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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.
Multilingual LLMs can be made significantly more reliable by directly optimizing for crosslingual consistency using a DPO-inspired method that requires no explicit reward model.
