According to @godofprompt on Twitter, Grassmann mixing offers a breakthrough in attention mechanisms by reducing computational complexity from the standard O(L²d) quadratic scaling to O(Ld²) linear scaling for fixed rank r. This improvement has significant implications for handling long sequences efficiently, as the performance gap between traditional attention and Grassmann mixing grows exponentially with sequence length. This advancement is not merely theoretical but can be practically leveraged to improve the scalability and efficiency of large language models in production environments.