DSReg provably recovers individual world latents without reconstruction, improving sparse control and editing across synthetic, pixel-trained, and external-renderer benchmarks
The work introduces DSReg (Dependency-Sparsity Regularization), which, on top of the linear-identifiability premise provided by LeJEPA, proves that under Structural Diversity—distinct dependency footprints of latents on observations—each world latent can be recovered up to signed permutation with no reconstruction, no decoder, and no labels; the method applies post hoc to any linearly identified representation, reuses trained checkpoints, and across synthetic regimes, world-model probes, pixel-trained encoders, and external renderers preserves dense prediction while improving individual-latent recovery and downstream sparse control, editing, and few-shot readout.