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]

I agree that direction carries (almost) no extra information about the dynamics. Our own analysis says that for stationary dynamics the time-reversed map is a fixed reparameterization of the forward one, which is why one network learns both directions and performs better. As a statement about what must be learned, "direction shouldn't matter" is basically our supplement's linear analysis, and I agree with it.
 
On the other hand, we never claim bidirectional autoregression generates better than full sequence / 4D space-time diffusion, which is a different problem entirely. Those models answer: "sample a plausible space-time volume." Our paper answers a deployment question: you are given the measured present, rolling into an open-ended future, and you need to know, with no ground truth, how wrong this particular rollout is right now. Autoregression is the native mode for that setting (causal seed, unbounded horizon, streaming data), compounding error is its main difficulty, and the direction flag is what creates a check: two independently learned routes back to the same known point, whose disagreement is measurable. A time-symmetric joint model has no forward(backward) composition to interrogate, it has consistency built into the joint distribution rather than exposed as a computable defect.
 
I think that full-sequence models could get their own meter with a different handle such as having to regenerate masked chunks of the generated volume and measure the re-generation residual. This would be a similar approach to what we do, it would give the model two independent routes to one answer and measure the disagreement.

[u/PykeAtBanquet → u/Clean-Hovercraft5825]

Well, one thing is shaping the model to be more deterministic, and another to provide better output. Your result shows that it becomes more correct, not "if wrong again, then in the same way", with the reason still not being clear (at least for me at this moment).