StarWM uses self-supervised dynamics to route attention for selective reconstruction, yielding robust world models that preserve task-relevant states and discard distractors across video backgrounds.
A kernel-based method defines operational design domains a posteriori from data, yielding deterministic, bounded, unit-invariant representations that support certifying safety-critical AI systems.