KGHEdit: Knowledge-Graph-Guided Continual Model Editing via Heat Diffusion

Large language models are increasingly deployed in domains with continuously changing knowledge, making post-pretraining updates necessary without repeated full-model retraining or loss of broad model capabilities. Model editing offers a practical path for such deployment-time updates because it can modify parametric knowledge without retraining the full model. However, existing editors remain poorly matched to continual domain updating: they typically treat each update as an independent fact rewrite, even though knowledge is structured and interconnected, and correlated edits can interact through shared entities and relations. We propose **KGHEdit**, a knowledge-graph-guided framework for continual knowledge editing that models incoming edits as local heat sources and uses heat diffusion to identify and weight structurally related support for a base editor. Together, matched controls, interaction-focused streams, and a 10K-edit audit provide convergent evidence that graph-guided support selection and conditional activation offer a practical path to more reliable continual knowledge updating.