Event-Aware Tensor-Network Inference for Path Risk in World Models

Estimating whether an event occurs along a future trajectory requires information that accurate terminal-state prediction need not preserve. Matrix product states (MPS) can compress this computation for structured stochastic dynamics, but standard singular-value truncation minimizes local error without regard to its effect on the queried probability. We propose event-aware tensor-network inference: an approximate backward survival function weights forward truncation residuals by their effect on the target event probability, and a cost-aware rule reallocates bond dimension across space and time, with backward propagation, allocation, and recomputation charged to the inference budget. The resulting indicators track the true effect of each compression better than residual magnitude, especially under nonlocal coupling (median Spearman 0.68 vs. 0.39), and ablations confirm that both event sensitivity and the cost term contribute. On structured benchmarks with exact references, the method matches fixed-rank accuracy at equal or lower recorded cost, and in a visual path-event proof of concept it improves Brier score under moderate occlusion. Benefits depend on correlation structure and the evaluated scale is limited; exact propagation remains preferable on small systems.