MinMax RNCs are recurrent networks over the min-max semiring that achieve regular language expressivity, log-depth parallel scan, uniformly bounded states, and non-vanishing state gradients while showing competitive empirical performance.
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Kaczmarz Linear Attention replaces the empirical coefficient in Gated DeltaNet with a key-norm-normalized step size derived from the online regression objective, yielding lower perplexity and better needle-in-haystack performance.
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MinMax Recurrent Neural Cascades
MinMax RNCs are recurrent networks over the min-max semiring that achieve regular language expressivity, log-depth parallel scan, uniformly bounded states, and non-vanishing state gradients while showing competitive empirical performance.
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Kaczmarz Linear Attention
Kaczmarz Linear Attention replaces the empirical coefficient in Gated DeltaNet with a key-norm-normalized step size derived from the online regression objective, yielding lower perplexity and better needle-in-haystack performance.