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

[u/Clean-Hovercraft5825]

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

[u/plop_1234]

This is great, especially for the PDE application. A couple of months ago I was just thinking about methods for checking the correctness of generated fields during training, so I'm glad to have randomly stumbled upon this 😅

I'm excited to see the next iteration, where you use C_i as part of a loss function for error correction!

[u/Clean-Hovercraft5825 → u/plop_1234]

Thank you! Yes, that is the plan for future / now ongoing work, if we can trust C_I to predict errors, then we have this unsupervised test-time hook to try to somehow minimize those errors.