Causal fate dynamics track influence beyond immediate effects
Causal-fate dynamics of unrealized influence
Machine Learning
Summary
Sometimes, actions or events in complex systems have effects that don’t show up right away, but still shape what happens later. The authors propose a new way to think about and model these delayed or hidden influences, calling it causal-fate dynamics. They test their ideas in three areas: a worm neural network model, Internet routing data, and a new design for a language model that keeps track of past hidden influences. This helps explain how information from the past can still matter as systems change and evolve.
What this means in practice
- •For network schedulers: Improve routing predictions by retaining historical influence beyond current route data using causal-fate dynamics.
- •For natural language engineers: Build language models that manage latent contextual information explicitly to enhance understanding and generation.$Commercial implications: Enables advanced Transformer-based language models with better context handling, improving AI applications for companies developing conversational agents and text tools.
Authors
Yiwei Liu, Luwei Yang, Shunbo Lei
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.