Asynchronous Multimodal Candidate Reweighting for Multi-Horizon Prediction

Multi-horizon prediction of real-world systems often requires reasoning over multiple candidate futures using heterogeneous observations acquired at different times. These candidates may be produced by physical simulators, learned predictors, or ensembles, yet existing fusion methods commonly assign each candidate a single reliability score that is reused across all prediction horizons. This assumption becomes restrictive as candidate futures diverge over time and the relevance of asynchronous evidence changes with the prediction target. We introduce Asynchronous Multimodal Candidate Reweighting (AMCR), a general framework that estimates reliability jointly over candidate identity and target time. Rather than synchronizing observations to a common clock, AMCR preserves their native timestamps together with modality, availability, and quality, and constructs target-conditioned contexts through modality-aware temporal relevance weighting. The resulting candidate-time reliability matrix enables the same candidate bank to be reweighted differently across prediction horizons. We establish a bounded-influence result showing that the effect of temporally distant evidence decays under the proposed temporal gate, together with a sufficient condition for candidate-selection stability. We evaluate AMCR under controlled timestamp perturbations, missing modalities, evidence-quality variation, and horizon-dependent candidate changes, and further instantiate it for physics-based oil-spill evolution using satellite imagery, vessel trajectories, environmental forcing, and simulator-generated futures.