Executable World Models for ARC-AGI-3: Agent Maintains and Refactors Python Model Before Acting

arXiv·medium signal

A coding-agent system for ARC-AGI-3 maintains an executable Python world model, verifies it against previous observations, and refactors toward simpler abstractions as a practical MDL-like simplicity bias before planning actions. Uses a scripted controller with predefined interfaces — no hand-coded game logic. Early results on a notoriously hard benchmark, demonstrating that explicit world-model maintenance improves agent planning.