Huawei and UCL Introduce Memento 3, Reporting Success Across 25 Public ARC-AGI-3 Games

Huawei’s Noah’s Ark Lab, UCL, and collaborators introduced Memento 3, an AI agent they report cleared all 25 public ARC-AGI-3 games and their 183 levels without updating model weights or using game-specific fine-tuning. The team reported that it used 7518 actions, about 56% fewer than humans, and achieved a 100 RHAE score. Built on Claude Opus 5, Memento 3 iteratively revises a Markdown rulebook and an executable Python world model. Its improvements come through those revisions rather than changes to the underlying model’s weights. In a separate Atari Pong experiment, the researchers reported that its learned code-based strategy won three games 21:0 without further calls to the language model. The experiments primarily cover games with relatively clear rules. Claude Opus 5 ran at Extra-High reasoning intensity, and exploration, code generation, and verification still required substantial tokens and computing resources. Whether the approach works in more complex, changing real-world environments remains untested.

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