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

arXiv cs.AI · 18 hours ago

Researchers introduced OPINE-World, an LLM-based agent that learns programmatic world models through interaction by coupling two cooperating agents that generate and test hypotheses about environment structure. The system achieved a score of 78.4 on the ARC-AGI-3 benchmark, solving 20 of 25 games without per-game training. This approach enables agents to efficiently learn reusable, data-efficient models of unfamiliar environments by synthesizing code rather than relying on deep networks that require extensive training data.