Sense and reinforce stable, synergistic semantic configurations.
Penalize incoherent or semantically unstable outputs.
Optimize for meaning generation, not just token plausibility.
2. Architecture of SR-RLL
The reinforcement loop integrates with a transformer-based LLM fine-tuned with the following components:
The reward function is defined as:
R=W+S+1Level2R = \alpha \cdot W + \beta \cdot S + \gamma \cdot \mathbb{1}_{\text{Level} \geq 2}
Where:
WW = average Interaction Weight
SS = Interaction Stability (semantic resonance)
1Level2\mathbb{1}_{\text{Level} \geq 2} = reward boost for interpretative interactions beyond literal
Hyperparameters ,,\alpha, \beta, \gamma can be adjusted based on the domain (literary, conversational, educational, etc.).
3. Simulated Feedback and Bootstrapping
In early-stage prototyping, where rich human evaluation is costly, we propose bootstrapping semantic feedback via:
Contrastive scoring: comparing multiple generations for resonance and stability.
Heuristic CAS-6 scoring agents: rule-based approximators to simulate human-like semantic reward.
Synthetic prompts: using metaphor-rich, idiomatic, or artistically framed queries (e.g., "Write a poem with the soul of a river.")
These allow a model to progressively internalize semantic patterns beyond frequency or syntax, learning to compose meaning as interaction, not just as prediction.
4. Preliminary Hypotheses
We hypothesize that:
