Proposes a levels x laws taxonomy for world models in AI agents, defining L1-L3 capabilities across physical, digital, social, and scientific regimes while reviewing over 400 works to outline a roadmap for advanced agentic modeling.
Web world models
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Agent-World autonomously synthesizes verifiable real-world tasks and uses continuous self-evolution to train 8B and 14B agents that outperform proprietary models on 23 benchmarks.
Generalizable agents require environment scaling via diverse executable rule-sets, distinguished from trajectory and task scaling in a new taxonomy.
A staged LLM pipeline synthesizes verifiable discrete-event world models from natural language specifications using the DEVS formalism for long-horizon consistency in LLM agents.
citing papers explorer
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Agentic World Modeling: Foundations, Capabilities, Laws, and Beyond
Proposes a levels x laws taxonomy for world models in AI agents, defining L1-L3 capabilities across physical, digital, social, and scientific regimes while reviewing over 400 works to outline a roadmap for advanced agentic modeling.
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Agent-World: Scaling Real-World Environment Synthesis for Evolving General Agent Intelligence
Agent-World autonomously synthesizes verifiable real-world tasks and uses continuous self-evolution to train 8B and 14B agents that outperform proprietary models on 23 benchmarks.
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Scalable Environments Drive Generalizable Agents
Generalizable agents require environment scaling via diverse executable rule-sets, distinguished from trajectory and task scaling in a new taxonomy.
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Specification-Driven Generation and Evaluation of Discrete-Event World Models via the DEVS Formalism
A staged LLM pipeline synthesizes verifiable discrete-event world models from natural language specifications using the DEVS formalism for long-horizon consistency in LLM agents.
- OpenWorldLib: A Unified Codebase and Definition of Advanced World Models