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Selective spectral reversal can effectively undo harmful edits in language models while keeping beneficial changes intact, challenging the notion that all edits must be globally removed.
E2E-CDiff achieves a groundbreaking balance between controllability and realism in traffic scenario generation, crucial for autonomous vehicle testing.
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.
