Working paper evaluates memory capacities of AsoMemm in a modular machine-learning framework, suggesting implications for retrieval methods.
SPIRALbase is a working paper on context-gated associative memory as a modular machine-learning component. The paper studies AsoMemm, the first SPIRALbase instantiation, and shows how masked storage, replay, pseudo-likelihood learning, and structural partitioning affect recall, interference, and capacity in a shared substrate. It also evaluates a text-facing and language-model interface, arguing that the same memory core can support bounded persistent retrieval without collapsing into exact database lookup.
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Robin Langell (2026) studied this question.
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