aiqbench analysis

This paper introduces MASS, a framework enabling large language models to recursively improve themselves via multi-agent self-supervision without external human feedback. The approach demonstrates how autonomous agent teams can collaboratively refine model performance through iterative internal critique and generation cycles. This represents a significant step toward scalable, unsupervised alignment and capability expansion in foundational models.