In CAS-6, Interaction Weight ranges from -2 (inhibitory) to +2 (synergistic) and encodes the latent force of a word pair or phrase to generate meaningful interpretation.
High implicit weight indicates deep cultural embedding (e.g., "air mata buaya" feigned emotion).
Zero or negative weight often marks syntactic artifacts, literal collisions, or semantic dissonance (e.g., "buaya mata").
This weight is not learned purely by co-occurrence but may arise from:
Narrative traditions (e.g., mythological metaphors),
Emotive coding (e.g., "motherland," "blood debt"),
Historical usage in high-impact contexts (e.g., national anthems, sacred texts).
Thus, a phrase's interpretive power may be disproportionately high compared to its corpus frequency---a mismatch that purely probabilistic models routinely miss.
3. Rebalancing the Semantic Equation
The CAS-6 framework proposes that meaning (M) be reframed as a nonlinear function of not only frequency (F), but also interactional pattern (P), stability (S), and implicit weight (W):
M=f(F,P,S,W)M = f(F, P, S, W)
Where:
FF: statistical frequency in training data,
PP: structure of interaction (order, topological pattern),
SS: semantic resonance and contextual persistence,
WW: implicit weight from cultural, emotional, and idiomatic salience.
This formulation allows systems to prioritize low-frequency, high-stability phrases (e.g., "crocodile tears") over high-frequency, low-salience combinations (e.g., "the water was") in contexts that demand interpretation, not just prediction.
4. Practical Implication in Narrative Contexts
In narrative generation and interpretation, such as storytelling, poetry, or satire, the most powerful meanings often emerge from rare, metaphor-laden constructions. These are precisely the outputs where LLMs currently struggle. CAS-6's ability to compute interactional stability and implicit weight gives such systems a mechanism for:
Elevating rare but rich phrases into semantic focus,
Disambiguating metaphorical from literal usage,
Identifying idiomatic cohesion even with minimal training data.
For example, given the phrase:
