Unverified50% confidenceFactExact time
The exact online gradient descent loop is the fixed point of ParaTTT's iterations, while decoding requires no refinement iterations.
1
Sources
50%
Confidence
Long-term
Relevance
10/30/2026
First Seen
Sources
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Related Claims
UnverifiedExisting TTT implementations such as TTT-MLP rely on sequential, chunked weight updates and run several times slower than chunk-parallel linear recurrence.64% similarUnverifiedIteration in latent space eliminates the need to generate intermediate tokens, saving decoding overhead, but at the cost of reduced interpretability.61% similarUnverifiedThe computational cost of one recurrent depth iteration is roughly equivalent to a forward pass through one Transformer layer, but without the need to perform softmax sampling over the vocabulary and token decoding, so the overhead may be orders of magnitude lower for the same amount of thinking.59% similarUnverifiedAlgorithms such as online gradient descent can guarantee O(√T) regret under convex or approximately convex assumptions.57% similarUnverifiedAlthough over-parameterized networks have enough capacity to memorize random labels, the implicit regularization of gradient descent favors learning simple, consistent patterns first.57% similar
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