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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.
Sentiment models often disagree on Holocaust oral histories, not on the presence of positive or negative sentiment, but on the boundary of neutrality, revealing a critical gap in their ability to handle nuanced historical narratives.