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StreamPI transforms VLA models by enabling them to retain temporal context and enhance spatial perception, outperforming traditional single-frame approaches.
EO-WM achieves unprecedented accuracy in forecasting vegetation response to extreme weather, outperforming traditional models by effectively modeling uncertainty and cumulative stress.
Flow-based VLAs can react to environmental changes ten times faster by adaptively prioritizing near-term actions during sampling, unlocking unprecedented real-time responsiveness.
