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Streaming Context: Historical observations are continuously incorporated into the model context through streaming inference, reducing per-step compute from 125 TFLOPs to 3.3 TFLOPs while retaining 60 frames of history and supporting up to one minute of visual memory. This moves VLA beyond reactive action generation from single-frame observations toward continuous decision-making grounded in long-horizon visual context. Efficient Inference: Inherits MiniCPM-V 4.6's visual token compression, reducing each frame from 256 to 64 visual tokens for 4× compression. With H100, BF16, and single-frame input, model-forward latency per decision step is 120 ms, compared with 234 ms for π0.5. The measurement excludes task autoregressive decoding. 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