WRC 2026 | Native Multimodal World Model: From Simulated World to Interactive World
The WRC 2026 conference focuses on native multimodal world models, a technology aimed at advancing AI from simulated worlds to interactive worlds, representing significant progress in the field of agents.
World models are the core capability for AI to understand and predict environmental dynamics, while multimodality means the system can process multiple types of information such as text, images, and videos simultaneously. Native multimodal world models deeply integrate these two capabilities, providing AI with a more comprehensive cognitive foundation.
Traditional AI largely remains at the level of data analysis and content generation, whereas the goal of world models is to enable AI to possess the ability to plan and make decisions in virtual or real environments. The leap from simulation to interaction marks AI's shift from passive response to active exploration, which is a key step in building artificial general intelligence.
The maturity of such technology will greatly promote the development of fields such as agents, robotics, and autonomous driving. AI systems will be able to predict the consequences of actions more accurately, thereby making better decisions in complex dynamic environments, laying the foundation for building safer and more efficient automation systems.
Currently, global tech giants and research institutions all regard world models as one of the core technologies for next-generation AI. The related discussions at the WRC 2026 conference reflect the industry's common pursuit of achieving higher levels of AI autonomy and the exploration of technical routes.
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