Emergent Semantic Representations in World Models through Physical Interaction without Linguistic Supervision
What does a world model learn from physical exploration, without any linguistic supervision? We argue the answer is organized by a single principle: the geometric structure of the physical world. Training a VAE-based world model on random embodied exploration, we find that its latent space develops spatial semantic structure that mirrors physical geometry -- direction accuracy 0.677+-0.029 versus 0.547 for a randomly initialized encoder, and position RSA 0.192+-0.047 versus 0.029 for random encoders (6.6x improvement), showing that training induces genuine structural organization beyond CNN inductive bias. Across 20 temporal checkpoints, prediction performance and semantic alignment co-im...
| Authors | Jiayi Fang |
|---|---|
| Primary category | cs.LG |
| Published | 22 May 2026 |
| Last updated at source | 3 July 2026 |
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