Evolution of Large Model Technologies: World Models Drive Artificial Intelligence from Perception to Decision-Making (Invited)

Large Language Models (LLMs) have propelled artificial intelligence into an era of natural language-centric interaction; however, they remain significantly limited in terms of physical world modeling and complex decision-making. To address these limitations, this paper considers the world model as its core paradigm and systematically analyzes the key technical pathways for the evolution of LLMs into decision-making agents. First, the capability boundaries of LLMs are delineated, highlighting their intrinsic limitations in structured knowledge representation, real-world perception, and applications that require high reliability. Subsequently, the core essence and key characteristics of world models are summarized in terms of dynamic prediction, task-driven selective modeling, multimodal fusion, and physical consistency. Building on this, data-driven generative modeling and physics-prior-driven simulation modeling are systematically reviewed and compared. Additionally, common technical challenges, including acquisition of high-quality interactive data, long-term prediction consistency, unified multimodal representation, and real-time inference efficiency, are analyzed. Furthermore, the potential and limitations of world models in bridging common-sense gaps, enhancing planning and decision-making capabilities, and supporting embodied intelligence on the path toward Artificial General Intelligence (AGI) are discussed. Finally, considering current technological trends, a forward-looking perspective on future research directions, including LLM-world model integration, data and algorithm co-optimization, fusion of physics priors with generative modeling, tight integration with embodied intelligence, and ethical and safety governance, is provided. This paper systematically analyzes the current status and future development of world-model technologies and provides theoretical and practical guidance for advancing artificial intelligence from perception-to decision-driven capabilities.