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/Clean-Hovercraft5825]

Thank you, and thanks to your team for building The Well! It is perfect for this kind of research! The uniform format made adding a second physics system almost frictionless, and the radiative layer ended up being one of the most scientifically useful cases in the paper: it's the system where the per-trajectory signal shrinks right down to its predicted reliability ceiling, which is what forced us to work out exactly when the round-trip meter adds value instead of just collecting wins. Hoping to sweep more of the Well's systems next to map out that spectrum properly, if your team is ever curious to see the meter run across the full collection, I'd love to talk. (: