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University of Oklahoma
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Pretrained time-series foundation models offer surprisingly inconsistent benefits for EMG tasks, with full fine-tuning often outperforming linear probing and few-shot adaptation only partially recovering performance on new users.
Severity scores are meaningless without spatial grounding: gating RL rewards on strictly ordered localization and classification dependencies forces MLLMs to diagnose visual degradation rather than hallucinate holistic quality metrics.
