based tool for last closed-flux surface reconstruction on tokamaks](https://mlsanity.com/paper/2207.05695/) (2022)
- [Active Disruption Avoidance and Trajectory Design for Tokamak Ramp-downs with Neural Differential Equations and Reinforcement Learning](https://mlsanity.com/paper/2402.09387/) (2024)
- [Experiment data-driven modeling of tokamak discharge in EAST](https://mlsanity.com/paper/2007.10552/) (2020)
- [FusionMAE: large-scale pretrained model to optimize and simplify diagnostic and control of fusion plasma](https://mlsanity.com/paper/2509.12945/) (2025)

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

- [TokaMind: A Multi-Modal Transformer Foundation Model for Tokamak Plasma Dynamics](https://mlsanity.com/paper/2602.15084/) (2026)
- [Benchmarking Sensor Robustness in Plasma Diagnostic Models: A Systematic Evaluation on TokaMark](https://mlsanity.com/paper/2607.11915/) (2026)
- [FusionMMT: A Unified Multimodal and Multitask Learning Framework for Nuclear Fusion](https://mlsanity.com/paper/2609.26095/) (2026)

## Abstract (arXiv)

Development and operation of commercially viable fusion energy reactors such as tokamaks require accurate predictions of plasma dynamics from sparse, noisy, and incomplete sensors readings. The complexity of the underlying physics and the heterogeneity of experimental data pose formidable challenges for conventional numerical methods, and highlight the promise of modern data-native approaches. A major obstacle in realizing this potential is, however, the lack of curated, openly available datasets and standardized benchmarks. Existing fusion datasets are scarce, fragmented across institutions, facility-specific, and inconsistently annotated, which limits reproducibility and prevents a fair and scalable comparison of AI approaches. In this paper, we introduce TokaMark, a structured benchmark to evaluate AI models on real experimental data collected from the Mega Ampere Spherical Tokamak (MAST). TokaMark provides a comprehensive suite of tools designed to unify access to multi-modal fusion data and standardize evaluation protocols. The benchmark includes a curated list of 14 tasks spanning a range of physical mechanisms, exploiting a variety of diagnostics and covering multiple operational use cases. A baseline model is provided to facilitate transparent comparison and validation within a unified framework. By establishing a unified benchmark, TokaMark aims to accelerate progress in data-driven AI-based plasma modeling, contributing to the broader goal of achieving sustainable and stable fusion energy. The dataset, benchmark, documentation, and tooling are open-sourced under https://github.com/UKAEA-IBM-STFC-Fusion-FMs/tokamark_baseline.