AINeutralarXiv – CS AI · 5h ago6/10
🧠
The Quantization Trap: Breaking Linear Scaling Laws in Multi-Hop Reasoning
Researchers demonstrate that quantization—reducing AI model precision to improve efficiency—paradoxically increases energy consumption and degrades reasoning accuracy in multi-hop reasoning tasks, contradicting established neural scaling laws. The study identifies hardware dequantization overhead as a critical bottleneck and proposes a Critical Model Scale metric to predict when quantization becomes counterproductive across different model sizes and hardware configurations.