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Towards principled knowledge editing methods for large language model reasoning
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AI
DOI：10.1038/s42256-026-01276-y.png)
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Abstract
En 中文
Knowledge editing has emerged as a promising approach that leverages understanding of a model’s inner knowledge mechanisms to enable precise knowledge updates and behaviour control without costly retraining. It is particularly appealing for enabling continuous knowledge adaptation, a capability essential for building truly intelligent, self-evolving AI systems. However, current methods treat large language models as modular knowledge stores where facts can be edited independently, ignoring the fact that knowledge forms an interconnected system in which elements depend on each other. As large language models increasingly exhibit sophisticated reasoning abilities, such as spanning multistep deduction and causal inference, the need for reasoning-consistent knowledge updates becomes critical. In this Perspective we explore some limitations of existing knowledge editing techniques and argue that effective knowledge editing must account for the intricate nature of knowledge representation. We outline three promising research directions: (1) addressing knowledge interdependence through deductive closure circuit editing; (2) integrating model beliefs and confidence into the editing process; and (3) enabling contextualized updates for complex, interdependent knowledge forms. Together, these directions suggest a pathway towards more principled knowledge editing methods capable of supporting the next generation of adaptive, reasoning-driven AI systems. Chen et al. explore limitations of current knowledge editing techniques in large language models and propose three promising research directions that respect the complexity of knowledge representation in a real-world setting.
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