SIA is a new open-source project that autonomously improves any model or agent against a benchmark. It works by looping three roles — a meta agent, a target agent, and a feedback agent — that together rewrite the system's scaffolding and weights.
Rather than relying on manual tuning, SIA closes the loop on self-improvement: the meta agent proposes changes, the target agent runs them, and the feedback agent evaluates results to guide the next iteration.
The approach points toward AI systems that can optimize themselves against a measurable objective, reducing the human effort traditionally required to squeeze better performance out of models and agents.
As an open-source release, SIA gives researchers and builders a concrete framework to experiment with autonomous, benchmark-driven self-improvement.