Ultimately, CAS-6 moves toward an AI that not only generates language but understands its resonance---bridging the gap between computational representation and human meaning.
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Appendix I. Experiment Protocol
A. Objective
To evaluate the effectiveness of the CAS-6 framework in enhancing the interpretive capability of a language model in understanding multi-level semantic structures---including denotative, connotative, idiomatic, and artistic meaning---beyond probabilistic co-occurrence.
B. Materials and Tools
1. Base Language Model:
GPT-style transformer (e.g., GPT-2 or GPT-NeoX) for controlled fine-tuning.
2. Dataset:
A curated corpus of polysemantic phrases and idioms from multiple cultural-linguistic contexts (e.g., English, Indonesian, Hindi).
Examples include:
Tears of a crocodile, eyes of the storm, blood is thicker than water, etc.
Their paraphrases, literal translations, and metaphorical variants.
3. Annotation Interface:
Human evaluators label interpretations on a 5-point scale of denotative artistic meaning.
Variables logged: perception of semantic coherence, cultural appropriateness, poetic value.
4. CAS-6 Variable Embedding Module:
Engineered layer that injects CAS-6 dimensions during training:
Interaction Level
Pattern (permutation/ordering/topology)
Probability (co-occurrence baseline)
Weight (annotated or inferred)
Stability (resonance across samples or model outputs)
Output (generated interpretation)
C. Procedures
1. Data Preparation
Select 100--500 phrase triplets involving 3 base lexical items (e.g., tears, eyes, crocodile).
Construct all permutations up to Level-3 interaction.
Annotate each permutation with:
Conventional semantic type (denotative/connotative/metaphorical/artistic)
Contextual polarity and cultural markers (optional)
2. Model Fine-Tuning
Fine-tune baseline LLM on the annotated dataset in two branches:
Branch A (Baseline): Standard LLM fine-tuning (no CAS-6 signals)
Branch B (CAS-6 Augmented): Fine-tuning with CAS-6 vector injected into hidden representations or output conditioning.
3. Evaluation Protocol
