Moir: Let the Model Direct Its Own Story for Robust Cross-Domain Knowledge Editing
While language models remain frozen at their training state, the world evolves continuously. Knowledge editing has emerged as a key alternative to full retraining, but its deployment is bottlenecked by the erosion of core capabilities: mathematical and programmatic reasoning collapse while encyclopedic recall remains intact. We trace this asymmetric degradation to a distributional mismatch. Covariance-based editors preserve only the subspaces spanned by their reference corpus, but fail to capture the operative distribution shaped by post-training such as SFT and DPO. Static external corpora, including Wikipedia and even the original pretraining mixture, cannot recover this shifted manifol...
| Authors | Jea Kwon, Jiwon Kim, Dong-kyum Kim et al. |
|---|---|
| Primary category | cs.CL |
| Published | 10 May 2026 |
| Last updated at source | 10 May 2026 |
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