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

• URL: http://arxiv.org/abs/2605.05138v1
• Date: Wed, 06 May 2026 17:12:36 GMT
• Title: Executable World Models for ARC-AGI-3 in the Era of Coding Agents
• Abstract summary: We evaluate an initial coding-agent system for ARC-AGI-3.<n>The system uses a scripted controller, predefined world-model interfaces, verifier programs, and a plan executor.<n>Results provide preliminary evidence that verifier-driven executable world models are a promising approach for ARC-AGI-3 agents.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We evaluate an initial coding-agent system for ARC-AGI-3 in which the agent maintains an executable Python world model, verifies it against previous observations, refactors it toward simpler abstractions as a practical proxy for an MDL-like simplicity bias, and plans through the model before acting. The system is intentionally direct: it uses a scripted controller, predefined world-model interfaces, verifier programs, and a plan executor, but no hand-coded game-specific logic. We report results on the 25 public ARC-AGI-3 games. Each recorded playthrough uses a fresh agent instance with no access to previous playthrough-specific files or conversation state. Most games have a single recorded playthrough; for a few games, we report multiple independent fresh-agent playthroughs to expose run-to-run variability. The agent fully solved 7 games, achieved a Relative Human Action Efficiency greater than 75%, on 6 games, and obtained a mean per-game RHAE of 32.58%. Because the system uses no game-specific code, it can serve as a game-general baseline for ARC-AGI-3. Performance on the private validation set remains to be tested. Overall, the results provide preliminary evidence that verifier-driven executable world models are a promising approach for ARC-AGI-3 agents.

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