Autoresearch agents independently hit 98% memory reduction

The "racing" of these two agents proves that Karpathy's hill-climbing approach is stable and repeatable, finding a "natural" solution for the given metric and hardware. Both agents independently discovered that reducing model depth and batch size allowed for more optimizer steps in a fixed time window, achieving a 98% memory reduction from 43.9GB to ~2.1GB while improving val_bpb from 1.82 to 1.22. This highlights the vast overhead in standard baselines and demonstrates how the NVIDIA DGX Spark's GB10 Blackwell architecture enables rapid, hardware-aware architectural optimization.