DSReg: Provably Recovering Individual World Latents without Reconstruction

DSReg is a novel method that recovers individual world latents without reconstruction, decoders, or labels. It relies on Structural Diversity, a condition where different latents leave distinct dependency footprints on observations. The method applies post hoc to any linearly identified representation and improves individual-latent recovery and downstream use across various synthetic and real-world scenarios.