arxiv.org
NeuroWorld: A Latent Brain World Model for Stimulus-Conditioned Human Brain Dynamics
Forecasting human brain activity during naturalistic experience requires modeling how endogenous neural states evolve causally under continuous sensory drive. Existing brain encoding models instead frame this as stimulus-to-response regression without strict temporal constraints, allowing future stimuli to leak into current predictions. We introduce NeuroWorld, to our knowledge the first brain world model, which casts naturalistic brain functional dynamics prediction as stimulus-conditioned evolution in a learned latent brain-state space, separating endogenous states (measured via fMRI) from exogenous multimodal stimuli across two stages. Latent Dynamics Learning (LDL) jointly learns a transition-sufficient representation and causal dynamics through next-latent prediction, without reconstructing the observed fMRI signal. Latent Rollout Decoding (LRD) freezes LDL, autoregressively rolls latent states forward from an observed fMRI prefix, and decodes them into subject-specific whole-brai