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CoT transformers can simulate Word RAM algorithms with poly-logarithmic overhead, revolutionizing our understanding of their computational efficiency.
Linear RNNs achieve transformer-like parallelization because they're essentially log-depth arithmetic circuits, while nonlinear RNNs are fundamentally limited by their ability to solve computationally harder problems.
Forget massive transformers: tiny hybrid models can achieve state-of-the-art zero-shot time series forecasting with 100x fewer parameters.
