<ul><li>LrcSSM is a nonlinear recurrent model designed for efficient sequence modeling, capable of processing long sequences quickly.</li><li>The model achieves parallel processing of full sequences with a single prefix-scan, leading to optimal time and memory complexity.</li><li>LrcSSM provides a formal gradient-stability guarantee that sets it apart from other systems like Liquid-S4 and Mamba, making it reliable for training.</li><li>In comparison to quadratic-attention Transformers, LrcSSM shows superior performance on long-range forecasting tasks, outperforming models like LRU, S5, and Mamba.</li></ul>

Scaling Up Liquid-Resistance Liquid-Capacitance Networks for Efficient Sequence Modeling

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