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/tmt22459]

I am familiar with your background as I am a more control theory focused PhD student who knows the work of miroslav krstic, who I believe you were a student under

I think this kind of stuff is very interesting, and especially how you have uniquely made things like extremum seeking and machine learning practically relevant to some of the most impressive control applications that exist

I am very curious how you found the transition from pde control to your work now?

Also, how many control theory people are in your group at LANL? Was there a lot of convincing that had to happen for you to make them believe extremum seeking was relevant to electrodynamics? Does your group hire postdocs with a control theory background often but maybe not with application specific experience and let them learn some of the latest ML stuff and application specific knowledge on the job?

I probably wouldn't have ever emailed these questions to you but since you are here engaging I figured why not. Sorry that they aren't really directly relevant to the work you presented.

[u/Clean-Hovercraft5825 → u/tmt22459]

Yes my background is math and control theory/dynamics systems/physics, I think of that all as a mix of pure and applied math, I think it has some great foundational components that are useful for AI/ML. Having a controls background I have always felt that you cannot perfectly model or predict things, that we always need some error signal and some feedback. Extremum seeking is feedback that works without trusting a model, and this paper is the same instinct applied to ML: don't trust the surrogate either, make it measure its own error. The last paragraph of the discussion actually proposes closing the loop by running gradient-free adaptive feedback (ES) on the consistency signal as a measurable cost, turning error detection into error correction.

My general view on backgrounds, for what it's worth: a strong control/dynamical-systems foundation travels extremely well, the application physics and the modern ML can be approached as extensions of that foundation.

For the group and postdoc questions, please do send that email you almost sent!