Fast and Memory-Efficient Lifelong Model Editing with Certified Evaluation Reuse

Model editing writes new knowledge into a frozen large language model without retraining. In lifelong editing, edits arrive continually and must be re-verified against the accumulated history. A single edit in a memory-based editor changes one active value row, yet the standard execution path recomputes edit-invariant prefixes and retrieval at every optimization step, allocates training state for the whole table, and re-tests the full history at every checkpoint. We propose eHoReN, which removes this mismatch in editors that retrieve before they inject and write one row, with a single principle: the execution scope of every computation and every piece of storage should equal its dependency scope on mutable state. Each technique carries an equivalence proposition fixing when the original behavior is preserved. On the editor that holds the quality frontier at scale, eHoReN edits up to 3.5x faster, with outputs identical to the baseline on Llama-3.1-8B and quality within seed variability on Qwen2.5-7B. Its training state shrinks from 4.58 GiB to 48 KiB and stops growing with the memory. The same dependency records make reuse of historical evaluation results checkable, and a cost model predicts when checking pays off. Certified reuse speeds up evaluation by 1.89x per checkpoint without lag, and periodic re-certification reaches 1.6-2.4x over exact checking with exact agreement at every anchor. Transferred to a second editor with different retrieval and injection mechanisms, the editing speedup reproduces and the evaluation gain follows the benefit condition's prediction. Code is available at: https://anonymous.4open.science/r/eHoReN/