发表机构
School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen; Shenzhen Research Institute of Big Data (SRIBD)（香港中文大学（深圳）理工学院; 深圳大数据研究院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出因果-命运动力学，结合秀丽隐杆线虫模型、互联网路由研究及Transformer架构，探究未实现影响的未来相关性与传输实现机制。
AI 中文摘要
许多动力学系统产生的影响，其后果在产生时并未在已实现的轨迹中完全耗尽。这类后果常被视为不存在、延迟或静态存储，导致未实现的影响如何随系统演化保持未来相关性的问题尚不明确。本文提出因果-命运动力学，其中产生的影响可被实现、保持潜在状态或被后续动力学转化，当相关映射被指定时，给出精确的有限传输表示。首先，连接组约束的秀丽隐杆线虫（Caenorhabditis elegans）模型提出生物学假设：未解决的神经元间影响可能持续存在并促进后续传播；但该模型未在活体动物中证实此机制。接着，研究运营互联网路由，其中动态更新的跨观察者历史保留超出当前本地路由状态的预测信息。随后，利用该表示构建Transformer架构，其能显式传输并选择性实现潜在上下文影响，同时保留语言建模功能。三项研究分别区分了模型驱动的科学假设、与未来相关历史兼容的观测现象，以及通过后续计算承载未实现影响的可执行构造。
英文摘要
Many dynamical systems generate influences whose consequences are not fully exhausted in the realized trajectory at the moment they arise. Such consequences are often treated as absent, delayed or statically stored, leaving unclear how unrealized influence retains future relevance as the system evolves. Here we formulate causal-fate dynamics, in which generated influence may be realized, remain latent, or be transformed by subsequent dynamics, and give an exact finite-transport representation when the relevant maps are specified. A connectome-constrained Caenorhabditis elegans model first motivates the biological hypothesis that unresolved inter-neuronal influence may persist and contribute to later propagation; it does not establish such a mechanism in living animals. We next examine operational Internet routing, where a dynamically updated cross-observer history retains predictive information beyond the current local route state. We then use the representation to construct a Transformer architecture that explicitly transports and selectively realizes latent contextual influence while retaining language-modeling function. The three studies distinguish a model-motivated scientific hypothesis, an observational phenomenon compatible with future-relevant history and an executable construction for carrying unrealized influence through subsequent computation.
Comments39 pages (20-page main text and 19-page Supplementary Information), 6 figures, 14 supplementary tables. Code: https://github.com/Hotaru366/causal-fate-code
