arXiv Machine Learning July 22, 2026 By Xin Ma, Wei Chen, Qi Liu, Derong Xu, Zhi Zheng, Tong Xu, Enhong Chen

More Edits, More Stable: Understanding the Lifelong Normalization in Sequential Model Editing

Read the original on arXiv Machine Learning →

arXiv:2605. 11836v2 Announce Type: replace Abstract: Lifelong Model Editing aims to continuously update evolving facts in Large Language Models while preserving unrelated knowledge and general capabilities, yet it remains plagued by catastrophic forgetting and model collapse.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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Large language models (LLMs) have demonstrated strong capabilities in knowledge acquisition and reasoning, yet their ability to retain previously acquired knowledge under repeated updates remains insufficiently understood. Existing evaluation paradigms primarily focus on single-step reasoning or static knowledge editing, which fail to capture the temporal dynamics of knowledge retention and degradation during continual model modification.

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LOKI: Memory-Free Null-Space Constrained Lifelong Knowledge Editing

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