Could prediction error tell a policy when to stop committing?

Future state prediction is usually discussed as a training signal. I wonder whether the error after each action could also tell a policy when its current plan is becoming unreliable.

Raw error would be a poor trigger because many contact outcomes are naturally uncertain. It would need calibration by task phase and comparison with a policy confidence baseline. If calibrated error rises, the controller could shorten its action horizon, request a new observation, or stop.

LingBot-VA 2.0 uses Foresight Reasoning to predict future visual states before producing actions, but that does not mean its deployed policy exposes prediction error as a control gate. A study could save the predicted state, compare it with the next observation, and check whether a rising residual precedes failed contact or a distribution shift. It would matter only if unsafe commitments fall without an excessive number of false stops.