OPINE-World: Programmatic World Modeling with Ontology-error-Prioritized Interactive Exploration for ARC-AGI-3

Learning a useful world model from minimal interaction is central to building agents that adapt to unfamiliar tasks. Programmatic world modeling approaches such as WorldCoder quickly learn transition models when given pre-supplied symbolic representations; however, such approaches are insufficient when the symbolic representation and goal are unknown and continually changing, and are intractable under large action spaces due to rigid planning heuristics. Extending the programmatic world modeling approach, OPINE-World additionally maintains an abstracted representation in which provisional objects, incomplete causal explanations, and unresolved interactions can exist in a Bayesian, exploration-centric hypothesis space. This enables learning an unknown, dynamically evolving state, transition, and goal. Exploration and program revision share this evolving abstraction, allowing new evidence to revise both the hypothesized mechanics and their implementation in tandem at test time. ARC-AGI-3 presents this open-world problem as a general-intelligence learning benchmark. Through our OPINE-world model learning paradigm, we improve the Relative Human Action Efficiency score of Claude Opus 4.8 high from 1.5% to 78.40% on the ARC-AGI-3 benchmark.