Emerging research in suggests that models can learn to represent data in latent spaces far smaller than traditional codecs. By training on domain‑specific datasets (e.g., MRI scans), a model could automatically generate a minimal representation that preserves medically relevant features while discarding noise. This semantic repacking moves beyond blind bit‑level compression toward meaning‑aware reduction .
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