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This study synthesizes adjacent research and introduces Enactive AI as a conceptual framework for enterprise and industry reasoning, site-level decision support, and execution feedback
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https://arxiv.org/list/cs.AI/new
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https://arxiv.org/list/cs.CL/new
3% coverage), and a visual tablet renderer, packaged as a pip-installable toolkit with CLI and web demo
https://arxiv.org/list/cs.CV/new
Classical world models instantiate this proxy primarily through future physical-state prediction, a formulation useful yet narrow for agents that require actionable feedback beyond raw state transitions
https://arxiv.org/pdf/2608.03086
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