TEMPORAL HYPERGRAPH JOINT- EMBEDDING PREDICTIVE ARCHITECTURE FOR MULTI-AGENT PATH FINDING

Multi-Agent Path Finding (MAPF) becomes difficult in dense environments where multiple agents must coordinate through shared aisles, intersections, and bottlenecks. Existing learned methods typically predict actions from the current interaction state, while pairwise representations can lose information about conflicts involving several agents. We introduce TH-JEPA, a Temporal Hypergraph Joint-Embedding Predictive Architecture that models MAPF as the evolution of higher-order agent-resource interactions. TH-JEPA first constructs a dynamic conflict hypergraph from anticipated shared-resource usage, with persistent resource nodes preserving conflict identity over time. Training proceeds in two stages: masked temporal pretraining learns the structure of conflict evolution without actions, followed by action-conditioned post-training that predicts future agent and resource representations over multiple horizons. To prevent collapse in the shared latent space, we introduce Conflict-Spectral Dispersion Regularization (CSDR), which preserves variation over the conflict hypergraph while discouraging redundant latent directions. At inference, candidate joint-action sequences are rolled forward in latent space and evaluated using receding-horizon planning. We further establish that pairwise projection cannot, in general, preserve higher-order conflict structure and derive sufficient conditions under which CSDR prevents structural collapse. Experiments across dense and out-of-distribution MAPF settings show improved scaling to larger agent populations, stronger performance under higher-cardinality conflicts, and accurate multi-horizon prediction.