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DSReg: Provably Recovering Individual World Latents without Reconstruction

This paper introduces DSReg, a method that provably recovers individual world latents without requiring reconstruction, decoders, or labels. It leverages Structural Diversity, where different latents leave distinct dependency footprints on observations, enabling the recovery of individual latents up to signed permutation. DSReg builds on linear identifiability from LeJEPA and improves latent recovery without compromising dense prediction.

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PublishedOctober 7, 2026Yujia Zheng, David Klindt, Randall Balestriero
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WHY IT MAY MATTER

This method enables more accurate recovery of individual latent variables in representation learning without relying on reconstruction or additional supervision, which can improve the interpretability and utility of learned representations.

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