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    Towards principled knowledge editing methods for large language model reasoning

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    Towards principled knowledge editing methods for large language model reasoning

    Abstract

    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.

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    Fig. 1: Transition from static knowledge repositories to dynamic knowledge connections.
    Fig. 2: Knowledge is an entangled, belief-dependent and context-sensitive system, and knowledge editing serves as both a mechanism for continuous updates and a foundation for controllable AI.
    Fig. 3: An illustrative case in which the headquarters of Tailscale Inc. is edited from San Francisco to Toronto with MEMIT, and the edited Qwen-2.5-7B model is then prompted to write a paragraph about the company.
    Fig. 4: Editing entangled knowledge.
    Fig. 5: Framework of confidence-guided KE when the LLM responds under different belief states.
    Fig. 6: Reasoning-augmented belief revision for KE.
    Fig. 7: Framework for expressive and contextualized KE.

    Subjects

    • Language and linguistics
    • Computer science

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    Acknowledgements

    N.Z. and H.C. are funded by the New Generation Artificial Intelligence-National Science and Technology Major Project (grant no. 2025ZD0122801), the National Natural Science Foundation of China (grant no. 62576307), the Fundamental Research Funds for the Central Universities (226-2023-00138), the Yongjiang Talent Introduction Programme (2021A-156-G) and the Information Technology Center and State Key Lab of CAD& CG, Zhejiang University. We thank N. Peng and H. Ji for helpful discussions.

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    Contributions

    Y.Y., J.Q., and Z.Y. contributed to the conceptualization of the paper. N.Z., Y.Y., J.Q., H.X. and Y.Z. prepared the initial draft. Major revisions to the initial draft were contributed by Y.Y., H.X. and Y.Z., with additional feedback provided by S.D., M.W., J.-C.G. and H.C. Y.Y. and Y.T. contributed to the experimental studies and analyses. Y.Y. and J.Q. developed Fig. 1. Y.Y. and Y.Z. developed Figs. 2 and 4. J.Q. developed Figs. 5 and 6. H.X. developed Fig. 7. All authors participated in discussions, contributed to edits and approved the final version of the paper.

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    Zhang, N., Yao, Y., Qin, J. et al. Towards principled knowledge editing methods for large language model reasoning.
    Nat Mach Intell (2026). https://doi.org/10.1038/s42256-026-01276-y

    • Version of record:14 August 2026

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      :https://doi.org/10.1038/s42256-026-01276-y

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