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.
arXiv preprint arXiv:2502.11881 , year=
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Improvements in LLM Theory of Mind on static benchmarks do not reliably improve performance in dynamic, first-person human-AI interactions across goal-oriented and experience-oriented tasks.
Observational analysis of Brazilian YouTube climate content identifies psychological engagement traits and explores their use in generative AI campaigns, accompanied by a public dataset of 226K videos and 2.7M comments.
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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Does Theory of Mind Improvement Really Benefit Human-AI Interactions? Empirical Findings from Interactive Evaluations
Improvements in LLM Theory of Mind on static benchmarks do not reliably improve performance in dynamic, first-person human-AI interactions across goal-oriented and experience-oriented tasks.
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Characterizing AI Manipulation Risks in Brazilian YouTube Climate Discourse
Observational analysis of Brazilian YouTube climate content identifies psychological engagement traits and explores their use in generative AI campaigns, accompanied by a public dataset of 226K videos and 2.7M comments.