Deploying Large Language Models (LLMs) in real-world dynamic environments raises the challenge of updating their pre-trained knowledge. While existing knowledge editing methods can reliably patch isolated facts, they frequently suffer from a "Reasoning Gap", where the model recalls the edited fact but fails to utilize it in multi-step reasoning chains. To bridge this gap, we introduce MCircKE (\underline{M}echanistic \underline{Circ}uit-based \underline{K}nowledge \underline{E}diting), a novel framework that enables a precise "map-and-adapt" editing procedure. MCircKE first identifies the causal circuits responsible for a specific reasoning task, capturing both the storage of the fact and the routing of its logical consequences. It then surgically update parameters exclusively within this mapped circuit. Extensive experiments on the MQuAKE-3K benchmark demonstrate the effectiveness of the proposed method for multi-hop reasoning in knowledge editing.

翻译：将大型语言模型（LLMs）部署于动态真实环境时，面临更新其预训练知识的挑战。现有知识编辑方法虽能可靠地修补孤立事实，但常出现"推理鸿沟"——模型能回忆已编辑事实却无法在多步推理链中加以利用。为弥合这一鸿沟，我们提出MCircKE（基于机械回路的**知识编辑**），一种实现精确"映射-适配"编辑流程的新型框架。MCircKE首先识别负责特定推理任务的因果回路，同步捕获事实存储与逻辑结果路由机制；随后在该映射回路内实施精确参数更新。在MQuAKE-3K基准上的大量实验证明了所提方法在多跳推理知识编辑中的有效性。