Decomposing Learning Dynamics to Predict Continual Adaptation

A new unified view of continual learning decomposes how updates change predictions through softmax force, shared readout geometry, and residual connections. This token- and layer-wise decomposition allows for forward-computable approximations of how learning from one piece of data affects future model performance.