CALM-VR: Candidate-Specific Temporal Readout for Early VR Target Prediction

Early target prediction in virtual reality (VR) ranks changing legal objects from causal multimodal histories. We ask whether candidate-specific temporal readout adds value when candidate information already enters both the final scorer and aligned history summaries. CALM-VR encodes gaze, head, and controller histories with modality-specific GRUs, then uses each candidate token to retrieve temporal evidence for masked ranking. A parameter-matched broadcast-query control shares one readout while retaining individual candidate tokens and aligned summaries; crossed controls vary summary alignment separately. An event-aligned, three-task collection provides mutually exclusive 97/21/30 fitting/selection, development/calibration, and frozen-protocol evaluation roles. In the 30-participant evaluation, 500-ms Top-1 was 51.20% versus 46.42% for designated late fusion (+4.78 pp, 95% CI [2.88, 6.68], Holm p < .001). The prespecified secondary query comparison estimated a +2.40 pp overall gain (95% CI [1.35, 3.45], Holm–3 p = .027). Equal task weighting gave a descriptive +1.40 pp point estimate; task-specific contrasts ranged from −3.56 pp in target acquisition to +6.15 pp in sequential assembly. Smaller positive aggregate gains were observed at 800 and 1000 ms. These results quantify the incremental value of candidate-specific temporal readout beyond candidate-conditioned scoring and aligned summaries, with observed variation across tasks in the evaluated VR setting.