n square to n uh memory and compute requirements in a single layer but in the worst case you need n layers to recover the the power of the full trend of the full transformer and that is the last one of their theoretical results right here so first they prove universal approximations and second they prove turing completeness these two properties have been proven for full attention mechanisms and third they prove that there are tasks where you actually do need n layers to solve them with their limited attention