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Round-trip consistency allows generative models to self-assess their rollout errors, achieving remarkable accuracy without the need for ensembles or ground truth data.
Reinforcement learning policies can maintain high performance even when the world changes by combining them with a simple, model-free optimization loop.
A single Latent Evolution Model can solve the trifecta of forward modeling, inverse problems, and tuning for accelerator beam diagnostics, offering a unified approach to traditionally separate challenges.
