D. Multi-Agent CAS-6 Simulation: A Preview
We also tested these combinations in a simulated multi-agent environment, where separate agents represented:
Literalizer (focuses on dictionary definitions),
Poeticizer (optimizes for aesthetic and metaphor),
Idiom Recognizer (specializes in cultural references),
Context Tracker (maintains situational awareness),
Critic (rates coherence and emotional effect).
The phrase "air mata buaya" triggered competition between the Literalizer ("tears of crocodile") and Idiom Recognizer ("feigned emotion"), with the Critic validating the idiomatic meaning as dominant based on contextual memory and emotional resonance.
This use case illustrates how CAS-6 operationalizes complex semantic phenomena beyond literal comprehension. It enables an AI to move fluidly across semantic layers, interpret figurative and artistic expressions, and distinguish idiomatic usage from noise or incoherence. These capabilities are essential for creating AI systems that can genuinely understand and generate human language in its full richness.
E. Narrative Explanation: Meaning Beyond Frequency --- Toward Semantic Stability and Implicit Weight
Traditional statistical language models---particularly those based on frequency-driven co-occurrence such as Word2Vec, GloVe, or Transformer-based architectures like BERT and GPT---rely heavily on distributional statistics to approximate meaning. The core premise, derived from the Distributional Hypothesis ("You shall know a word by the company it keeps"), assumes that frequent neighboring patterns encode semantic relationships. However, this assumption reveals critical limitations when confronted with:
Low-frequency yet high-impact expressions (e.g., idioms, metaphors, poetic constructions),
Culturally encoded nuances, and
Emotionally charged phrasing with minimal statistical reinforcement.
In contrast, the CAS-6 framework introduces a fundamentally different conceptual axis: semantic meaning as an emergent property not only of frequency but of interactional stability and implicit interpretive weight.
1. Semantic Stability as Meaning Attractor
We define semantic stability as the degree to which a combination of words tends to settle into a coherent interpretation across varying contexts and cognitive evaluations. Unlike statistical frequency, which may privilege overused but semantically shallow phrases, stability accounts for the resonance a phrase carries---culturally, emotionally, and cognitively.
For instance, "air mata" (tears) is both frequent and semantically stable, aligning denotative clarity with emotional salience.
In contrast, "air buaya" (water + crocodile) is rare, semantically unstable, and lacks interpretive gravity---it dissipates quickly unless placed in an artificial or poetic context.
In this paradigm, semantic resonance becomes akin to an attractor state in a dynamic system: once entered, interpretive agents (whether human or artificial) tend to converge rapidly and reliably toward meaning, regardless of syntactic permutations.
2. Implicit Weight as Latent Cultural and Cognitive Charge
