CORE-MTL: Rethinking Gradient Balancing via Causal Orthogonal Representations 事件

CORE-MTL: Rethinking Gradient Balancing via Causal Orthogonal Representations arXiv:2606.02221v1 Announce Type: new Abstract: Multi-task learning (MTL) aims to construct a joint model for multiple tasks by sharing a common representation across domains. To achieve this goal, existing optimization-centric methods either balance task gradients or modify the shared architecture. However, as these approaches remain agnostic to the content of the shared representation, they fail to disentangle task-