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arXiv Machine Learning By Yiwei Liu, Luwei Yang, Shunbo Lei

Causal-fate dynamics of unrealized influence

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The paper introduces causal‑fate dynamics, a framework describing how influence generated in a system can be realized, remain latent, or be transformed by later dynamics. It presents an exact finite‑transport representation for these processes and applies the concept to three domains: a connectome‑constrained model of *Caenorhabditis elegans* suggesting unresolved neuronal influence may persist, an analysis of Internet routing showing that cross‑observer history retains predictive power beyond local state, and a Transformer architecture that transports and selectively realizes latent contextual influence while preserving language‑modeling performance. These studies collectively illustrate how unrealized influence can be modeled, observed, and harnessed in computational systems.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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