No Accidental Software Agent First Canonical Code for Human Code Entropy Reduction and 30 to 500 times Lower Frontier Model Requirements

The article presents a novel approach called agent-first canonical code aimed at reducing human code entropy in software repositories, which may unintentionally include extraneous information. This method utilizes a proof-carrying substrate to transform routine software into structured profiles, potentially achieving up to 100-fold reductions in verification costs, although these figures are currently theoretical. Preliminary experiments with the Qwen2.5-Coder-14B model indicate the potential for learning from 64,088 canonical trajectories, yet they do not confirm behavior preservation or cost-effectiveness in verified changes, highlighting the need for further validation in practical applications.