Round-Trip Consistency: Bidirectional Diffusion Models Can Predict Their Own Rollout Errors [R]

Reddit r/MachineLearning 08/06/26, 12:10 PM Papers

diffusion-models bidirectional rollout-error self-supervised latent-diffusion dynamical-systems

Summary

This paper introduces a bidirectional latent diffusion model that steps dynamical systems forward or backward in time, using round-trip consistency as a self-supervised test-time error signal to predict rollout errors without ground truth or ensembles.

Whether generating CELEBV-HQ videos or turbulent plasma fields (digital twins), autoregressive models (such as latent diffusion or flow models) accumulate error over long rollouts, yet at deployment there is no ground truth to measure against. I train a single conditional latent diffusion model that steps a dynamical system forward or backward in time via a direction flag, and show that this bidirectionality supplies a measurement-free test-time error signal: rolling forward steps and then backward steps must return the model to its start, so the round-trip discrepancy is a self-supervised proxy for the unobservable rollout error: no ensembles, no held-out data, no governing equations, for one extra rollout. Furthermore, training both directions in one network is shown to beat two specialist models in both directions. Paper: https://arxiv.org/abs/2608.00675 Code (data generation, training, analysis): https://github.com/alexscheinker/round-trip-consistency Project page: https://alexscheinker.github.io/roundtrip.html

Original Article

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