AI RESEARCH
Scalable Knowledge Editing for Mixture-of-Experts LLMs via Tensor-Structured Updates
arXiv CS.LG
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ArXi:2605.16686v1 Announce Type: new Knowledge editing (KE) provides a lightweight alternative to repeated fine-tuning of LLMs. However, most existing KE methods target dense feed-forward layers, while modern LLMs increasingly adopt Mixture-of-Experts (MoE) architectures for their superior memory footprint and inference efficiency. This mismatch leaves a growing class of production models without principled editing tools. We propose a MEMIT-like framework for knowledge editing in MoE-based LLMs.