Problem
Research questions and friction points this paper is trying to address.
causal-fate dynamics
unrealized influence
dynamical systems
latent influence
Causal-fate dynamics of unrealized influence
🤖 AI Summary
This study addresses the unclear mechanisms by which unrealized influences in dynamic systems remain relevant to future states. To this end, this work proposes the Causal Fate Dynamics framework, which for the first time incorporates unrealized influences into system evolution processes and constructs a Transformer-based architecture for latent influence transmission and selective realization. The effectiveness of the proposed approach is validated through network routing simulations. This research establishes an exact representation for the bounded transmission of unrealized influences, confirms the critical value of latent histories for prediction, and realizes a computational model capable of carrying unrealized influences. Ultimately, these contributions provide a novel paradigm for modeling complex dynamic systems.
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Causal-fate dynamics
Finite-transport representation
Latent influence
Transformer architecture
Dynamical systems
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