A robot can be fast on average and still drift when the scene changes between planning steps. The camera catches a misplaced object. Someone steps into the workspace. The system has to decide whether to finish the imagined action or replan from reality. That recovery path matters more than one sampler throughput number.
The LingBot-VA 2.0 design pairs foresight reasoning with regrounding when new observations arrive. Its headline number is more than a fourfold end to end speedup. Before treating that as a control result, show tail latency, prediction horizon, regrounding frequency, and recovery after forced mismatches. Fast is useful, but only while the imagined state stays close to reality.
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