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LAWA is a world action model that uses latent actions to enable efficient future imagination for robot control, achieving state-of-the-art performance with reduced inference latency. It improves over baselines in generalization and efficiency without generating future observations.
Modal introduces Modal Servers, promising 6x faster responses than classic Web Functions, and shares technical details of the architecture underlying their new Auto Endpoints feature.
This paper introduces Learned Relay Representations (Relay), a method that allows masked diffusion models to propagate latent information across denoising steps, overcoming the hard reset problem and improving performance-latency trade-offs. The method is shown to outperform standard supervised finetuning on coding tasks while reducing inference latency by up to 32%.