From Perception to Simulation: The Emergence of World Models in Multi-modal Reasoning

World models are rapidly reshaping artificial intelligence, evolving from systems that passively perceive the world into engines capable of simulating, reasoning, and planning within it. This tutorial examines how recent advances in generative modeling, self-supervised learning, and multimodal architectures are enabling machines to move beyond recognition and prediction toward mental simulation, counterfactual reasoning, and decision making.

    We will explore the foundations of world models, approaches for learning dynamics from visual and multimodal data, and the integration of planning and reasoning. The tutorial highlights connections between video generation, diffusion models, discrete representations, and embodied AI, while addressing key challenges such as grounding, causality, physical consistency, and evaluation.

    Designed for researchers, practitioners, and students, this session provides both conceptual insights and practical perspectives on building AI systems that reason about environments rather than merely interpreting them.