Lifelong knowledge editing aims to inject a stream of factual updates into large language models (LLMs) without retraining, yet existing memory-based editors often suffer from catastrophic forgetting as edits accumulate. We argue that a key factor is the coupled knowledge memory mechanism, where addressing (routing) and storage (writing via memory-module updates) are entangled. This entanglement makes it difficult to confine the effects of each edit to its intended scope, particularly in multi-domain and associated-fact editing streams, where updates either span diverse semantic domains or repeatedly modify related attributes of the same subject. Consequently, updating memory for one edit inadvertently alters the routing and stored representations of previously injected edits, leading to catastrophic forgetting as edits accumulate. We propose **DKME**, which decouples addressing from storage via two stages: decoupled semantic addressing learns a fact-aware manifold for scope-aware routing, and partitioned memory storage localizes edits to memory partitions identified by unsupervised clustering in the embedding space. Experiments on three benchmarks, including HalluEditBench, CKnowEdit, and WikiDatacounterfact, demonstrate that DKME consistently achieves a more favorable trade-off between editing success and locality compared to baselines, while maintaining more stable performance as the edit scale increases.

“Domain-specific text classification often needs more external knowledge, and fraud cases havefewer descriptions. Existing methods usually utilize single-stage deep models to extract semanticfeatures, which is less reusable. To tackle this issue, we propose a two-stage training frameworkbased on within-task pretraining and multi-dimensional semantic enhancement for CCL23-EvalTask 6 (Telecom Network Fraud Case Classification, FCC). Our training framework is dividedinto two stages. First, we pre-train using the training corpus to obtain specific BERT. The seman-tic mining ability of the model is enhanced from the feature space perspective by introducing ad-versarial training and multiple random sampling. The pseudo-labeled data is generated throughthe test data above a certain threshold. Second, pseudo-labeled samples are added to the trainingset for semantic enhancement based on the sample space dimension. We utilize the same back-bone for prediction to obtain the results. Experimental results show that our proposed methodoutperforms the single-stage benchmarks and achieves competitive performance with 0.859259F1. It also performs better in the few-shot patent classification task with 65.160% F1, whichindicates robustness.”