Identifiable World Models from Pretrained Diffusion Representations

The paper demonstrates that a frozen pretrained diffusion model can be endowed with identifiable latent coordinates via a lightweight alignment map called Contrastive Diffusion Alignment (ConDA). By leveraging auxiliary-variable nonlinear ICA guarantees, ConDA aligns diffusion latents so that the resulting representation recovers latent dynamical states up to permutation and componentwise invertible transformations, preserves the underlying structural causal model, and simplifies lagged graph recovery. Experiments on physical and robotic video systems show that ConDA, especially under TCL and GCL assumptions, achieves near-perfect state recovery and accurate lagged graph reconstruction, even in simulated falling-body and bipedal robot scenarios.

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• diffusion
• robotics
• efficiency
• safety