---
license: other
tags:
  - ufo-compression
  - model-quantization
  - tokenizer-compression
  - rust
  - cpp
  - go
  - patent-evidence
language:
  - en
pipeline_tag: translation
title: UFO Compression Python
---

# UFO Hyper-Compression & Self-Reconstruction Framework Kit (Python Edition)
<!-- Patent Pending — USPTO Provisional Application | Watermark: ip zymatica.space -->

This repository houses the core algorithmic framework for the **UFO (Ultra-Frequency-Optimized) 7-Level Compression & Self-Reconstruction Pipeline**. 

This codebase is published privately to establish legal ownership and empirical utility evidence for USPTO provisional patent filings. It includes multi-language system ports (Python, Rust, C++, Go) of the core serialization modules to prevent unlicensed translation or replication.

---

## 1. Intellectual Property & Patent Claims

This framework implements the following proprietary claims under USPTO provisional applications:

1. **Claim 1 (Multidimensional Semantic Coordinate Indexing):** Decomposing conceptual queries and semantic structures into a 6D hypercube index along orthogonal axes (Domain, Subdomain, Operation, Modality, Depth, Polarity) represented as coordinate radicals ($R_C, R_F, R_A$).
2. **Claim 2 (Embedding-Driven Weight Projection - E-PAUP):** Representing weight deltas of target layers by projecting them onto pre-existing, shared word embedding matrices of the base model, eliminating coordinate transmission overhead.
3. **Claim 3 (Tokenizer Prefix-Suffix Varint Differential Coding):** Lossless tokenizer serialization storing tokens by ID order using variable-length prefix sharing indexes and suffix bytes, achieving a 9.37x stand-alone reduction and 3200x reference-mode reduction.
4. **Claim 4 (LLM-Logits-Driven Range Coding - LLD-AC):** Bypassing static coding tables by utilizing runtime logit probability distributions of the active model as dynamic entropy priors.

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## 2. Tokenizer 7-Level Compression Paradigm

The tokenizer framework compresses original metadata files (~23 MB) down to either a stand-alone 2.4 MB capsule or a 28-byte base-oracle reference capsule using a 7-Level descent:

* **Level 1 (Raw Baseline):** Loading raw `tokenizer.json`, `merges.txt`, `vocab.json` (23 MB).
* **Level 2 (Structured Extraction):** Isolating vocab mappings and merge tuples (15 MB).
* **Level 3 (Byte/ID Delta Packing):** Varint packing of contiguous IDs; merges represented as vocabulary index pairs (7 MB).
* **Level 4 (Prefix-Suffix Differential Compression):** Prefix character length extraction + suffix arrays (4 MB).
* **Level 5 (Base Oracle Reference):** Zero-delta alignment vs `Qwen/Qwen3.5-0.8B`.
* **Level 6 (Deflate Entropy Coding):** Zlib Level 9 hyper-deflate (2.4 MB absolute, 28 bytes reference).
* **Level 7 (XOR-FEC Chirp Packetization):** Packetization into 28 × 255-byte packets (27 data + 1 FEC parity).

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## 3. Multi-Language System Implementations
To ensure broad patent coverage and prevent unauthorized ports, the core algorithms have been transpiled and verified in:
* `python/`: Production-ready Python modules.
* `rust/`: Systems-level Rust implementation (`tokenizer_coder.rs`) for native speed.
* `cpp/`: Low-level C++ port (`tokenizer_coder.cpp` / `.hpp`) for embedded systems and edge microcontrollers.
* `go/`: Go port (`tokenizer_coder.go`) for high-concurrency server wrappers.

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*Watermark: ip zymatica.space | astronautshe.com | Patent Pending — All Rights Reserved*