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

A developer built a meaning-learning engine that uses 2-bit ternary values instead of LLMs or embeddings. The system learns purely from word co-occurrence, requires no hard-coded linguistic rules, and works across languages. After training on English and Turkish, it automatically discovered 247 suffixes and 38 prefixes and formed cross-language bridges between equivalent concepts.

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.