CAS-6-enhanced RL agents will develop stronger generalization in low-frequency figurative expressions.
The model will exhibit greater resilience to adversarial prompts that exploit denotative ambiguity.
Over time, CAS-6 RL will emerge with latent structures that resemble aspects of human conceptual blending (e.g., Fauconnier & Turner's theory).
5. Long-Term Vision
Integrating CAS-6 into an RL framework opens a pathway to self-refining LLMs that go beyond mimicry, and towards meaning generation as a systemically guided process. This represents a shift from token-by-token imitation to interaction-aware cognition, situating AI language models closer to human-like semantic intuition.
D. Potential for Enhancing Culturally Grounded Interpretations
One of the critical limitations of current large language models (LLMs) lies in their cultural flattening---a tendency to average out or marginalize localized meanings, idiomatic expressions, and aesthetic conventions that do not appear frequently or uniformly in global datasets. As a result, many culturally rich expressions become either misinterpreted or stripped of their nuance.
The CAS-6 framework, with its emphasis on interactional weight, stability, and level, provides a mechanistic pathway to retain and amplify cultural-linguistic specificity, offering a structured means to enhance LLMs' interpretative depth in localized or underrepresented contexts.
1. Beyond Surface Translation: Modeling Deep Cultural Semantics
Unlike statistical machine translation or token-based alignment systems, CAS-6 allows a model to understand why and how certain word combinations resonate within a cultural frame. For example:
The English expression "crocodile tears" maps not only to a literal image, but carries implied deception, a meaning culturally recognized and reinforced through literature and social discourse.
In Indonesian, "air mata buaya" carries the same implication, yet gains additional connotative weight when paired with local idioms like "buaya darat"---a term that adds another dimension of gendered moral critique.
By modeling these combinations with explicit interaction weights (e.g., synergistic versus ironic), probabilistic divergence (how expected or rare the phrase is), and semantic stability (how persistently the interpretation is reinforced in context), CAS-6 enables LLMs to internalize meaning as an emergent property of interaction, not just frequency.
2. Leveraging CAS-6 for Cross-Cultural Interpretive Learning
Incorporating CAS-6 into fine-tuning or reinforcement learning loops (as described in 5.C) also enables multi-local learning, where culturally situated interactions are given differentiated rewards based on:
Interpretative alignment: Does the model preserve the metaphor, idiom, or irony of the original language?
Semantic resonance: Is the output coherent with the cultural register (e.g., formality, tradition, spiritual belief)?
Adaptive fluency: Can the model switch between literal and poetic modes appropriately in multilingual or code-switched contexts?
This opens the door for a contextual grounding mechanism in multilingual models, making them more responsive to moral, aesthetic, and rhetorical patterns unique to specific linguistic communities.
