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Findings show that foundation models require CGM-specific adaptation for reliable forecasting and that dietary context provides clinically meaningful signals beyond CGM alone, especially during postprandial periods.
Turing-20B-A2B achieves exceptional long-context capabilities with only a fraction of its parameters activated, pushing the boundaries of efficiency in physical AI applications.
