New TokaMark benchmark shows plasma diagnostics fail under late sensor corruption
Neerav Gupta · hf · 2026-07-21
A new paper benchmarks the robustness of plasma diagnostic ML models on TokaMark, a dataset of 11,573 MAST shots.
What it evaluates
- Models: XGBoost, LSTM, Transformer, and TokaMark CNN baseline
- Failure scenarios: six physically grounded sensor-failure cases
- Imputation methods: three strategies
- New metric: Robustness Score (RS) for cross-architecture comparison
Main findings
- Disruption-proximate sensor failure severely hurts sequence models: LSTM +212% NRMSE, while XGBoost +37% remains more stable.
- Forward-fill nearly removes degradation from random dropout for sequence models, but does little when the final window is corrupted.
- Under proximate failure, LSTM alarm detection collapses to TPR=0.00, while mean-fill recovers it to TPR=1.00.
- Removing plasma current hurts all models the most, causing +73% to +140% degradation.
Code, data, and checkpoints are available on GitHub.
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