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Analysis indicates that embedding representations of physical measurements are strongly influenced by superficial string similarity, and recalibration of similarity does not substantially improve the alignment.
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.
Modern LLMs can drastically improve OCR accuracy for historical texts, but they risk over-correcting clean inputs, complicating their practical deployment.
