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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.
Dialectal variations can degrade LLM performance by over 6% in some cases, revealing a critical gap in their robustness to real-world language use.
