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.

翻译：托卡马克等商业可行聚变能源反应堆的开发与运行，需要从稀疏、含噪且不完整的传感器读数中准确预测等离子体动力学。基础物理的复杂性以及实验数据的异构性对传统数值方法构成了严峻挑战，同时凸显了现代数据驱动方法的潜力。然而，实现这一潜力的主要障碍在于缺乏经过整理的公开可用数据集和标准化基准。现有聚变数据集稀缺、分散于不同机构、针对特定装置且标注不一致，这限制了研究的可重复性，并阻碍了对人工智能方法进行公平且可扩展的比较。本文介绍了TokaMark，这是一个结构化基准，用于在兆安球形托卡马克（MAST）收集的真实实验数据上评估人工智能模型。TokaMark提供了一套全面的工具，旨在统一多模态聚变数据的访问并标准化评估协议。该基准包含14个精选任务，涵盖一系列物理机制，利用多种诊断手段，并覆盖多个运行用例。我们提供了一个基线模型，以便在统一框架内进行透明的比较和验证。通过建立统一的基准，TokaMark旨在加速数据驱动的人工智能等离子体建模的进展，从而为可持续和稳定的聚变能源这一更广泛目标做出贡献。数据集、基准、文档和工具已在https://github.com/UKAEA-IBM-STFC-Fusion-FMs/tokamark_baseline开源。