Researchers introduced Memento 3 for model-based recursive self-improvement. It lets frozen AI agents maintain a natural-language rulebook as memory. The agent compiles the rulebook into code for prediction and planning. It refines both rulebook and code using prediction errors. Updates are accepted only when judged faithful and verified by replay. A single agent cleared all 25 ARC-AGI-3 public games. It achieved 100.0 mean Relative Human Action Efficiency. It used 44% of the human action count. In Atari Pong, a learned controller won 21:0 in each of three episodes. The underlying AI model remains unchanged throughout.

• 100.0 mean Relative Human Action Efficiency
• 44% of human action count
• 21:0 win record in Atari Pong
• 25 public games cleared