When Does Structure Matter in Continual Learning? Dimensionality Controls When Modularity Shapes Representational Geometry

ArXi:2604.27656v1 Announce Type: cross To preserve previously learned representations, continual learning systems must strike a balance between plasticity, the ability to acquire new knowledge, and stability. This stability-plasticity dilemma affects how representations can be reused across tasks: shared structure enables transfer when tasks are similar but may also induce interference when new learning disrupts existing representations. However, it remains unclear when and why structural separation influences this trade-off.