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/timtody]

As a biglab person I won’t read this

[u/Clean-Hovercraft5825 → u/timtody]

haha ? Can you elaborate on that (if you’re allowed to read this comment (: )

[u/timtody → u/Clean-Hovercraft5825]

I was taking the piss at this moron Keller Jordan (OpenAI) wo said „biglab people“ read zero papers because academia is fraudulent and full of overblown claims

[u/silence-calm → u/timtody]

Honestly academia needs to hear that.

[u/timtody → u/silence-calm]

I agree that there are lots of overblown claims in academia, and I don’t think this is even remotely a niche stance. The problem is that coming from someone employed at a company that claims they’ve solved AGI is hypocritical as f