Round-Trip Consistency: Bidirectional Diffusion Models Can Predict Their Own Rollout Errors
Alexander Scheinker
stat.ML, cs.LG, physics.comp-ph, physics.plasm-ph
Submitted: 2026-08-13
Updated: 2026-08-14
Comments: Code: https://github.com/alexscheinker/round-trip-consistency
Code: https://github.com/alexscheinker/round-trip-consistency
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Terminology
Sources
- Uncertainty Quantification for Forward and Inverse Problems of PDEs via Latent Global Evolution
- Self-Adaptive Forecasting for Improved Deep Learning on Non-Stationary Time-Series
- Improving Motion Forecasting for Autonomous Driving with the Cycle Consistency Loss
- Uncertainty Toolbox: an Open-Source Library for Assessing, Visualizing, and Improving Uncertainty Quantification
- Cycle Consistency-based Uncertainty Quantification of Neural Networks in Inverse Imaging Problems
- Benchmarking Autoregressive Conditional Diffusion Models for Turbulent Flow Simulation
- Generative Latent Neural PDE Solver using Flow Matching
- Bidirectional Autoregressive Latent Diffusion for Forward and Inverse Magnetohydrodynamics
- Zero-Shot Uncertainty Quantification using Diffusion Probabilistic Models
- DiffusionRollout: Uncertainty-Aware Rollout Planning in Long-Horizon PDE Solving
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