Provide both branches with the same input prompts.
Collect model outputs for:
Phrase completion
Interpretation (instructed to "explain the meaning")
Contextual paraphrasing
Use human raters (blind to branch) to score:
Accuracy of interpretation
Depth of meaning (implicit/metaphoric detection)
Cultural appropriateness
Artistic/aesthetic quality
Additionally compute automatic metrics:
Semantic similarity (BERTScore, BLEURT)
Creativity/novelty (n-gram novelty)
CAS-6 variable traceability (if exposed)
D. Hypotheses
1. The CAS-6-augmented model will generate more semantically resonant and culturally grounded interpretations.
2. Outputs will show greater variation in connotative and artistic registers, indicating flexibility beyond literal prediction.
3. The CAS-6 injection will make model predictions more stable across paraphrased inputs and cross-cultural variations.
E. Analysis Plan
Statistical tests:
Mann--Whitney U test or t-test between Branch A and B human evaluation scores.
Correlation between CAS-6 weights and rated interpretive depth.
Qualitative analysis:
Case studies of key phrases illustrating success or failure in interpretive richness.
F. Reproducibility and Ethics
Datasets, annotations, and code will be open-sourced under CC BY-NC-SA.
Human annotators are compensated and instructed with clear guidelines to reduce bias.
No personally identifiable information is used.
Appendix II. Model Architecture Diagram
Overview
The architecture is based on a standard transformer decoder (e.g., GPT-style), with an auxiliary CAS-6 Interpretive Layer injected between the final transformer block and the language modeling head. This layer allows the model to modulate its output based not only on next-token probability but also on six semantically-rich interaction parameters.
A. CAS-6 Enhanced LLM Architecture
         INPUT TOKENS            Â
   ("crocodile", "tears", "eyes")        Â
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