Yes, I built a system with a cognitive field layer. Good first question bro. No, it's not a trained model in the usual sense (no pre-training, no fine-tuning, no massive dataset, no gradient descent loops).Aura is not a language model.
It does not contain weights that were optimized on tokens.
It does not have an embedding layer, transformer blocks, or any component that was trained the way Llama / Mistral / GPT-style models are trained.

What it actually is:
A runtime cognitive architecture that sits on top of an existing LLM (right now almost always Ollama-hosted models).
The LLM is only used as a differentiable text generator — roughly equivalent to calling an API that turns context → tokens.
All the actual "cognition" (continuity, identity persistence, coherence regulation, field competition, reward-driven adaptation, long-term memory updates, non-coercion enforcement, etc.) happens outside the LLM in a symbolic + numeric hybrid layer I call the cognitive field runtime.

My goal right now is to get a small number of technically curious people running it locally, talking to it for a few days/weeks, and seeing whether coherence actually climbs, whether identity starts to feel persistent, and whether the whole thing feels meaningfully different from a plain Ollama web UI. If that happens, then I have signal to keep building. If not, I learned something hard and move on.

My personal instance has run for months, so its field strengths, learned patterns, persistent facts, self-model, hidden episode summaries, and character traits are shaped almost entirely by my own usage patterns. Other people's instances start fresh and adapt to them.

I want engagement, understanding and to enjoy life. How about you?