[2510.13851] EvoEdit: Evolving Null-space Alignment for Robust and Efficient Knowledge Editing

Computer Science > Computation and Language arXiv:2510.13851 (cs) [Submitted on 11 Oct 2025 (v1), last revised 6 Apr 2026 (this version, v2)] Title:EvoEdit: Evolving Null-space Alignment for Robust and Efficient Knowledge Editing Authors:Sicheng Lyu, Yu Gu, Xinyu Wang, Jerry Huang, Sitao Luan, Yufei Cui, Xiao-Wen Chang, Peng Lu View a PDF of the paper titled EvoEdit: Evolving Null-space Alignment for Robust and Efficient Knowledge Editing, by Sicheng Lyu and 7 other authors View PDF HTML (experimental) Abstract:Large language models (LLMs) require continual updates to rectify outdated or erroneous knowledge. Model editing has emerged as a compelling paradigm for introducing targeted modifications without the computational burden of full retraining. Existing approaches are mainly based on a locate-then-edit framework. However, in sequential editing contexts, where multiple updates are applied over time, they exhibit significant limitations and suffer from catastrophic interference, i.e., new edits compromise previously integrated updates and degrade preserved knowledge. To address these challenges, we introduce EvoEdit, a novel editing strategy that mitigates catastrophic interference through sequential null-space alignment, enabling stable and efficient model editing. By performing sequential null-space alignment for each incoming edit, EvoEdit preserves both original and previously modified knowledge representations and maintains output invariance on preserved knowledge e...