We unpack HRM, a brain-inspired two-module architecture with a high-level H for abstract planning and a fast low-level L for detail work. Featuring hierarchical convergence, adaptive computation time, and a one-step gradient method, HRM achieves striking results on hard tasks—like Sudoku Extreme, large mazes, and ARC—with far fewer parameters and no pretraining. This Deep Dive explores how emergent dimensionality and brain-like organization could herald more efficient, general AI—and what remains to be understood about its real-world applicability.
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