LL = Level of interaction (1 to n)
PP = Interaction pattern (sequence, topology)
WW = Weight (inhibitive synergistic)
SS = Probability (contextual frequency and fit)
TT = Stability (semantic resonance and memory persistence)
The function \Phi is non-linear, capable of producing multi-modal outputs depending on how the prior dimensions reinforce or inhibit one another. For instance, high interaction weight and stability with metaphor-compatible patterning (e.g., non-literal subject-verb-object structure) are more likely to yield metaphoric outputs.
C. Beyond Prediction: Towards Meaningful Generation
Traditional LLMs primarily optimize next-token prediction, often sacrificing interpretive depth for fluency or coherence. CAS-6 reframes output generation as a semantic construction task, in which:
Meaning is emergent, not pre-scripted.
Output is influenced by context history, stability, and interaction feedback.
Interpretation is multi-layered, allowing the same surface string to be interpreted literally or metaphorically depending on interactional state.
For example:
D. Cultural and Aesthetic Sensitivity
A major advantage of CAS-6 is its capacity to encode cultural semiotics, allowing the AI to differentiate idioms from literal phrases or understand the emotional gravity of figurative expressions in diverse cultural settings. This is crucial for global-scale AI that must navigate across linguistic ambiguity, poetic license, and symbolic narratives.
CAS-6 encourages the inclusion of:
Cross-cultural metaphor libraries
Emotionally valenced semantic anchors
Dynamic idiom recognition and interpretation models
This paves the way for applications such as:
Emotionally resonant storytelling
Idiomatic machine translation
Culturally-aware dialogue agents
E. Output Calibration and Evaluation
To evaluate the semantic quality of interaction outputs, we propose:
1. Multidimensional Scoring:
Literalness Score
Metaphoric Depth Score
Aesthetic Coherence
Contextual Fidelity
2. Human-AI Semantic Alignment Tasks:
Comparing AI interpretations with human semantic judgments across cultures.
3. Output Diversity and Interpretability Metrics:
Measuring how the same phrase adapts in different contexts with varying CAS-6 parameters.
Conclusion
