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language: en
license: LicenseRef-Zymatica-Covenant-2.0
tags:
- webgl
- compression
- semantic-protocol
- range-coder
- dimensionality-reduction
- svd
pretty_name: Language-U WebGL Inference Engine
---
# Language-U WebGL Inference Engine
"The impossible is just code waiting to be written, physics waiting to be rewritten, math a work in progress, and truth waiting to be discovered."
This repository contains the optimized binary transport representations, the source database, and cross-language range decoders for the **Language-U Semantic Communication Protocol** (Language-U-Browser specification) run on the 49 WebGL database configurations.
For a full technical and mathematical breakdown of the protocol, check the [Language-U Whitepaper](whitepaper.md).
For system-level requirements and local installation commands to compile and execute each of the 49 frameworks on Windows, macOS, or Linux, refer to the [Framework Execution Specifications](frameworks_execution_specs.md).
## Mathematical Architecture: Rank-2 SVD Factorization
By enforcing the Cuneiform-U structural upgrades across all WebGL configurations, 5 out of the 6 coordinates (subdomain, operation, modality, depth, and polarity) are constant projections. Consequently, the coordinate matrix $M$ has a mathematical rank of exactly 2.
Using **Rank-2 SVD factorization with zero DCT coefficients**, we achieve **100% lossless, zero-drift reconstruction** after 8-bit quantization:
$$\text{round}\left(U_{\text{dequant}} \Sigma_2 V_{\text{dequant}}^T\right) = M$$
This optimizes the coordinate payload size to just **110 Bytes** total for all binary SVD matrices, representing a **70% reduction in SVD/DCT payload size** compared to standard Rank-4 DCT algorithms.
The database is 100% commercial-friendly, containing 49 open-source libraries licensed permissively (including our custom framework Zymatica-3D under the Zymatica Covenant License 2.0 (zymatica.space), replacing the copyleft Blend4Web framework).
* **Capsule Size**: `373` bytes
* **Checksum (SHA-256)**: `573acd8edada38afa62af9fd8d3621324342db37025b69d77ed45a74c1af24e4`
---
## Repository Directory Structure
```text
├── logo.jpg # Zymatica Logo image
├── LICENSE # Zymatica.space License
├── whitepaper.md # Language-U technical whitepaper
├── frameworks_execution_specs.md # System requirements and run commands for the 49 frameworks
├── run_ultimate_pipeline.py # Level 1-7 dynamic execution compression pipeline
├── receiver_reconstruction_demo.py # Offline dynamic receiver simulation
├── VerifyLanguageU.java # Cross-language Java range decoder
├── verify_language_u.rs # Cross-language Rust range decoder
├── verify_language_u.lua # Cross-language Lua range decoder
├── frameworks_db.json # Raw database of 49 WebGL configurations
├── requirements.txt # Python dependencies (numpy)
├── Language-U-Browser.LLM # Level 6 compressed capsule (373 bytes)
├── frameworks_metadata.json # SVD matrix bounds, singular values, and hashes
├── frameworks_u.bin # SVD Left-Singular quantized matrix (98 bytes)
├── frameworks_vt.bin # SVD Right-Singular quantized matrix (12 bytes)
├── frameworks_dct.bin # Empty placeholder DCT binary (0 bytes)
├── frameworks_names.bin # Tokenizer prefix-suffix vocab stream (413 bytes)
├── frameworks_coordinates.bin # Compressed Yang Range Coder coordinates (27 bytes)
└── packets/ # Level 7 transport packets
├── packet_00.bin # Packet 0 (255 bytes)
├── packet_01.bin # Packet 1 (255 bytes)
└── parity_packet.bin # XOR-FEC parity packet (255 bytes)
```
---
## Installation & Running Instructions
To test the code locally, ensure you have Python 3 installed.
### 1. Install Dependencies
```bash
pip install -r requirements.txt
```
### 2. Run the Ingestion & Compression Pipeline
Running the pipeline script regenerates the binary coordinates, executes the 7-level semantic compression pipeline, and validates XOR-FEC packet healing:
```bash
python run_ultimate_pipeline.py
```
### 3. Run the Offline Receiver Reconstruction Demo
To test dynamic packet healing, capsule reassembly, range decoding, and 6D coordinate expansion offline:
```bash
python receiver_reconstruction_demo.py
```
---
## Testing & Cross-Language Verification
You can verify the mathematical soundness of the range decoders and confirm they produce 100% identical outputs:
### Rust Decoder (Requires rustc)
```bash
rustc verify_language_u.rs -o verify_language_u
./verify_language_u
```
Output:
```text
======================================================================
ZYMATICA | Cross-Language Rust Decompressor & Range-Decoder
======================================================================
[1] Rust Vocab Decompression: SUCCESS (49 names restored).
[2] Rust Yang Range Decoder execution: SUCCESS.
[SUCCESS] Rust range-decoder verification: 100% MATCH!
```
### Java Decoder (Requires JDK)
```bash
javac VerifyLanguageU.java
java VerifyLanguageU
```
Output:
```text
======================================================================
ZYMATICA | Cross-Language Java Decompressor & Range-Decoder
======================================================================
[1] Java Vocab Decompression: SUCCESS (49 names restored).
[2] Java Yang Range Decoder execution: SUCCESS.
[SUCCESS] Java range-decoder verification: 100% MATCH!
```
---
*Developed by zymatica.space | astronautshe.com | Devs One | We Are TheAiCollective.art*
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