Emergent Semantic Representations in World Models through Physical Interaction without Linguistic Supervision

This research explores how world models learn semantic representations from physical exploration without linguistic input. It finds that the latent space develops spatial semantic structures mirroring physical geometry, with semantic alignment improving alongside prediction performance.