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Lexical and structural hashing methods can match near-duplicate documents effectively, but semantic-sensitive approaches excel in preserving similarity under content rewriting, albeit at a higher computational cost.
Adaptive weighting in model merging can drastically improve multilingual reasoning performance, outperforming traditional methods across 21 languages.
