EvoA — Autonomous Model Iteration
Evidence-driven experiments, validation gates, and reusable model-improvement memory.
EvoA closes the loop from model failure diagnosis to experiments and reusable lessons. I led the design and delivery around existing training infrastructure.
- Diagnose → hypothesize → pilot → validate → reflect connects agent judgment with reproducible tool execution.
- Pilot experiments and validation gates select promising directions before full training.
- Experiment memory records outcomes and failure modes to inform later iterations.
- Candidate preparation is separated from controlled production rollout.
In a controlled online experiment, the selected model reduced the human audit rate by approximately 4.6% relative. This is a measured outcome for that deployment, rather than a general guarantee of model quality or training speed.