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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.
Language models can struggle to mimic the nuanced variations in student writing proficiency, but reinforcement learning with a tailored reward can bridge this gap.
