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T2-Z is introduced for test-time optimization of environment latents, which augments a robotic world model with a learnable environment latent to condition world predictions, and can efficiently infer environment latents from as little as a single test-time interaction trajectory.
DriveReferee is introduced, which uses a learned geometry readout to predict the scene representation from camera observations and executes the geometric safety rule directly rather than learning it, and which matches or outperforms all learned-verifier and heuristic baselines.
PhysWAM is presented, a unified world-action model for autonomous driving that co-denoises multiview video, metric depth, and ego motion within a single flow-matching transformer and Coupled Point Projection is introduced, demonstrating that the geometric relationship between scene depth and ego motion provides a direc...
Logic-VLA is introduced, a formal-requirement-aware VLA that conditions on Signal Temporal Logic (STL) specifications supplied at inference time, showing that a single VLA can adapt its behavior to varying formal requirements without requiring a separate policy for each specification.
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