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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 can flip their answers up to 97.3% of the time when faced with counterarguments, revealing a critical instability that accuracy metrics overlook.
Despite advancements in OCR, current models fail to generalize beyond a small set of scripts, often hallucinating characters or producing noise when faced with unfamiliar writing systems.
