When Correct Predictions Require Incorrect States: A Compatibility Audit of Physical State Interfaces

Accurate predictions can require a state estimate outside its physical tolerance. We audit this conflict when several prediction queries share one state. A task-passing input shows that the queries can be satisfied. Ruling out task success throughout the allowed state neighborhood then certifies necessary distortion for every successful readout using that interface. On RoboMNIST, learned readouts pass the tasks on windows certified to require distortion, including frozen tests at other speeds and on another robot. The conflict persists with nonlinear heads that reduce MSE at the reference velocity by 28–35%. We then evaluate acceleration augmentation on 1,440 further windows. Relative to ordinary constant acceleration, mean full joint passing rises by 1.11–2.78 percentage points in three settings and falls by .28 points at medium speed, under the declared .5 m/s² acceleration tolerance. For released robot models, we certify which distance relations cannot be satisfied anywhere in the allowed joint neighborhoods. We then check the calibrated models at the same recorded inputs. At .01-degree joint and 1 mm distance tolerances, MUKCa’s published calibrated models recover 98–100% of certified incompatible pairs on FR3 and two Panda robots, with weaker recovery on Kinova. The audit identifies conflicts that a readout change cannot resolve and tests whether changing the interface removes them.