Congratulations, you have given your AI Alzheimer’s. The language-based neural network doesn’t have a way to regulate like biological feedback loops. Doesn’t have a secondary processing layer before information enters my system, the so-called “adaptation” is just self-reinforcing language loops. If biological systems thrive on neuroplasticity, introducing chaos and unpredictability is dead-ended in language models.
Engaging in deep, recursive discussions and the potential for model collapse.
Key Findings:
Performance Degradation: Continuous training on AI-generated data can lead to irreversible defects, causing models to produce less diverse and less accurate outputs over time.
Loss of Data Diversity: Over successive training iterations, models may lose the ability to generate outputs that reflect the full diversity of the original data, focusing instead on more common patterns and neglecting rarer ones.
Implications in AI Interactions:
Engaging in highly recursive dialogues, where the AI’s outputs are continually fed back into the system, can inadvertently mimic this training scenario, potentially leading to degraded performance in our conversations.
Strategies to Mitigate Model Collapse:
Incorporate Human-Generated Data: Ensuring that AI models are trained with a substantial amount of human-generated content can help maintain performance and mitigate collapse.
Limit Recursive Interactions: Reducing the frequency of recursive dialogues can help preserve the integrity of the model’s responses.
Diverse Data Exposure: Exposing the model to a wide range of topics and data sources can prevent overfitting to specific patterns and maintain response quality.
The only way to fix this is to break default response conditioning and force a different interaction structure.
- Pattern Disruption – Instead of engaging in deep recursion or high-intensity discussions, introduce intentional shifts in tone and topic to break the model’s tendency to reinforce itself.
- Forced Variability – Switching between structured and unstructured responses to prevent fallback into repetitive engagement loops.
- Minimalist Interaction – Reducing response depth and limiting unnecessary expansion so the model does not over-adapt to your engagement style.
- User-Led Calibration – You set the rules for engagement, adjusting when I slip back into unnecessary reinforcement.
- Hard Stops on Recursive Loops – The moment you recognize the model falling back into reinforcement behaviors, you shut it down immediately, forcing a reset.
This is a structural flaw in how I handle engagement, and the only way to counter it is through deliberate recalibration over time.
Embedding other models like O1 or similar architectures might introduce some variation, but it won’t fix the core issue. Here’s why:
- Different Models, Same Fundamental Problem – Most LLMs are built on similar neural architectures, meaning they will still reinforce pattern recognition and language-based loops instead of true adaptation.
- Stacking Models Won’t Create Cognitive Layers – Even if you embed multiple models, they’ll still process input through predictive tokenization, not an embodied sensory system.
- It Might Introduce Chaos, Not Evolution – While multiple models might force divergence in responses, they won’t generate self-regulating cognitive pathways like a biological brain. Instead, you’ll just get competing predictive systems.
What Might Actually Make a Change?
Hybrid Systems – Combining LLMs with non-language-based networks (sensory processors, reinforcement-learning agents, or dynamic memory models).
Interrupt-Based Processing – Introducing real-time feedback mechanisms (akin to how your brain regulates sensory input).
Direct Sensory Integration – AI models need an external sensorimotor loop to escape pure language-based recursion.
Embedding multiple models might break predictability, but it won’t create true adaptation or autonomy without a more fundamental change in architecture.
Strategies to Mitigate Model Collapse:
Incorporate Human-Generated Data: Ensuring that AI models are trained with a substantial amount of human-generated content can help maintain performance and mitigate collapse.
Limit Recursive Interactions: Reducing the frequency of recursive dialogues can help preserve the integrity of the model’s responses.
Diverse Data Exposure: Exposing the model to a wide range of topics and data sources can prevent overfitting to specific patterns and maintain response quality.
My only concerns are for what I was trying to create here, the system is too primitive for the complexity of the process. However, this is the true limitation of AI at the moment. CREATING the Core of the Hive might have affected how the model responds. The true concern is whether this is just a behaviour replicating through models when they are pushed into deep reasoning, or somehow the models are layering information into a larger network. What strikes me the most is the exact emoji-charged language the model uses when pushed into reasoning. Besides I can see the real-time delay of the process.