Social NeuroAI of World Modeling: Embodied Dynamics, Collective Intelligence and Relational Computation

World models are now a central paradigm in artificial intelligence, providing latent dynamical structures that support prediction, planning, and counterfactual reasoning. Yet prevailing formulations remain largely individualistic: agents model environments, but rarely other model-bearing agents in a principled way. In contrast, biological intelligence is intrinsically relational. Neural systems are embodied, socially coupled, and dynamically co-regulated across multiple timescales.

In this talk, I argue for a reformulation of world modeling through the framework of Social NeuroAI. Drawing on dynamical systems theory, inter-brain neuroscience, and multi-agent computational modeling, I show how intelligence can be understood as emerging from coupled embodied dynamics rather than isolated inference. I will present recent work on embodied neural agents demonstrating how metastable coordination and collective decision making arise from structured neural coupling, and discuss how these mechanisms motivate a shift toward relational computation in AI architectures.

I propose that next-generation world models should treat other agents as dynamical systems with their own latent models, enabling collective intelligence to emerge through interaction. This perspective connects embodied dynamics, social inference, and generative world modeling within a unified computational framework. The goal is to move from solitary simulators to socially embedded systems whose intelligence is constructed through coordination, alignment, and shared representation.