Decoupled Predictive Decision Making: A Shared Conditional Predictor for Agents

Decoupled Predictive Decision Making (DPDM) is an architecture that formulates heterogeneous agent decisions as conditional prediction using a shared pretrained learner. Language models provide semantic representations of current and historical records, while a general-purpose conditional predictor predicts task-defined targets from the represented evidence. Pretraining supplies reusable inference capability, and deployment evidence instantiates problem-specific predictions without updating either model's neural parameters. A single frozen LimiX-2 predictor serves all five task categories.