Why learning multiple tasks together doesn't cost extra

Learning multiple related operators (functions) simultaneously can leverage shared structure without sacrificing efficiency. The authors prove that Multiple Neural Operators achieve near-optimal approximation and generalization bounds for Lipschitz operator maps—matching single-task learning complexity despite the added multi-task burden. They also show DeepONet variants perform equivalently, establishing that task sharing doesn't incur hidden costs in worst-case scaling.