Toward Interpretative Language Model: a CAS Framework with Six Interaction Variables to Capture Implicit Meaning

              LANGUAGE MODELING HEAD (Softmax)        

                         

                         

                     OUTPUT TOKEN                    

        ("That's crocodile tears, not sorrow.")      

B. Notes on CAS-6 Interpretive Layer Functionality

1. Embedding Injection
CAS-6 variables are embedded into a continuous vector and fused with transformer hidden states via:
Attention biasing (e.g., additional keys/values)
Cross-attention from latent CAS-6 nodes
Residual modulation (e.g., FiLM-style gating)
2. Training Objective
Dual-loss:
Standard cross-entropy for next-token prediction.
Auxiliary loss for semantic alignment: either contrastive (against wrong interpretations) or alignment (with human-annotated CAS-6 representations).
3. Output Conditioning
 CAS-6 Output Type can condition decoder outputs:
e.g., shift toward poetic/metaphoric phrasing when Output = "artistic"
4. Cross-Cultural Adaptability
The layer supports cultural embeddings (region, language, idiom base) to modulate meaning toward culturally appropriate interpretations.
C. Scalability & Compatibility

Plug-and-play for pre-trained transformer models.
Can be trained from scratch or fine-tuned as an adapter module.
Modular: CAS-6 layer can be turned off to revert to baseline probabilistic inference.

Appendix III. Annotated Dataset Scheme

To enable training and evaluation of the CAS-6-enhanced language model, we propose a semiotically enriched, multi-layered dataset with annotations specifically tailored to represent the six CAS-6 dimensions. This appendix outlines the annotation schema and structural organization of the dataset.

A. Dataset Structure