u/Unikum_01

Here is my current research project status, it has become quite extensive in the meantime.

https://github.com/unikum-sol/brainstem/blob/main/Project_Status_2026-09-28.md

I look forward to feedback and discussions.

Here is a small excerpt:

„BrainStem is a self-learning language-understanding system designed to acquire sentence-, word-, and relation-level structure from an unsegmented text corpus purely through statistical observation, without word lists, grammars, or filters. The system’s stated design principle is that every unit of knowledge, a sentence-level hypothesis, a word boundary, a relation between two entities, a category, or a question, begins as a **low-confidence, fully correctable hypothesis**, and only becomes a durable fact, relation, category, or question after surviving a multi-cycle, neuromodulator-gated consolidation process modeled on biological sleep-dependent memory consolidation.”

https://github.com/unikum-sol/brainstem

Hey everyone, I have been working on a project for a while now. It is a custom RNS-AI architecture written in Python that runs locally on a single CPU core using a basic SQLite database.

The main reason I built this is because standard LLMs drive me crazy with hallucinations and catastrophic forgetting. They just merge everything into a giant statistical blob of weights. If you ask a question and the model does not know the answer, it just guesses something plausible to please you. That does not work if you need the system for high risk environments like medical decision support, where total auditability and clear provenance chains are mandatory.

My System works on a completely different rule: no black box, no unearned answers, no word filters. Instead of using dense vectors, it stores context hypotheses in a shadow layer while reading. It never erases errors or contradicted data because mistakes are valuable evidence. Before a hypothesis becomes an accepted fact, it has to survive multiple slow wave sleep cycles. This is an active consolidation phase where the system uses stochastic replay to test if a hypothesis remains stable over time. If there is no verified anchor in the database for your query, the system simply reports the gap instead of guessing.

I am using terms like sleep and neuromodulation on purpose because the code actually mimics those exact functional mechanics at an algorithmic level. For instance, dynamic floating point parameters continuously tune excitation, inhibition, and sleep pressure to keep the system balanced without needing gradient descent.

In recent production tests over a 664 MB Wikipedia corpus, it processed around 155k chunks and tracked over 1.4 million hypotheses on a single CPU thread without breaking down or corrupting its state.

I am currently validating the hypothesis graduation pipeline and would love to hear your thoughts. How do you guys deal with parameter saturation or balancing strict line of sight provenance against fluid generalization in continuous learning loops?