"He wept crocodile tears while holding the deed to her family's land."
A traditional LLM may treat "crocodile" and "tears" independently or miss the idiom's emotional sarcasm. A CAS-6-enhanced system, recognizing the high-weight + high-stability idiomatic construct, would foreground the metaphor, inferring insincerity, and correctly modulate downstream emotional or rhetorical interpretation.
By integrating semantic stability and implicit interpretive weight into the comprehension pipeline, CAS-6 surpasses frequency-centric paradigms and moves closer to human-like interpretation. Meaning is shown to be not just a matter of occurrence, but of resonance, culture, and cognitive synergy. This shift is essential for advancing LLMs from statistical language generators to semantic interpreters and co-creators of meaning
5. Proof-of-Concept: Toward CAS-6-Enhanced LLMs
A. Integration of a CAS-6-Based Semantic Layer in LLM Fine-Tuning
To bridge the current limitations in Large Language Models (LLMs) regarding deep semantic understanding, we propose the integration of a CAS-6-informed semantic layer into the existing architecture, specifically during the fine-tuning phase. This layer operates orthogonally to traditional transformer attention mechanisms and augments them by modeling interactional dynamics among lexical units, informed by the six CAS-6 dimensions:
Interaction Level (L)
Interaction Pattern (P)
Interaction Probability (Pr)
Interaction Weight (W)
Interaction Stability (S)
Interaction Output Type (O)
1. Semantic Layer Architecture
The proposed semantic layer functions as a dynamic, context-sensitive graph engine embedded post-attention or mid-layer in a transformer-based model. Each token sequence input is processed in parallel by two tracks:
Traditional Self-Attention Track: Captures syntactic and statistical dependencies.
CAS-6 Semantic Track: Constructs an Interaction Graph (IG) wherein each node represents a token, and each edge encodes a CAS-6 interaction with weighted parameters.
Semantic Graph Construction Process:
For an input sequence T=[w1,w2,...,wn]T = [w_1, w_2, ..., w_n], the CAS-6 graph G=(V,E)G = (V, E) is built such that:
V={w1,w2,...,wn}V = \{w_1, w_2, ..., w_n\}
E={(wi,wj,fCAS6(wi,wj))}E = \{(w_i, w_j, f_{CAS6}(w_i, w_j))\}
Where fCAS6f_{CAS6} computes a vector embedding of the CAS-6 attributes for the pair (wi,wj)(w_i, w_j), such as:
