@inproceedings{liu-etal-2026-specedit,
    title = "{S}pec{E}dit: A Spectral Approach for Multi-Round Knowledge Editing",
    author = "Liu, Junxian  and
      Deng, Zikun  and
      Wu, Xin  and
      Deng, Dazhen  and
      Cai, Yi",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.922/",
    pages = "18511--18524",
    ISBN = "979-8-89176-395-1",
    abstract = "Multi-round batch knowledge editing often suffers from performance degradation as edits accumulate. Focusing on the locate-then-edit paradigm, we analyze this phenomenon from a spectral perspective and identify a previously overlooked structural factor: the intrinsic knowledge of the model and historical edit memories exhibit markedly different spectral characteristics and information distributions, yet are naively coupled and jointly inverted during editing. Based on this insight, we propose SpecEdit to improve the model editing from a spectral perspective. SpecEdit performs spectral decoupling to isolate editing-critical directions and reduce destructive coupling, followed by spectral-structure-aware information compensation and spectral fusion to construct a refined closed-form solution. The module integrates seamlessly into existing editing methods without altering their original optimization procedures. Experiments on multiple LLMs and editing methods show that SpecEdit consistently improves performance, demonstrating that modeling spectral structure is an effective, interpretable approach and a promising direction for future research."
}