Constructional approaches to language have evolved from rigid tree-based representations to framing constructions as flexible, multidimensional pairings of form and function. However, it remains unclear how to formalize this conceptual shift in ways that are both computationally scalable and scientifically insightful. This paper proposes dynamic tensegrity – a term derived from “tensile integrity” – as a novel architecture metaphor for modelling linguistic form. It argues that linguistic structure emerges from dynamically evolving networks of constraint-based tensions rather than fixed hierarchies. The paper explores the theoretical consequences of this view, supplemented with a proof-of-concept implementation in Fluid Construction Grammar, demonstrating how a tensegrity-inspired approach can support robustness and adaptivity in language processing.

Human languages have multiple strategies that allow us to discriminate objects in a vast variety of contexts. Colours have been extensively studied from this point of view. In particular, previous research in artificial language evolution has shown how artificial languages may emerge based on specific strategies to distinguish colours. Still, it has not been shown how several strategies of diverse complexity can be autonomously managed by artificial agents . We propose an intrinsic motivation system that allows agents in a population to create a shared artificial language and progressively increase its expressive power. Our results show that with such a system agents successfully regulate their language development, which indicates a relation between population size and consistency in the emergent communicative systems.