OPINE-World: Programmatic World Modeling with Ontology-error-Prioritized Interactive Exploration
Researchers have introduced OPINE-World, a breakthrough LLM agent that learns an object-centric programmatic world model online through interaction. By guiding exploration with a novel 'ontology error' metric, it overcomes the data-hungry nature of traditional deep networks and achieves high efficiency on the ARC-AGI-3 benchmark.
Computer Science > Artificial Intelligence
Title:OPINE-World: Programmatic World Modeling with Ontology-error-Prioritized Interactive Exploration
View PDF HTML (experimental)Abstract:Learning how an environment behaves from interaction is central to building agents that adapt to unfamiliar tasks. World models learned with deep networks are flexible but data-hungry and transfer poorly beyond their training distribution. Program-synthesized world models, written as source code by LLMs and refined through counterexample-guided inductive synthesis (CEGIS), are instead data-efficient and reusable, yet they have been demonstrated mainly on structured-state worlds with a given object vocabulary, and a single program search does not scale to pixel-rendered environments whose object structure must be hypothesized flexibly. We introduce OPINE-World, an LLM agent that learns an object-centric programmatic world model online from interaction. OPINE-World couples two cooperating agents in a loop of hypothesis and test, one acting in the environment and one synthesizing the model in code with replay verification and model-based planning, and it steers exploration with a Bayesian measure of object-type adequacy we call ontology error. We evaluate OPINE-World on ARC-AGI-3, a benchmark for skill-acquisition efficiency in which the object vocabulary, the goal, and the action semantics are withheld. OPINE-World solves 20 of 25 games without per-game training and reaches an action-efficiency score of 78.4 against the human baseline.
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Source: arXiv cs.AI Recent
