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LOKI: Memory-Free Null-Space Constrained Lifelong Knowledge Editing
arXiv CS.AI
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이 매체는 공공·자유 라이선스로 본문을 직접 표시합니다.Computer Science > Machine Learning
[Submitted on 18 Jun 2026]
Title:LOKI: Memory-Free Null-Space Constrained Lifelong Knowledge Editing
View PDF HTML (experimental)Abstract:Lifelong knowledge editing aims to efficiently and sequentially update language models over time, as new knowledge becomes available or when the model makes mistakes, while preserving acceptable performance on past knowledge. One unresolved challenge is that existing methods modify a fixed set of layers for all new knowledge samples, reducing flexibility and increasing catastrophic forgetting. Another is requiring access to previous knowledge and extensive pre-processing to obtain data statistics. To address these challenges, we introduce LOKI, a novel approach that uses dynamic layer selection based on the Hilbert-Schmidt Independence Criterion and projects gradient updates onto the null-space of the model weights, bypassing the requirement for previous knowledge access. We show that LOKI achieves superior performance to existing approaches across a wide variety of experiments, achieving up to a 14\% improvement in average accuracy.
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