WorldAgent++: A Deep, Constraint- and Verification-Aware Blueprint for Building World Agents

A world agent is a decision-making system that maintains an internal, learned simulator of its environment and uses that simulator to imagine counterfactual futures before acting. While learned world models have made rapid progress, turning them into robust, safe, and reproducible agents remains difficult: multi-step rollouts drift, planning induces distribution shift, and agents exploit model bias; moreover, real deployments demand explicit constraints (feasibility, physical safety, logical validity) and, for tool-using agents, semantic verification (schema correctness and groundedness). This paper provides an implementation-grade, deeply detailed blueprint for building world agents, including (i) belief-state inference under partial observability, (ii) ensemble latent dynamics for epistemic uncertainty, (iii) reward/value/constraint heads with calibration, (iv) risk-and constraint-aware MPC optimized by CEM, (v) verification-as-constraints for tool use, and (vi) decisionfocused replay and rollout-consistency regularization. We also provide a complete experimental protocol and tables covering per-suite task specifications, constraint label rules, ablation grids, sensitivity sweeps, compute accounting, and failure taxonomy. All reported numbers are clearly marked as illustrative placeholders to be replaced by actual logs.