Executable World Models for ARC-AGI-3 in the Era of Coding Agents

1. 1Build an executable Python world model using a scripted controller
2. 2Verify the model against previous observations using verifier programs
3. 3Refactor the model toward simpler abstractions using an MDL-like simplicity bias
4. 4Plan through the model before acting using a plan executor
5. 5Test and evaluate the system's performance in various scenarios

AI researchers and engineers working on AGI systems can benefit from this approach to improve their agents' decision-making and planning capabilities

💡 Executable world models can improve AGI agents' planning and decision-making capabilities by providing a simpler and more abstract representation of the environment

🤖 Build executable world models for coding agents using Python! 💻