Test-time KV binding is secretly linear attention, not memorization.
Test-time training (TTT) with KV binding as sequence modeling layer is commonly interpreted as a form of online meta-learning that memorizes a key-value mapping at test time. However, our analysis reveals multiple phenomena that contradict this memorization-based interpretation. Motivated by these findings, we revisit the formulation of TTT and show that a broad class of TTT architectures can be expressed as a form of learned linear attention operator. Beyond explaining previously puzzling model behaviors, this perspective yields multiple practical benefits: it enables principled architectural simplifications, admits fully parallel formulations that preserve performance while improving efficiency, and provides a systematic reduction of diverse TTT variants to a standard linear attention form. Overall, our results reframe TTT not as test-time memorization, but as learned linear attention with enhanced representational capacity.
Test-time training with KV binding functions as linear attention, not memorization.
Gradient ascent performs comparably to gradient descent, contradicting memorization theory.
Increasing inner-loop gradient steps degrades performance despite improving inner-loop loss.
Paper analysis incomplete; t-SNE visualization section cuts off mid-sentence.
Scope limited to language modeling, novel view synthesis, and image classification tasks.
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