3. Results and Analysis
The CAS-6-enhanced model outperformed the baseline across all tasks, especially in capturing semantic resonance and distinguishing subtle metaphorical vs. idiomatic usage. Visualizations of the CAS-6 interaction graphs showed consistent clustering patterns for stable, high-weight expressions, aligning with human intuition.
Example qualitative outputs:
Input: "His apology was just crocodile tears."
Baseline: Literal
CAS-6: Metaphorical, weight +1.7, high stability
Input: "The river cried tears of history."
Baseline: Unclassified
CAS-6: Artistic, weight +2.0, stability medium
4. Implications
These preliminary results validate that CAS-6 modeling adds interpretative precision for expressions whose meanings are not linearly deducible from token frequency or word proximity alone. This experiment also demonstrates that semantic resonance and implicit weight---as operationalized in CAS-6---are not only theoretically grounded but also empirically learnable.
The success of this pilot experiment strengthens the case for CAS-6 as a plausible and scalable extension for current LLM fine-tuning pipelines. By embedding semiotic structures and emergent meaning dynamics into model learning, we move one step closer to an AI capable of true semantic insight, rather than mere pattern mimicry.
C. Integrating CAS-6 into a Semantic Resonance-Based Reinforcement Learning Loop
While supervised fine-tuning using annotated semiotic data offers an initial validation of the CAS-6 framework, scaling interpretative intelligence in LLMs requires models to learn, adjust, and generalize semantic complexity dynamically. This motivates the integration of CAS-6 into a reinforcement learning (RL) paradigm---not purely for optimizing token likelihood, but for cultivating deeper semantic resonance across interactions.
1. Motivation and Design Philosophy
Conventional RLHF (Reinforcement Learning from Human Feedback), such as used in ChatGPT and InstructGPT, optimizes reward based on alignment and safety metrics derived from scalar human preference ratings. However, such systems lack structured semantic feedback---they do not reward interpretative nuance, aesthetic coherence, or cultural resonance.
We propose a Semantic Resonance Reinforcement Learning Loop (SR-RLL) that augments the conventional policy gradient loop with CAS-6 feedback tensors, allowing the model to:
