SIA implements a self-improving loop where a Meta agent coordinates with Target and Feedback agents to progressively enhance AI system performance on benchmark tasks. The framework simultaneously updates both the evaluation harness and model weights based on feedback signals. It reports significant gains across domains including legal reasoning (LawBench), GPU kernel optimization, and single-cell RNA denoising.