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SOTER is presented, a generative foundation model for wearable physiological time series that unifies cross-channel coupling, spectrum-guided expert specialization, and continuous-time latent evolution within a single pre-training framework and achieves the best RMSE on 4 of 6 datasets and the best MAE on 5 of 6.
MLLMs that excel at medical image diagnosis may be relying on shortcuts, undermining their trustworthiness for clinical deployment.