Generative Data Reconstruction via Topological Eigenvectors
A fundamentally different approach to information storage — not compression, but regeneration.
Traditional storage: Your data is compressed and saved.
NORTH: Your data's structural DNA is extracted, the original is discarded, and later perfectly regenerated from a deterministic field.
Think of it like biology: A human's DNA is ~750 MB. The full human body "data" (every cell position, protein state, neural connection) would be exabytes. Yet the DNA alone is sufficient to build a human. That's a compression ratio of ~10¹⁵:1 — not by removing information, but by storing generative instructions instead of the final form.
NORTH does this for digital data.
Imagine you want to store a forest.
(1993, Angus Dewer, In the Shadow of the Poor)
Traditional Compression:
NORTH Approach:
The retrieved forest is bit-perfect identical to the original. But you never stored the forest itself — only its generative code.
| System | "Data" Size | "DNA" Size | Compression Ratio | Method |
|---|---|---|---|---|
| Human Body | ~10²⁴ bytes (all molecular states) | ~750 MB (genome) | ~10¹⁵:1 | Genetic code → cell growth |
| NORTH (Concept) | D bytes (full data) | N bytes (Northpole) | Variable | Topological eigenvector → substrate regeneration |
| Traditional Compression | D bytes | 0.3-0.8·D bytes | 1.2-3:1 | Statistical redundancy removal |
Because it doesn't store "put atom X at position Y" — it stores "apply rule R in context C". The actual structure emerges from deterministic biochemical processes (the substrate = Ω₀). NORTH does the same for digital information.
Where:
Result: Bit-perfect reconstruction. Hash(original) = Hash(regenerated).
| Feature | ZIP/JPEG/etc | NORTH |
|---|---|---|
| Philosophy | Reduce redundancy | Extract generative structure |
| Storage | Compressed artifact | Topological eigenvector |
| Retrieval | Decompress | Regenerate from substrate |
| Substrate | None needed | Deterministic field (Ω₀) |
| Bio-analogy | Shrink the tree | Store the seed |
| Feature | Neural Compression | NORTH |
|---|---|---|
| Training | Requires datasets | No training needed |
| Determinism | Probabilistic | Fully deterministic |
| Fidelity | Approximate | Bit-perfect |
| Mechanism | Learned patterns | Topological mathematics |
The concept and mathematics are public (Prior Art). The implementation algorithms remain trade secrets:
Let's make the analogy precise:
Human Genome
DNA: 3.2 billion base pairs × 2 bits/pair ≈ 800 MB
Epigenome (methylation patterns): ~100 MB
Regulatory networks: ~50 MB
Total "genetic Northpole": ~1 GB
Human "Full State"
~37 trillion cells × 10 KB/cell (rough molecular state) = 370 petabytes
Neural connectome alone: ~100 petabytes
Total "data if stored literally": ~1 exabyte (10¹⁸ bytes)
Compression Ratio
1 exabyte / 1 gigabyte = 10⁹ (billion-to-one)
But it's not compression — it's regeneration. The 1 GB doesn't "contain" the exabyte in compressed form. It contains the rules to build it via biochemical substrate (Ω₀ = cellular machinery + physics).
README.md - This file (conceptual overview)LICENSE.md - CC BY-NC-SA 4.0 with patent provisionsCITATION.cff - Citation metadataIf you reference NORTH in academic work:
Author Identifier:
Angus Dewer https://orcid.org/0009-0008-8238-1770
Public (Free):
Requires License:
Licensing inquiries: angus@dephaze.eu
This work establishes Prior Art under:
Patent applications filed after December 2025 claiming generative reconstruction via topological eigenvectors will be challenged.
A: Implementation is proprietary. The conceptual framework is public for research/education.
A: Not currently planned. Core algorithms remain trade secrets. Mathematical framework is public.
A: Compression = remove redundancy from static data. NORTH = extract generative code, discard data, regenerate from substrate. Like DNA vs. photographing every cell.
A: Ω₀ is algorithmically generated — zero storage. Like you don't "store" the laws of physics to use them.