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A neural memory module is a class of subnetwork whose parameters can be dynamically updated during model inference, usually implemented as key-value storage or a differentiable memory matrix, with its weights themselves adjusted in real time along the input stream.
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10/26/2026
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UnverifiedThe concept of the neural memory module can be traced back to early work such as Neural Turing Machines and the Differentiable Neural Computer, whose core idea is to model memory read and write operations as differentiable computations.78% similarUnverifiedCausal neural memory is a class of external memory modules updated incrementally in a causal (autoregressive) manner, whose typical implementations include linear attention, state space models, or fast weight mechanisms.76% similarUnverifiedGoogle's Titans architecture introduces an independent neural memory module that updates its own weights in real time during inference, enabling the model to continuously adapt to new data at the inference stage without retraining the entire model.74% similarUnverifiedNeuromorphic computing is a class of computing paradigms that draws on the structure and information processing methods of the nervous system, with typical features including spike encoding, event-driven operation and compute-in-memory.73% similarUnverifiedReal-time classifiers are auxiliary neural networks deployed in model inference pipelines, typically built on fine-tuned small language models or BERT-class architectures.73% similar
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