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This work shows that specialist optimization implicitly selects from this latent trajectory space, and establishes a new view of specialist training: when gold reasoning is absent, tuning choices directly control the latent supervision passed to downstream models.
Machine translation benchmarks have functionally saturated, but pairing human-authored failure cases with deterministic verification rules reveals critical multimodal blind spots that automated metrics consistently miss.
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.
