MentalSimulationGPT: A General-Purpose Multimodal Artificial Intelligence Framework for Prospective Scenario Construction, Counterfactual Reasoning, and Predictive Decision Simulation

Contemporary generative artificial intelligence systems primarily operate through information retrieval, language generation, and reasoning over existing representations. Although recent advances in large language models have substantially improved planning, problem solving, and multimodal understanding, an important capability remains insufficiently integrated into general-purpose AI interaction: the ability to construct and mentally simulate alternative situations before an action is performed, evaluate their potential consequences, and use the simulated outcomes to support better decisions. Human cognition frequently employs mental simulation to anticipate future events, imagine alternative possibilities, examine potential actions, and prepare for uncertain circumstances. Rather than relying exclusively on immediate responses or retrospective evaluation, individuals can construct internal representations of situations that have not yet occurred and examine how different conditions or decisions might influence subsequent outcomes. Inspired by this cognitive capability, this paper proposes MentalSimulationGPT, a conceptual framework for a general-purpose artificial intelligence system designed to perform structured mental simulation before recommending, executing, or evaluating actions. The proposed architecture integrates six interconnected mechanisms: contextual state representation, scenario construction, prospective simulation, counterfactual exploration, predictive consequence evaluation, and simulation-guided decision support. A central component is an adaptive simulation engine that generates multiple possible future trajectories under alternative assumptions, actions, and environmental conditions. The framework further introduces a simulation–evaluation–revision loop through which candidate scenarios can be repeatedly examined, compared, and reconstructed as additional information becomes available. Importantly, MentalSimulationGPT is designed to support multimodal interaction, allowing simulations to be constructed from and communicated through text, images, audio, video, spatial representations, and other structured information. This capability potentially extends the framework beyond conventional language-based reasoning toward visual situation understanding, audio-based event anticipation, spatial planning, embodied interaction, and multimodal decision support. The proposed system is intended for general-purpose applications, including scientific research, education, engineering, business, robotics, healthcare information support, and everyday decision-making. This paper establishes a conceptual and technically oriented foundation for mental-simulation-oriented artificial intelligence, presents an architectural implementation model, and identifies research hypotheses and evaluation criteria for future empirical investigation. No experimental performance or demonstrated superiority is claimed at this conceptual stage.