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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.
Current LLMs struggle with state preservation and cultural nuance, achieving only 49.2% success in a new benchmark designed for real-world multilingual tasks.
