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Transferring frozen memory between models can yield substantial performance gains, but only if the target model's reader is properly aligned.
Current LLMs falter in delivering reliable medical assistance, exposing a critical gap in their ability to coordinate knowledge, communication, and EHR interactions.
No single model can reliably assess translation quality across all languages, highlighting the need for a nuanced, direction-aware approach to multilingual data evaluation.
