Deployed models decay over time due to shifting inputs, changing user needs, or emergent knowledge gaps. When harmful behaviors are identified, targeted edits are required. However, current model editors, which adjust specific behaviors of pre-trained models, degrade model performance over multiple edits. We propose GRACE, a Lifelong Model Editing method, which implements spot-fixes on streaming errors of a deployed model, ensuring minimal impact on unrelated inputs. GRACE writes new mappings in…