Resolving Hard Paraphrase-Unrelated Query Regimes in Lifelong Model Editing

Lifelong model editing requires Reliability on edited knowledge, Generality over semantically equivalent paraphrases, and Locality on unrelated queries. In memory-based editors, these objectives depend on routing each query to either the edit-specific path or the base-model path correctly. We identify **hard paraphrase-unrelated query regimes**, where paraphrases are lexically distant from the edit query, unrelated queries are lexically near it, or both. Although defined by surface distance, these regimes exhibit the same distance trend in hidden-state representation space across evaluated backbones, increasing routing-score overlap between paraphrase and unrelated queries and thereby making simultaneous Generality and Locality difficult for memory-based routers. We propose **SEAL** (**S**yn**E**rgistic intr**A**- and extra-mode**L** framework), which combines an intra-model Hopfield-based router with a lightweight extra-model Sentinel Prompt used at test-time inference. The Sentinel Prompt induces an Asymmetric Pulling Effect, suppressing unrelated-query routing scores while maintaining or increasing paraphrase scores, thereby separating the overlapping routing-score distributions. Decoupled Two-Stage Inference removes the Sentinel Prompt on the base-model path to preserve the original output distribution. Experiments on four backbones across four benchmarks show that SEAL improves Generality and Locality in hard regimes.