Researcher Hand-Rebuilds 16-Neuron Network, Beating Original Accuracy

Community researcher DragggonZ hand-rebuilt a 16-neuron network without training, achieving 96.0% accuracy versus the original's 95.3%, while ex-OpenAI researcher Jacob Hilton proposed noise-based tests to verify mechanistic faithfulness.

2026-10-18 ~ 2026-10-18 · 3 related posts

• Hand-built 16-neuron network without training hits 96.0% vs 95.3% for trained original — DragggonZ · 2026-10-18
• Hand-crafted 16-neuron net matches trained model (96.0% vs 95.3%), Hilton proposes faithfulness metrics — JacobHHilton · 2026-10-18
• Hilton specifies noise test: N(0, 0.0025²) weights typically cost 1-10 accuracy points — JacobHHilton · 2026-10-18