HUMAN-SELECTABLE PRESERVED INFERENCE BRANCHES FOR PHYSICAL ARTIFICIAL INTELLIGENCE MISSIONS AND ACTIONS

A physical artificial intelligence system generates multiple candidate mission or physical-action branches before selection and preserves branch-specific reusable inference state for at least two branches. The preserved state may include key-value cache state, world-model state, mission-planning state, navigation state, motion-planning state, object state, person state, predicted physical state, or candidate-action state. An output interface presents a branch-selectable interaction corresponding to the candidate branches. A verbal, textual, gestural, gaze-based, or other response is mapped to a selected branch identifier. The system retrieves the preserved state associated with the selected branch and continues physical artificial intelligence processing without regenerating at least a portion of an earlier inference or planning sequence. Preserved physical state may be compared with current sensor data so that a valid state portion is retained while an invalid state portion is updated. A resulting physical action may be validated before actuator control.