|
|
--- |
|
|
license: cc-by-4.0 |
|
|
tags: |
|
|
- glyphic |
|
|
- symbolic-language |
|
|
- semantic-protocol |
|
|
- agent-cognition |
|
|
- drift-resistant |
|
|
- llm-training |
|
|
- interpreter |
|
|
- protocol |
|
|
library_name: glyphic-language |
|
|
language: |
|
|
- en |
|
|
pretty_name: Glyphic Language |
|
|
--- |
|
|
|
|
|
Glyphic Language |
|
|
|
|
|
A symbolic language, semantic protocol, and training pipeline designed for drift‑resistant agent cognition. |
|
|
|
|
|
Glyphic provides a deterministic structure for representing: |
|
|
|
|
|
Identity |
|
|
|
|
|
Intent |
|
|
|
|
|
Memory |
|
|
|
|
|
Behavior |
|
|
|
|
|
Safety |
|
|
|
|
|
State |
|
|
|
|
|
Thought |
|
|
|
|
|
It is built for systems where consistency, structure, and long‑term stability matter more than free‑form natural language. |
|
|
|
|
|
Glyphic is not a “constructed language.” |
|
|
It is a protocol layer for intelligent systems. |
|
|
Why Glyphic Exists |
|
|
|
|
|
Modern LLMs are powerful but unstable: |
|
|
|
|
|
They drift over time |
|
|
|
|
|
They reinterpret instructions |
|
|
|
|
|
They lose identity |
|
|
|
|
|
They hallucinate structure |
|
|
|
|
|
They cannot maintain long‑term memory |
|
|
|
|
|
They treat meaning as prose instead of protocol |
|
|
|
|
|
Glyphic solves these problems by introducing: |
|
|
1. A deterministic symbolic language |
|
|
|
|
|
Meaning is encoded as structured glyph sequences, not ambiguous sentences. |
|
|
2. A strict grammar and syntax |
|
|
|
|
|
Defined in BNF and enforced by validators. |
|
|
3. A semantic dictionary |
|
|
|
|
|
Concepts, actors, emotions, objects, modifiers, places, and contexts. |
|
|
4. A CTX protocol layer |
|
|
|
|
|
Identity, intent, memory, behavior, safety, state, and thought are explicit fields. |
|
|
5. A training pipeline |
|
|
|
|
|
Generate text↔glyph pairs, structured meaning, and CTX envelopes for LLM training. |
|
|
6. A runtime envelope |
|
|
|
|
|
Controllers wrap LLMs in deterministic Glyphic envelopes to eliminate drift. |
|
|
Repository Overview |
|
|
Code |
|
|
|
|
|
glyphic-language/ |
|
|
├── data/ # CTX layers + protocol definitions |
|
|
├── dictionary/ # Ontology: concepts, actors, emotions, objects, etc. |
|
|
├── docs/ # Formal documentation + specifications |
|
|
├── generator/ # Dataset generator + templates + training builder |
|
|
├── interpreter/ # Encoder, decoder, validator, syntax engine |
|
|
├── runtime/ # Envelope builder for agent controllers |
|
|
├── syntax/ # Grammar rules, ordering rules, BNF |
|
|
└── training/ # Dataset formats, pipeline, evaluation |
|
|
|
|
|
Quickstart |
|
|
Install |
|
|
bash |
|
|
|
|
|
git clone https://github.com/GlyphicMind-Solutions/Glyphic-Language.git |
|
|
cd glyphic-language |
|
|
python -m venv .venv |
|
|
source .venv/bin/activate |
|
|
pip install -r requirements.txt # if present |
|
|
|
|
|
Encode / Decode Glyphic |
|
|
Encode text → structured meaning → glyph |
|
|
python |
|
|
|
|
|
from interpreter.glyph_encoder import encode_text |
|
|
|
|
|
glyph = encode_text("The agent remembers a promise.") |
|
|
print(glyph) |
|
|
|
|
|
Decode glyph → meaning → text |
|
|
python |
|
|
|
|
|
from interpreter.glyph_decoder import decode_glyph |
|
|
|
|
|
meaning = decode_glyph("<G:...>") |
|
|
print(meaning) |
|
|
|
|
|
See interpreter/README.md for full examples. |
|
|
Generate a Dataset |
