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Embodied AI agents can now continuously self-improve by co-evolving their policies, training curricula, and judges, overcoming the bottleneck of static evaluation.
Static infrastructure dominates occupancy perception, occupying 97.3% of voxels, while dynamic elements barely register, challenging existing benchmarks and methods.
SCAPE reduces scenario-level prediction error by up to 34.7%, enabling safer and more efficient deployment of robot-learning policies in real-world environments.
SpikeRestormer achieves state-of-the-art image restoration performance with significantly lower energy usage, revolutionizing the potential of Spiking Neural Networks in real-time applications.
