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This paper introduces a verb-based paradigm, together with precise definitions of \\emph{timing computation} and \\emph{causal factum}, that enables AI to function as an instrument for spontaneously constructing a causal-reasoning world model.\n  Applied to longitudinal EHR data from 3,276 breast cancer patients, the framework empirically demonstrates: (1) automatic discovery of clinically significant patient trajectories, and (2) counterfactual timing deduction, that is, a \\emph{What-If Machine}. Both results are achieved in a purely data-driven manner, without recourse to prior domain knowledge, and represent, to our knowledge, the first such demonstrations in the machine learning literature.","title":"To Use AI as Dice of Possibilities with Timing Computation","url":"https://arxiv.org/abs/2605.01134","vendor":"arxiv_cs_ai"},"summary":"arXiv:2605.01134v3 Announce Type: replace \nAbstract: The dominant noun-based modeling paradigm has fundamentally constrained AI development, precluding any adequate representation of the future as an open temporal dimension. 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