arXiv Machine Learning
Aligning Agentic World Models via Knowledgeable Experience Learning
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MineExplorer: Evaluating Open-World Exploration of MLLM Agents in Minecraft
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Code as Worlds: Agentic Discovery of Executable World Representations for Physical Reasoning
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Emergent Semantic Representations in World Models through Physical Interaction without Linguistic Supervision
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OPINE-World: Programmatic World Modeling with Ontology-error-Prioritized Interactive Exploration for ARC-AGI-3
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Hugging Face Trending Papers
Jul 1
OPINE-World: Programmatic World Modeling with Ontology-error-Prioritized Interactive Exploration
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.