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Closing the sim-to-real gap for LiDAR in adverse weather is now possible: physics-informed GANs generate synthetic data that allows models to perform as well as if trained on real-world data.
Decoupling reasoning from action generation in autonomous driving VLMs lets you beat larger end-to-end models while slashing training costs.
