Do Coding Agents Need Executable World Models, Simplification, and Verification to Solve ARC-AGI-3?

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Researchers used gpt-5 level models to dissect which components of their ARC-AGI-3 agent—world modeling, code simplification, or verification—actually drive performance on abstract reasoning tasks. The study confirms that executable world models and strict verification are required to solve the hardest spatial logic puzzles that previously stumped prior LLM generations. This moves us closer to a systematic blueprint for building agents that can 'think' through novel physical or logic problems rather than just predicting text.