大模型技术演进:世界模型让人工智能从感知走向决策(特邀)OA

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.