For instance, the phrase "crocodile tears" is not simply a collocation of two frequent tokens; it invokes a culturally entrenched conceptual metaphor for insincerity, rooted in narratives of deceptive behavior. Current LLMs are unable to natively represent or reason over such conceptual blends without extensive fine-tuning or prompt engineering.
Our proposed CAS-6V framework draws inspiration from this tradition by treating words not as atomic tokens but as interactive cognitive agents, whose combinatorial arrangements (e.g., "tears", "eyes", "crocodile") trigger shifts in meaning based on internal semantic dynamics, cultural mappings, and aesthetic tensions.
2.2.2 Emergent Semantics in Multi-Agent Systems
In parallel, research in multi-agent systems (MAS) has explored how semantics can emerge from distributed interactions among autonomous agents (Steels, 2005; Luc Steels & Kaplan, 2002). These systems simulate the co-evolution of communication conventions, showing how meaning can arise dynamically from negotiation, reinforcement, and feedback mechanisms.
Such emergent semantics offer a critical alternative to top-down statistical learning: instead of predefining lexical semantics, agents construct meaning through situated interaction and adaptive coordination. This principle directly informs our framework's treatment of words as dynamic semantic entities interacting across layered levels (e.g., pairwise, triadic), producing nonlinear meaning outputs.
While most MAS studies focus on low-level symbol grounding (e.g., object naming in robots), we extend this idea to semantic emergence at higher abstraction levels---such as metaphor, irony, and cultural idioms---by embedding agent interactions within a complex adaptive system (CAS) formalism.
2.2.3 Semiotics and Interpretive AI
Semiotics, particularly Peircean and structuralist traditions, frames meaning as a triadic relation among sign (representamen), object, and interpretant. Recent work in AI (Chandler, 2007; Kittler, 2010) has begun to explore how symbolic systems can be interpreted algorithmically, moving from statistical encoding to sign-based modeling.
Interpretive AI approaches argue for models that are meaning-aware, i.e., capable of not just generating grammatical output but also discerning contextual relevance, symbolic implication, and aesthetic effect. This aligns with artistic AI, affective computing, and AI-driven literature analysis.
CAS-6V positions itself as a semiotic-inspired system, where permutations of signs (e.g., "eyes", "tears", "crocodile") act as signals whose meaning shifts depending on their interaction topology. This provides a path to interpretative dynamism, in contrast to static token-to-token probability estimates.
2.2.4 Complex Adaptive Systems in AI
