Researchers hand-coded weights for one-layer MLPs to memorize token sequences of length two. These models achieve 90% accuracy with a memory capacity that scales linearly with parameter count. While they mirror the scaling behavior of trained models, the AI Alignment Forum report notes a significant gap in efficiency. This suggests a limit to manual weight engineering.