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.

A situated query like "where is Lin Wei?" often encodes more than its literal content: the user may also want to know whether Lin Wei is free, in a good mood, or worth interrupting now. Standard tool-use agents answer the literal question and stop. AURA inserts an inference step between scene perception and tool use that produces an IntentFrame: a structured estimate of the implicit need with a scalar gap score that controls per-query probe budget and tool selection. On a 100-query four-scene implicit-intent benchmark, AURA improves implicit-need coverage over ReAct-style probing (Delta = +0.07, p < 10^-6); three of four scenes are individually significant, the gain reproduces on a second backbone, and a prompt ablation attributes the lift to gap calibration rather than answer memorisation. A component ablation localises the gain further: the gap-derived budget ceiling alone outperforms the full architecture (p = 0.02), and bin-wise calibration analysis (r = -0.33) identifies the gap score as a probe trigger rather than a calibrated value estimator. On factual lookup the controller trades raw accuracy for 82% fewer probes and zero forbidden-tool violations on a privacy-sensitive slice; scope conditions are detailed in Limitations. Code, simulator, and benchmark are released at https://github.com/innovation64/AURA, with the implicit-intent benchmark also on the HuggingFace Hub (innovation64/aura-implicit-intent).