The Global Neural World Model: Spatially Grounded Discrete Topologies for Action-Conditioned Planning
We present the Global Neural World Model (GNWM), a self-stabilizing framework that achieves topological quantization through balanced continuous entropy constraints. Operating as a continuous, action-conditioned Joint-Embedding Predictive Architecture (JEPA), the GNWM maps environments onto a discrete 2D grid, enforcing translational equivariance without pixel-level reconstruction. Our results show this architecture prevents manifold drift during autoregressive rollouts by using grid ``snapping'' as a native error-correction mechanism. Furthermore, by training via maximum entropy exploration (random walks), the model learns generalized transition dynamics rather than memorizing specific e...
| Authors | Noureddine Kermiche |
|---|---|
| Primary category | cs.LG |
| Published | 17 April 2026 |
| Last updated at source | 17 April 2026 |
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