Retain Knowledge, Adapt to Change: When Does Fitting an Environment Code Help?

Fitting a small environment code can improve a frozen world model after conditions change. The same update can also improve prediction when the dynamics have not changed, because the initial code was estimated imperfectly. We study how to distinguish these effects. Our evaluation combines provably inert physical interventions with matched code replay: codes updated on unchanged and changed probe trajectories are both scored on the same held-out changed trajectories. This defines a signed,change-specific prediction gain without subtracting errors from different test distributions. In a controlled comparison of discrete-time CAVIA and CoDA implementations and a jointly trained context model, ten training seeds and three shift amplitudes expose judgments that aggregate gains hide. On the rolling ball, all three obtain positive change-specific gains; a 6.5-percentage-point total-gain advantage of the joint context model over CoDA becomes an unresolved -0.8-point difference in change-specific gain. On the pendulum, the joint context model's change-informed update is harmful even after control transfer is accounted for. Complementary experiments across nine systems show when code fitting helps prediction and test whether modular structure is needed. Dynamically equivalent interventions also show why counting changed physical parameters cannot by itself explain adaptation difficulty. The contribution is a controlled way to measure what real-change probe observations are worth to an update under a specified intervention, code parameterisation, loss and update schedule, alongside the prediction accuracy that matters at deployment.