Papers with REVIVE

    1 papers
    Spectral Characterization and Mitigation of Sequential Knowledge Editing Collapse (2026.acl-long)

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    Challenge: Existing approaches to reducing the effects of knowledge editing are insufficiently understood.
    Approach: They propose a plug-and-play framework that preserves the dominant subspace of the original weights and analyzes parameter updates in the spectral basis of the weights.
    Outcome: The proposed framework improves editing efficacy while preserving general abilities under long-horizon sequential editing, including extreme settings with up to 20,000 edits.

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