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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 reveal a modular cognitive architecture strikingly similar to that of the human brain, challenging our understanding of intelligence across different systems.
