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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.
LLMs struggle to balance rule compliance and effective communication in Taboo, revealing a significant gap in their guessing abilities compared to humans.
