aiqbench analysis

This article outlines a theoretical framework where large language models utilize collective intelligence to iteratively refine their own architectures and capabilities. The core concept involves agents collaborating to generate code, debug errors, and propose architectural changes that lead to subsequent generations of more advanced systems. This represents a significant shift from static model training toward dynamic, self-evolving AI ecosystems capable of autonomous progress.