What if world simulation models could operate not in imagined environments, but directly on real, living cities?

In this talk, we present Seoul World Model (SWM), a city-scale world simulation model grounded in real-world geospatial data. Unlike prior world models that generate plausible yet fictional environments, SWM leverages large-scale street-view imagery and retrieval-augmented generation to produce temporally consistent, spatially faithful simulations of an actual metropolis.

We will briefly introduce the core technical ideas behind SWM -- including cross-temporal pairing, synthetic trajectory augmentation, and the Virtual Lookahead Sink for long-horizon stability -- and demonstrate how these enable controllable, kilometer-scale simulation with realistic geometry, motion, and user-driven scenarios.

Beyond the technical contribution, this session aims to situate SWM within the rapidly evolving landscape of world models and physical AI. We will discuss concurrent efforts from both academia and industry, highlighting key differences between imagined-world generation and real-world grounded simulation.

The session will conclude with a panel discussion focusing on:

* Open research challenges, including dynamic object modeling, data quality, and scaling real-world grounding
* Business opportunities for companies operating large-scale street-view or map platforms (e.g., simulation-as-a-service, digital twins, autonomous driving data generation)
* Strategic implications of geospatial foundation models in the era of sovereign AI

By bringing together perspectives from research and industry, this session explores how world models can evolve from generative media technologies into core infrastructure for understanding and simulating the physical world.