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Modular TTT treats test-time training (TTT) layers as composable inner-learning graphs rather than as separately hard-coded model variants. A TTT memory is represented as a directed acyclic graph whose nodes are primitive operations such as `Linear`, `Act`, `Norm`, `Add`, `Mul`, and loss functions. Given local train-view, train-view backward, and query-view rules for each primitive, the framework composes the full TTT computation automatically. 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