1. Contextual Feedback Loops
Each interaction between words (or multi-word phrases) can create a semantic trail that affects future interpretations. As more words or ideas accumulate, certain meanings resonate more strongly, while others may fade or be suppressed.
2. Temporal Decay and Amplification
Interaction stability is dynamic and context-sensitive. Over time, some interactions become more stable, while others may experience semantic drift or attenuation depending on the cumulative narrative or conversation context.
3. Memory Persistence in LLMs
CAS-6 introduces memory slots into the neural architecture. These slots track the semantic state of key interactions across a discourse sequence, allowing the model to reference and adapt these states over time. This mechanism helps in creating longer, more coherent outputs that respect historical context and maintain semantic continuity.
C. Quantifying Stability: A Recursive Framework
To quantify interaction stability within the CAS-6 framework, we introduce a recursive stability function that adjusts over the course of text generation or interaction. This function is based on the cumulative effects of previous interactions:
Sij(t)=Sij(t1)+(1)f(resonance,persistence)S^{(t)} = \alpha \cdot S^{(t-1)} + (1 - \alpha) \cdot f(\text{resonance}, \text{persistence})
Where:
Sij(t)S^{(t)} represents the stability of the interaction between tokens ii and jj at time step tt.
Sij(t1)S^{(t-1)} is the stability at the previous time step.
\alpha is a weighting factor that determines the influence of previous states on current interactions.
This recursive formulation allows CAS-6 to adaptively build meaning based on prior context, leading to a more resilient interpretation of word interactions in long texts.
D. Applications in AI Semantics and Pragmatics
The introduction of interaction stability opens up a range of applications in AI language systems:
1. Long-Term Coherence
Models can maintain coherent themes and topics in extended narratives, making them better suited for applications like story generation, dialogue systems, and content summarization.
2. Contextual Adaptation
CAS-6 models can adapt meaning depending on the discourse history, allowing for more nuanced semantic shifts (e.g., irony, sarcasm) that may occur across time.
3. Cultural and Emotional Resonance
Stability allows for culturally resonant language to persist, such as the use of specific idioms, metaphors, or symbolic constructs, which can be crucial for building AI models that understand cultural context or emotional undertones.
4. Dynamic Memory Networks
A memory-based architecture enables models to reference past interactions and incorporate them into current decision-making processes, which improves predictive accuracy in tasks requiring long-term inference.
E. Practical Implementation
In practice, the memory-like mechanism of interaction stability can be implemented as a series of recurrent layers within the CAS-6 network. These layers would allow for semantic feedback based on a sliding window of prior interactions, essentially emulating short-term memory within a long-term discourse framework.
The model can also use attention mechanisms to give different levels of weight to previous interactions depending on their resonance and persistence, leading to more contextually-aware generation.
Conclusion
