I built a meaning-learning engine that works without LLMs, embeddings, or
translation tables. Everything is stored as 2-bit ternary values {-1, 0, +1}.
It learns meaning purely from word co-occurrence in plain text.
No hard-coded linguistic knowledge. No stoplists, POS tags, tokenizer,
fixed vocabulary, or translation tables. All linguistic structure emerges
from training data.
Language-independent. Tested with English + Turkish. Same mechanism
works for any language with letter-based writing.
Ternary representation. {-1, 0, +1} — inhibition, unknown, excitation.
0
means "I don't know" — a first-class answer, not a failure.
Single decision rule. All thresholds come from each word's own
distribution. No hyperparameter tuning. (We call it "golden ratio freeze"
— referring to structural convergence, not φ = 1.618.)
Emergent morphology. After EN+TR dictionary training: 247 suffixes and
38 prefixes discovered automatically with zero linguistic rules.
Cross-language bridge. Without being told "water = su," the brain
forms bridges between equivalent concepts across languages. After 65 books:
average Jaccard 0.47, cosine 0.61 across 10 EN-TR word pairs.
/compare water su
, /map fire
, /senses storm
The brain builds a sparse graph of word relationships. Multi-meaning words
split into separate sense layers automatically. Meaning groups emerge from
community detection on the neighbor graph.
| Layer | What it does |
|---|---|
| Concept neurons | Each word is a neuron with sparse ternary signature |
| Sentence neurons | Sentences become neurons linking words |
| Synapse graph | PMI-weighted co-occurrence connections |
| Sense layers | Dynamic multi-meaning, born from data |
| Topic groups | Community detection on neighbor graphs |
| Metric | Value |
|---|---|
| Concepts | 288,407 |
| Sentences | 1,234,706 |
| Synapses | 102.7M |
| RAM | ~1.3 GB |
| English | Turkish | Jaccard | Cosine |
|---|---|---|---|
| water | su | 0.46 | 0.61 |
| fire | ateş | 0.35 | 0.57 |
| king | kral | 0.46 | 0.56 |
| sea | deniz | 0.49 | 0.60 |
| moon | ay | 0.43 | 0.67 |
git clone https://github.com/arifkurnaz/ternary-semantic-brain-demo
cd ternary-semantic-brain-demo
chmod +x scripts/linux/*.sh
./scripts/linux/02_train.sh --dict
Linux binary included. Windows via WSL2.
Full paper and architecture docs in the repo.