A Frozen 12B Beats Frontier Models on Verified Work: 100% Accuracy, 0 Tokens, Bit-Exact, Forever

Researchers have developed a method to improve language model performance by maintaining a side-database of verified solutions rather than retraining the model itself. This approach keeps the underlying model frozen, allowing it to retrieve exact, pre-validated answers instead of generating new tokens. By bypassing the non-deterministic nature of standard inference, the technique ensures consistent accuracy for repetitive tasks while significantly reducing the computational costs typically associated with model updates.