Implementing CAS-6 graphs as dynamic reasoning modules within symbolic execution engines.
Using differentiable CAS-6 matrices that can be trained end-to-end with gradient-based updates.
Linking CAS-based modules to external knowledge bases or ontologies, allowing the LLM to ground meanings culturally or historically.
4. Interfacing with Human Feedback Loops
Finally, a modular CAS-6 system enables human-in-the-loop training and correction, where annotators or end-users can directly adjust semantic weights or flag unstable interaction patterns. This aligns well with reinforcement learning from human feedback (RLHF) approaches and supports a more transparent and accountable path toward language understanding.
Summary of Future Modular Integration
The integration of CAS-6 into LLMs is not envisioned as a full-system replacement, but rather as a semantic enrichment module---one that can operate independently or in tandem with existing neural architectures. Through modular design, interaction-aware layers, and neuro-symbolic interfaces, CAS-6 has the potential to become a foundational tool for next-generation interpretive AI systems.
B. Human--AI Co-Interpretation Experiments
While the CAS-6 framework introduces a mathematically grounded approach to semantic modeling, its full potential lies in collaborative meaning construction between humans and machines. Future experimentation must therefore focus not only on computational performance but also on how effectively AI systems can co-construct, negotiate, and evolve meaning with human interlocutors---especially in contexts where language is ambiguous, metaphorical, or culturally nuanced.
1. Beyond Evaluation: Toward Co-Creation
Current evaluation benchmarks in NLP---such as BLEU, ROUGE, or even GPT-style preference models---tend to focus on accuracy, fluency, or syntactic alignment. However, semantic depth and interpretive nuance often evade such metrics. We propose a shift in methodology:
Instead of only evaluating AI's output post hoc, experiments should engage human subjects in real-time interpretation tasks, wherein both human and AI provide, revise, and refine meaning hypotheses based on CAS-6 output matrices.
Through interactive annotation platforms, humans can explore and manipulate semantic weights, resonance patterns, and interaction levels---thus creating a dialogical feedback loop that adapts both the model and the user's own interpretive expectations.
2. Experimental Setup: Interpreting Multi-Level Semantics
One potential design involves presenting subjects with n-gram expressions (e.g., "crocodile tears", "water eye", "eye of storm") and comparing:
The human-only interpretation,
The AI-only CAS-6 output, and
The hybrid co-interpretation, in which AI proposes semantic weights and interaction resonances, and humans validate or refine them.
Evaluation metrics could include:
