Finally, Complex Adaptive Systems (CAS) theory---originating from the Santa Fe Institute (Holland, 1992; Mitchell, 2009)---has provided powerful models for nonlinear, self-organizing behavior in decentralized environments. CAS-based approaches in AI have been used to simulate biological processes, swarm intelligence, and socio-cognitive interactions.
In the context of language, CAS offers a promising scaffold to model how semantic coherence, divergence, stability, and novelty emerge from local interactions among linguistic agents. Our proposed CAS-6V model operationalizes six interrelated variables---interaction pattern, level, probability, weight, stability, and output---as a way to quantify and simulate semantic emergence in linguistic structures.
By situating meaning within a CAS formalism, we allow for the modeling of semantic instability (ambiguity, irony), phase transitions (metaphoric leaps), and attractors (conventionalized meaning), all of which are poorly captured in conventional LLMs.
2.3. The Gap: Absence of a Systemic Framework Linking Complex Word Interactions to Semantic Understanding in AI
Despite the significant progress in natural language processing (NLP), most state-of-the-art language models remain constrained by token-level statistical learning, optimized primarily for next-word prediction and syntactic coherence. While these models exhibit impressive fluency and context retention, they often fall short in understanding deep semantic relationships, cultural nuance, implicit meanings, and aesthetic dimensions of language.
As highlighted in previous subsections, various fields---from cognitive semantics and semiotics to complex systems and multi-agent theory---have proposed rich, conceptual mechanisms for meaning construction. However, there remains a fundamental methodological and architectural gap: the absence of a computational framework that systematically models the emergence of meaning through multi-level, dynamic word interactions.
Existing LLMs like GPT and BERT treat words and phrases as vectors in high-dimensional embedding spaces. Though these embeddings capture distributional similarity, they do not inherently model:
Hierarchical or recursive semantic emergence, especially in small but meaningful permutations (e.g., "crocodile tears" vs. "tears of crocodile").
Inter-word interaction dynamics that generate context-sensitive and culturally encoded meanings.
The role of semantic stability and phase transitions, as seen in metaphor, irony, or aesthetic interpretation.
Systemic modeling of word clusters as adaptive agents whose meanings shift through internal interaction patterns, akin to complex adaptive systems in biological or social phenomena.
More specifically, no current framework integrates the six core components essential for capturing semantic emergence:
1. Interaction Patterns: Structural configuration of word combinations.
2. Interaction Levels: Degree of complexity (unary, binary, ternary, etc.).
3. Interaction Probability: Likelihood of a particular configuration occurring in natural language or being selected by context.
4. Interaction Weight: Semantic strength or contribution of each element.
5. Interaction Stability: The consistency or volatility of meaning across contexts or cultural domains.
6. Interaction Output: The emergent, often non-linear meaning resulting from the interaction of linguistic elements.
This six-variable model---CAS-6V---has not yet been formalized or implemented in any major LLM or AI architecture. Moreover, the lack of a unified computational framework prevents current models from achieving interpretative richness and cognitive fidelity akin to human understanding of metaphor, irony, or culturally bound idioms.
Therefore, we identify a clear theoretical and technological gap in the literature: a need for a systemic, adaptive, and semantically expressive framework capable of representing the emergence of meaning as a product of multi-level, weighted, and unstable interactions among linguistic units.
In response, the next section presents CAS-6V: a novel framework inspired by complex adaptive systems theory, designed to extend current LLM capabilities toward a more interpretive, culturally sensitive, and artistically aware AI language understanding.
