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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.
No single model dominates video embedding tasks, revealing stark contrasts in performance based on modality and task type.
Audio models can't do it all: a comprehensive benchmark reveals stark performance trade-offs between acoustic and linguistic understanding, with no single model dominating across diverse audio tasks.
