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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.
Small LLMs paired with symbolic solvers can outperform larger zero-shot LLMs on formal reasoning tasks, but still struggle with multilingual inputs.
