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Nuclear Science and Engineering Division, Argonne National Laboratory
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Millisecond-scale forecasting of reactor thermal-hydraulics, even with missing sensors, is now possible thanks to a physics-informed GNN-ODE digital twin that learns interpretable heat-transfer scaling.
Existing LLM watermarking schemes crumble when text is short, but XMark maintains high decoding accuracy and text quality even with limited tokens.
Heterogeneous federated LLM fine-tuning gets a boost from parallel one-rank adaptation, sidestepping the noise issues that plague existing LoRA-based methods.
