Round-Trip Consistency: Bidirectional Diffusion Models Can Predict Their Own Rollout Errors
Autoregressive models accumulate error over long rollouts, yet at deployment there is no ground truth to measure it against. We 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 $i$ steps and then backward $i$ steps must return the model to its start, so the round-trip discrepancy $\mathcal{C}_i$ is a self-supervised proxy for the unobservable rollout error: no ensembles, no held-out data, no governing equations, for one extra rollout. We validate on compressible magnetohydrodynamics (MHD), an astrophysical ...
| Authors | Alexander Scheinker |
|---|---|
| Primary category | stat.ML |
| Published | 1 August 2026 |
| Last updated at source | 13 August 2026 |
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