TokaReconBench: A Benchmark for Transferable AI in Tokamak Equilibrium Reconstruction

Developing reliable and transferable AI models for plasma equilibrium reconstruction is essential yet challenging for tokamak fusion reactors. Existing AI-based equilibrium reconstruction methods have shown promising performance as fast surrogates for traditional solvers. However, their predominantly in-distribution evaluation provides limited evidence of reliability under domain and device shifts. To address this limitation, we introduce TokaReconBench, a benchmark for transferable tokamak equilibrium reconstruction. It integrates heterogeneous experimental and synthetic datasets, including public MAST measurements, ITER-like simulations, and our newly contributed, configuration-aligned EXL-50U simulation and experimental data. We define unified task formulations and evaluation protocols covering three levels of generalization: simulation-to-simulation (sim2sim), simulation-to-real (sim2real) transfer, and cross-device generalization. We benchmark representative neural surrogate models, operator learning approaches, and physics-based baselines under diverse training loss settings. Metrics assess field accuracy, geometry, and physics consistency against reference source terms. Experiments reveal significant bottlenecks in cross-domain and cross-device transfer for existing learning-based reconstruction models. We hope TokaReconBench provides a unified platform for developing transferable AI methods towards future multi-device fusion applications. Our benchmark, datasets, and evaluation protocols are available upon acceptance.