|
|
Generate text↔glyph pairs |
|
|
bash |
|
|
|
|
|
python -m generator.run_generator |
|
|
|
|
|
This produces: |
|
|
|
|
|
training/text_to_glyph.jsonl |
|
|
|
|
|
training/glyph_to_text.jsonl |
|
|
|
|
|
training/structured_meaning.jsonl |
|
|
|
|
|
Dataset documentation is in: |
|
|
|
|
|
training/dataset_format.md |
|
|
|
|
|
training/dataset_generation_guide.md |
|
|
|
|
|
Training an LLM on Glyphic |
|
|
|
|
|
Glyphic includes a full training pipeline: |
|
|
|
|
|
generator/ — dataset builder |
|
|
|
|
|
training/ — formats, evaluation, fine‑tuning plan |
|
|
|
|
|
hf_finetune_glyphic.py — Hugging Face training script |
|
|
|
|
|
Training flow |
|
|
|
|
|
Generate datasets |
|
|
|
|
|
Train a base model (LLaMA/Mistral/etc.) on Glyphic sequences |
|
|
|
|
|
Export as .gguf |
|
|
|
|
|
Use Glyphic envelopes at runtime to eliminate drift |
|
|
|
|
|
A reference model will be available on Hugging Face: |
|
|
|
|
|
Model: GlyphicMind/glyphic-llm-v1 |
|
|
|
|
|
Dataset: GlyphicMind/glyphic-dataset-v1 |
|
|
|
|
|
Why Glyphic Eliminates LLM Drift |
|
|
1. Explicit structure |
|
|
|
|
|
Identity, intent, memory, behavior, safety, and state are explicit CTX fields. |
|
|
2. Protocol, not prose |
|
|
|
|
|
The model learns a symbolic protocol with strict syntax. |
|
|
3. Deterministic envelopes |
|
|
|
|
|
Controllers build and validate envelopes; the LLM fills content but cannot alter structure. |
|
|
4. Separation of concerns |
|
|
|
|
|
Long‑term meaning lives in Glyphic structures. |
|
|
The LLM becomes a stateless pattern engine. |
|
|
5. Drift‑resistant memory |
|
|
|
|
|
Memory is encoded symbolically, not as free‑form text. |
|
|
Contributing to Glyphic |
|
|
|
|
|
Glyphic is designed to be extensible, collaborative, and community‑driven. |
|
|
|
|
|
See: |
|
|
|
|
|
CONTRIBUTING.md |
|
|
|
|
|
GOVERNANCE.md |
|
|
|
|
|
You can contribute: |
|
|
|
|
|
new glyphs |
|
|
|
|
|
new dictionary entries |
|
|
|
|
|
new syntax rules |
|
|
|
|
|
new CTX fields |
|
|
|
|
|
new templates |
|
|
|
|
|
new training examples |
|
|
|
|
|
All contributions must pass: |
|
|
|
|
|
dictionary validation |
|
|
|
|
|
syntax validation |
|
|
|
|
|
CTX protocol validation |
|
|
|
|
|
Roadmap |
|
|
|
|
|
See ROADMAP.md for full details. |
|
|
v1 — Current |
|
|
|
|
|
Core dictionary |
|
|
|
|
|
Grammar + BNF |
|
|
|
|
|
Interpreter |
|
|
|
|
|
CTX protocol |
|
|
|
|
|
Dataset generator |
|
|
|
|
|
Training pipeline |
|
|
|
|
|
v2 — Recursive Glyphs |
|
|
|
|
|
Compositional glyphs |
|
|
|
|
|
Glyph inheritance |
|
|
|
|
|
Polarity + intensity |
|
|
|
|
|
v3 — Dynamic Glyph Generation |
|
|
|
|
|
On‑the‑fly glyph creation |
|
|
|
|
|
Glyph clustering |
|
|
|
|
|
Semantic compression |
|
|
|
|
|
v4 — Multi‑Agent Glyphic Communication |
|
|
|
|
|
Agent‑to‑agent glyphic messaging |
|
|
|
|
|
Shared memory substrates |
|
|
|
|
|
Distributed glyphic cognition |
|
|
|
|
|
License |
|
|
|
|
|
This project is licensed under: |
|
|
|
|
|
Creative Commons Attribution 4.0 International (CC‑BY 4.0) |
|
|
You may reuse, modify, and build upon this work with attribution. |
|
|
|
|
|
See LICENSE for full terms. |
|
|
Citation |
|
|
|
|
|
If you use Glyphic in research or development: |
|
|
Code |
|
|
|
|
|
Glyphic Language (2026). GlyphicMind Solutions. |
|
|
https://github.com/GlyphicMind-Solutions/Glyphic-Language |
|
|
|
