Ternary Semantic Brain Core — Zero Hard-Coding, Language-Independent Meaning Engine

Arif Kurnaz — Tue, 07 Jul 2026 17:29:01 +0000

Ternary Semantic Brain Core — Zero Hard-Coding, Language-Independent Meaning Engine

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.

What makes it different

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.

How it works

Start with an empty brain
Train with monolingual dictionaries (word → definition)
Optionally add books/articles for richer meaning
Query: /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.

Architecture

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

Results (65 books trained)

Metric	Value
Concepts	288,407
Sentences	1,234,706
Synapses	102.7M
RAM	~1.3 GB

Cross-Language Bridge Examples

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

What this is NOT

Not an LLM — no attention, no transformer, no text generation
Not open source — binary-only experimental release
Not a pretrained model — you train from scratch with your own data
Not AGI — no claims of consciousness or general intelligence

Try it yourself

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.

DEV Community: Arif Kurnaz

Ternary Semantic Brain Core — Zero Hard-Coding, Language-Independent Meaning Engine

Ternary Semantic Brain Core — Zero Hard-Coding, Language-Independent Meaning Engine

What makes it different

How it works

Architecture

Results (65 books trained)

Cross-Language Bridge Examples

What this is NOT

Try it yourself