MinMax RNCs are recurrent neural models using min-max recurrence that achieve full regular-language expressivity, logarithmic parallel evaluation, uniformly bounded states, and constant state gradients independent of time distance.
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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 neural models using min-max recurrence that achieve full regular-language expressivity, logarithmic parallel evaluation, uniformly bounded states, and constant state gradients independent of time distance.
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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.