Recurrent Dynamic

Recurrent dynamics, encompassing systems whose future state depends on their past, are central to understanding complex temporal processes in diverse fields. Current research focuses on improving the robustness and efficiency of recurrent neural networks (RNNs) for tasks like memory, robotic control, and signal processing, employing architectures such as Transformers, RWKV, and gated linear RNNs with state expansion. These advancements aim to address challenges like catastrophic forgetting and limited expressiveness, leading to more powerful and interpretable models with applications ranging from neuromorphic computing to advanced robotics and signal analysis.