PERCEPT-VR++: Learning Pre-Update Perceptual Risk for Risk-Aware Visual Update Scheduling in VR

A perceptual-risk score for a pending visual update may no longer describe the update after a scheduler changes its attributes. We study pre-update risk prediction conditioned jointly on multimodal behavioral history and the proposed update. PERCEPT-VR++ instantiates this formulation with M2P-CF, using eye dynamics, head motion, scene context, and update attributes. We construct VR-Mask++ through nine independently administered scene studies with 450 participants, collecting 9,000 candidate trials and retaining 8,301 valid multimodal events. Held-out AUROC increases from 0.766 for the strongest evaluated non-M2P baselines to 0.831 for M2P-CF; beta calibration reduces its ECE from 0.109 to 0.043. An independent closed-loop study recruits 55 new participants and records 7,425 candidate policy-event observations. False-safe rate decreases from 0.105 for calibrated prediction-only to 0.059 for current-score Safe-UARS and 0.030 for re-query Safe-UARS, while applied-update coverage rises from 0.231 to 0.265 and 0.280. Current-score and re-query share all frozen policy parameters, differing only in whether risk is re-estimated after update attributes change. Re-query yields a participant-paired FSR difference of −0.029 (95% CI [−0.053,−0.005]); both adjacent-policy paired intervals exclude zero, with consistent ordering across scene families. These results support re-estimating risk for the update actually proposed for application, rather than retaining the score of its original specification.