OpenPredictor: Harnessing General-Purpose Prediction

General-purpose prediction must translate open-ended questions into resolvable targets and combine evidence with appropriate estimators across heterogeneous answer types. Language models offer a flexible interface, yet reasoning and tool access alone do not ensure that target semantics are preserved throughout execution. We present OpenPredictor, a unified harness that coordinates this process through explicit task and evidence contracts. Requests become typed targets with declared answer semantics, information cutoffs, and resolution rules. These contracts guide evidence acquisition and restrict estimator selection to compatible methods. The workflow separates model-generated hypotheses from registered numerical operations, retaining units, label identities, and computational dependencies through aggregation and decision construction. Mechanical checks and a model critic guide bounded repair, while linked records make decision derivations inspectable. On FutureX Past, the complete workflow achieves substantial gains over direct answers on several backbones and lower numeric error on jointly answered tasks, although set and ranking accuracy remain challenging. These findings support contract-conditioned execution as a reusable approach to heterogeneous prediction. We will open-source OpenPredictor soon.