This research paper argues that Test-Time Training (TTT) with key-value binding—previously understood as a way for models to "memorize" data during inference—is actually a form of linear attention. The authors identify a "memorization paradox" where improving the model's internal memory fitting actually degrades task performance, and even reversing the learning process can improve results. By mathematically unrolling the TTT update rules, they prove that complex inner-loop architectures are equivalent to learned linear attention operators. This theoretical shift allows for architectural simplifications, such as removing redundant normalization and momentum components. Furthermore, this new perspective enables fully parallel formulations of TTT, significantly increasing inference speed. Ultimately, the work reframes TTT as a dynamic feature mixer rather than a retrieval system, providing a more efficient framework for sequence modeling.