Depth on Demand
The last post made depth a resolution: layers are time steps of a learned flow, and running more of them just renders the same trajectory finer. But every camera knows not to spend equal film on empty sky. This post gives a trained network the integrator's next tool, an error controller that chooses its own step size per input, with no retraining: the same weights, rendered to tolerance. The controller reproduces the reference verdicts at a fraction of the steps, its cost follows the integrator's textbook one-third power law, and the map of where it spends is a genuine surprise: effort tracks the stiffness of the learned flow, not the difficulty of the classification.