Action-Conditioned Generative World Models for Intelligent Virtual Environments and Human–AI Interaction

Abstract

Action-conditioned generative world models are computational systems that learn to predict how an environment evolves in response to interventions, and they are increasingly positioned as infrastructure for intelligent virtual environments, embodied agents, and human–AI interaction. Unlike static simulators or purely observational generative models, action-conditioned models couple perception, memory, and control, allowing agents and human users to probe counterfactual futures, coordinate shared goals, and rehearse decisions before acting in physical or institutional settings. This paper develops a system-level analysis of such models by examining their architectural trade-offs, deployment dependencies, and socio-technical governance. It argues that the value of action-conditioned world models lies less in isolated prediction accuracy than in their capacity to support interactive, persistent, and contestable environments. The discussion considers memory as a first-class design concern, because long-horizon interaction requires selective retention, retrieval, and revision of past states and intentions. It then analyzes infrastructure requirements, including simulation engines, data pipelines, compute scheduling, model updates, and observability, and connects these to sustainability and cost constraints. The paper further examines robustness, fairness, and policy implications, especially where synthetic environments inform training, planning, or public decision-making. It concludes that action-conditioned generative world models should be treated as governed public and organizational infrastructure, not merely as model artifacts, and that their legitimacy depends on transparency, accountability, contestability, and participatory design. The analysis offers a research agenda for building virtual environments that are adaptive, resilient, and aligned with human values.