用固定记忆单元替代序列注意力，实现线性计算复杂度的高效模型

While Large Language Models and their underlying Transformer architecture are remarkably efficient, they do not reflect how our brain processes and learns a diversity of cognitive tasks such as language, nor how it leverages working memory. Furthermore, Transformers encounters a computational limitation: quadratic complexity growth with sequence length. Motivated by these limitations, we aim to design architectures that leverage efficient working memory dynamics to overcome standard computational barriers. We introduce Echo State Transformers (EST), a hybrid architecture that resolves this challenge while demonstrating state of the art performance in classification and detection tasks. EST integrates the Transformer attention mechanisms with nodes from Reservoir Computing to create a fixed-size memory system. Drawing inspiration from Echo State Networks, our approach leverages several reservoirs (random recurrent networks) in parallel as a lightweight and efficient working memory. These independent units possess distinct and learned internal dynamics with an adaptive leak rate, enabling them to dynamically adjust their own temporality. By applying attention on those fixed number of units instead of input tokens, EST achieves linear complexity for the whole sequence, effectively breaking the quadratic scaling problem of standard Transformers. We evaluate ESTs on a recent timeseries benchmark: the Time Series Library, which comprises 69 tasks across five categories. Results show that ESTs ranks first overall in two of five categories, outperforming strong state-of-the-art baselines on classification and anomaly detection tasks, while remaining competitive on short-term forecasting. These results demonstrate that by shifting the attention mechanism from the entire input sequence to a fixed set of evolving memory units, it is possible to maintains high sensitivity to temporal events while achieving constant computational complexity per step.