Embedding layers (via CAS-weighted vectors),
Attention mechanisms (biasing toward semantically stable paths),
Fine-tuning loops (guided by interaction-level constraints and rewards).
Thus, CAS-6 enhances existing architectures with symbolic interpretability, cultural flexibility, and emergent depth, without sacrificing the scalability and efficiency of current transformer-based models.
In sum, CAS-6 offers a principled framework that marries the strengths of statistical language modeling with the structural and emergent principles of human meaning-making. It not only advances the interpretive sophistication of AI but also opens new avenues for interdisciplinary convergence between AI, linguistics, cognitive science, and the arts.
B. Challenges and Open Questions: From Numerical Representation to Evaluating Implicit Meaning
While the CAS-6 framework introduces a promising direction for enriching semantic capacity in large language models, its full realization faces several technical, theoretical, and methodological challenges. This section outlines key obstacles in implementing and evaluating the system, particularly regarding the representation of semantic weight and interaction stability, as well as the objective measurement of implicit meaning.
1. Representing Semantic Weight and Stability in a Numerical System
Two of the six CAS-6 variables---Interaction Weight and Interaction Stability---require translating abstract semantic phenomena into numerical or tensor-based representations. This translation is non-trivial, for several reasons:
Semantic weight (ranging from --2 to +2) attempts to capture inhibitory or synergistic relationships between word-pairs or higher-order constructs. Unlike probabilities, which are empirically derived from frequency, these weights must be inferred from contextual meaning alignment, which is often subtle, context-dependent, and culturally contingent.
Interaction stability, defined as the degree to which a phrase or multi-word expression maintains consistent interpretive resonance across contexts, mirrors constructs like semantic entrenchment or resonance in cognitive linguistics. Capturing this stability computationally may require recurrent exposure modeling, persistent memory layers, or the use of symbolic knowledge graphs alongside deep learning representations.
Designing these representations without oversimplifying or reducing their complexity into mere token-level embeddings remains a central open question.
2. Objectively Evaluating Implicit and Artistic Meaning
Perhaps the most profound challenge for CAS-6 is evaluation: how can we verify that an AI model has correctly understood an implicit, metaphorical, or aesthetic meaning?
Traditional metrics such as:
Perplexity,
BLEU score,
ROUGE or accuracy in next-token prediction,
are insufficient for capturing performance in artistic or connotative domains. Instead, CAS-6 demands new kinds of evaluation protocols, possibly drawing from:
