FutureMap: Omnimodal Future Prediction with Temporal Hypergraphs

Future prediction asks what will happen, and when, from only the evidence visible at a vantage point, and large language model (LLM) agents now make it feasible over archives whose precursors span five modalities. Forecasters have progressed from agentic search to temporal knowledge graphs and hypergraphs, and recently to omnimodal inputs. However, they leave omnimodal evidence unstructured, guess the regularities linking precursors to outcomes, and date an event on whatever deadline they are shown. We formalise Omnimodal Future Prediction (OmniFP) and release OmniFP-Bench, 1,530 questions over eight domains and 7,422 frozen resources in five modalities. We propose FutureMap, a training-free forecaster that builds an omnimodal temporal hypergraph, backtests hyper-rules with time and magnitude kernels, and reads one deadline-blind deduction out in code. Against twelve baselines, FutureMap leads on all three backbones by +4.1 to +5.3 points.