Lifelong model editing seeks to continuously incorporate new knowledge into Large Language Models (LLMs) without costly retraining. Memory-augmented editors preserve pretrained weights to protect unrelated capabilities, but struggle to balance Generalization to rephrased edit queries and Locality on non-edited knowledge. This challenge becomes particularly severe under dense editing settings, where semantically related edits induce substantial memory interference. To tackle this problem, we propose MH-HoReN, a multi-head Hopfield retrieval framework that performs retrieval across complementary subspaces and combines head-wise matching signals for more reliable memory selection. We further extend Hopfield-based editing into a clustered batched scheme that combines shared codebook memories for related edits with token-level pooling for efficient knowledge storage and query routing. Extensive experiments across diverse LLMs and editing benchmarks demonstrate that MH-HoReN achieves better locality-generalization trade-offs as editing scales increase, improving the sum of Locality and Generalization by more than 12% over existing methods while preserving almost perfect Reliability in sequential editing, alongside 7.7% gains in Overall Performance under batched editing. Our code is available here.