Predictive Statistics Shape Emergent World Representations of Grid Walkers
Researchers have explored how neural networks, specifically transformers and recurrent networks, develop internal representations of world dynamics. Using a simplified model of constrained random walks on a lattice, they observed that the first attention block in transformers effectively extracts a 'sufficient statistic' representing the walker's state and the problem's constraints. Subsequent layers then transform this state into predictive geometries, revealing a universal world-state representation that can be interpreted as a world model. AI
IMPACT Provides insight into how neural networks internalize data structure, potentially informing future model architectures.